ALTR Data Security Platform

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Easily control access and secure sensitive data with ALTR's free integration in Snowflake Partner Connect. DBAs, Data Engineers and Data Architects can drastically reduce manual tasks (or hand them off completely) and deliver more data value, more quickly.

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Get going in minutes with no SnowSQL, no contracts, no cost, no consultants and... really, no hassle, with our SaaS cloud-based platform.

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Sign up here or connect to your Enterprise Edition Snowflake account via Snowflake Partner Connect to get started.

With ALTR, you will have these capabilities at scale:

Encryption

Data Tokenization and Format Preserving Encryption

Advanced Security icon

Dynamic Data Masking and  Data Access Governance

Data Usage icon

Database Activity Monitoring

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Open-Source Data Catalog and ETL Integrations

Automated Data Insights icon

Data Discovery and Classification

No Code Example

Advanced Data Protection

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Format Preserving Encryption

Secure your data using ALTR's Format Preserving Encryption offering. Built off the industry leading FF3_1 algorithm, this method adds an additional layer of at-rest security, while maintaining data usability through determinism alongside length and format preservation.

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Tokenization

Maximize protection, and reduce compliance burden using ALTR's SaaS Tokenization Vault. Ideal for securing highly sensitive data, such as PHI or PCI, without sacrificing performance.

Identify,
Protect, and Monitor

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Database Activity Monitoring

Monitor all access to sensitive data controlled by ALTR with rich query logs. Alert security teams when data access surpasses normal thresholds.

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Data Classification

Identify what data is sensitive through built-in data classification or your own classification method. Use classification insights to automatically control data access.

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Data Usage Analytics

Easily identify how much data specific users are accessing. Easily analyze access trends over time to ensure policy is properly configured.

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Dynamic Data Masking

Protect sensitive data with column-based and row-based access policies and dynamic data masking. Scale policy creation with attribute-based and tag-based access control.

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ALTR Data Security Platform

Protect sensitive data and control access at scale.
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Database Activity Monitoring

Easily monitor data access and detect abnormal queries using our analytics and query audit logs. Enable alerts to allow security teams to quickly investigate any suspicious activity.

Dynamic Data Masking

Protect data through classification and dynamic data masking before it enters your cloud data warehouse. Seamlessly integrate masking with your data catalog or ETL/ELT pipeline.

Sensitive Data Protection

Secure highly sensitive data like PHI, PCI, and PII data from privileged access with advanced data protection and automated access policies.

Integrate with Data Catalogs

ALTR seamlessly integrates with data catalogs. Define and enforce security and policy in our platform while leveraging your existing tools.
FEATURES

One powerful platform for your

data team

infosec team

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WE ARE ALTR

Simplifying data governance and security for all

Reduce Complexity

Streamline data classification, RBAC, data activity monitoring, and at rest protection.

Increase Efficiency

Seamless interoperability with all data catalogs, ETL/ELT solutions, and BI tools.

Manage Risk

Advanced Data Protection with near real-time alerts for all SIEM solutions.

About ALTR
SECURITY YOU CAN TRUST
“Through their native cloud integration, ALTR’s visibility into Snowflake Data Cloud activity is providing a solution for customers who need to defend against security threats.”
Omer Singer
Head of Cybersecurity Strategy, Snowflake
Read case study
Jun 18
0
min
From Conundrum to Compliance: Simplifying Data Security in a Regulatory World

Data, its meticulous management, stringent security, and strict compliance have become pivotal to businesses' operational integrity and reputation across many sectors. However, the intricate maze of evolving compliance laws and regulations, as we discussed in a recent blog, poses a formidable challenge to data teams and stakeholders. This dynamic regulatory environment complicates the already intricate workflows of data engineers, who stand on the frontlines of ensuring data compliance, constantly navigating through a sea of changes to maintain adherence.

The Compliance Conundrum

The landscape of data compliance has shifted from a mere checkbox exercise to a continuous commitment to safeguarding data privacy and integrity. The advent of stringent regulations like the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States, among others, has escalated the stakes. Each regulation has its unique set of demands, and failure to comply can lead to severe repercussions, including substantial fines and a damaged reputation. A recent study from Drata found that 74% of organizations state compliance is a burden, and 35% spend 1,000 to 4,999 hours on compliance activities.

For data engineers, this presents an incredibly daunting task. They are tasked with the critical responsibility of ensuring that the data architectures they develop, the databases they oversee, and the analytics they perform are in strict alignment with a complex array of regulations that vary not only by jurisdiction but also by the nature of the data. This requires a vigilant eye on the ever-changing regulatory landscape, an in-depth understanding of each law, and a clear comprehension of its applicability to the data they manage. This constant state of monitoring and adaptation disrupts standard workflows, delays projects, and introduces a layer of uncertainty into data operations.

Navigating Through With Automation and Scalable Data Security

Amid these challenges, automation and scalable data security shine as beacons of hope, promising to alleviate the burden on data engineers and enable them to concentrate on their core tasks.

Data Classification: The Starting Point

The critical process of data classification is at the heart of any robust data security and compliance strategy. It tackles the initial hurdle of deciphering which regulations apply to specific data sets by identifying and categorizing data based on sensitivity. Automating this foundational step ensures that data is consistently managed in line with its classification, simplifying the maze of compliance with regulations like GDPR and CCPA.  

Dynamic Data Masking: Protecting Data in Real-Time

Dynamic Data Masking (DDM) emerges as a practical solution for the real-time protection of sensitive data, ensuring it remains accessible only to those with authorization. This tool is particularly pertinent to complying with regulations demanding strict data privacy and access controls, allowing data engineers to implement scalable data access policies without altering the actual data.

Database Activity Monitoring: The Watchful Eye

The continuous surveillance of database activities through Database Activity Monitoring is crucial for maintaining compliance. It enables the early detection of unauthorized access or anomalous data handling, which could indicate potential breaches or non-compliance. This tool is instrumental in keeping an audit trail, a prerequisite for many data protection regulations, ensuring any deviations from standard data access patterns are promptly addressed.

Tokenization: Minimizing Exposure Risk

Tokenization is a formidable shield for susceptible data types, such as Personal Health Information (PHI) or Payment Card Information (PCI), often under stringent regulatory scrutiny. By substituting sensitive data with non-sensitive equivalents, tokenization significantly reduces the risk of data exposure. It eases the compliance burden by narrowing the scope of data subjected to the most stringent regulations.  

Format Preserving Encryption: Balancing Security and Usability  

Format Preserving Encryption (FPE) allows organizations to secure data while preserving its usability, an essential factor for operational systems bound by data protection regulations. FPE ensures encrypted data remains functional within applications without modification, thus supporting compliance efforts by safeguarding data without hindering business processes.

Open Source Integrations: Streamlining Compliance

Integrating open-source tools for data governance facilitates a smoother compliance journey by automating and simplifying data management tasks. These integrations ensure consistent data handling practices, enhance data quality, and foster a comprehensive data governance framework capable of adapting to evolving regulations, thereby bolstering an organization's compliance posture in a scalable and efficient manner.

How Streamlined Compliance Fuels Business Growth

Navigating data compliance with automation and advanced data management brings significant benefits beyond mere regulatory adherence, enhancing operational efficiency and competitive positioning.

Accelerated Project Delivery

Automating compliance tasks liberates data engineers to concentrate on their core functions, significantly speeding up project timelines. Automation facilitates rapid adaptation to regulatory changes and maintains a constant state of compliance readiness, boosting productivity and enabling businesses to respond swiftly to market demands.

Elevated Data Quality

Implementing precise data classification and stringent access controls reduces the risk of errors and inconsistencies. This ensures a steady flow of accurate and reliable data through organizational pipelines, crucial for informed decision-making and maintaining operational integrity.

Competitive Edge

In today's data-sensitive environment, a strong reputation for data security and compliance can enhance customer trust and loyalty, offering a distinct competitive advantage. Demonstrable data protection meets regulatory requirements and fosters customer retention and brand differentiation, turning compliance into a strategic business asset.

Wrapping Up

While the ever-evolving landscape of compliance laws poses significant challenges, the path forward isn't about memorizing every regulation but about leveraging technology to create a culture of informed compliance. This allows data engineers to shift their focus from frantic firefighting to strategic data management, ultimately unlocking the true potential of the information they hold.

Jun 11
0
min
FPE vs Tokenization vs TSS

When talking to customers about data protection in Snowflake, a few things get a little mixed up with one another. Snowflake’s Tri-Secret Secure and masking are sometimes considered redundant with ALTR’s tokenization and format-preserving encryption (FPE) - or vice versa. What we’ll do in this piece is untangle the knots by clarifying what each of these is, when you would use each, and the advantages you have because you can choose which option to apply to each challenge you come across.

Snowflake’s Tri-Secret Secure is a built-in feature, and it requires that your Snowflake account is on the Business Critical Edition. Tri-Secret is a hybrid of the “bring your own key” (BYOK) and the “hold your own key” (HYOK) approaches to using customer-managed keys for the encryption of data at rest. [ProTip for the Snowflake docs: Tri-SecretSecure is essentially a brand name for the customer-managed keys approach, and if you read these docs understanding that, then these docs are a little clearer.] When you use customer-managed keys, there is often a choice between having to supply the key to the third party (Snowflake in this case) on an ongoing basis or only giving it when needed – BYOK and HYOK respectively. Snowflake effectively combines these approaches by having you provide an encrypted version of the key, which can only be decrypted when it calls back to your crucial management systems. So, you bring an encrypted version of the customer-managed key to Snowflake but hold the key that can decrypt it. Tri-Secret is used for the actual files that rest on disks in your chosen Snowflake cloud provider and is a transparent data encryption – meaning this encryption doesn’t require a user to be aware of the encryption involved. It protects the files on disk without affecting anything at run time.

Snowflake’s Dynamic Data Masking is a very simple yet powerful feature. This feature requires Enterprise Edition (or higher). When a masking policy is used to protect a column in Snowflake, at run time, a decision is made to return either the contents of a column or a masked value (e.g., a set of “****” characters). You can apply this protection to a column either directly as a column policy or via a tag placed on a column associated with a tag-based policy. When you need to ensure that certain individuals can never see the legitimate values in a column, then Dynamic Data Masking is a perfect solution. The canonical example is ensuring that the database administrators can never see the values of sensitive information when performing administrative tasks. However, there are slightly more complex instances of hiding information where masking falls short. You can easily imagine a circumstance where users may be identifiable across many tables by values that are sensitive (e.g., credit card numbers, phone numbers, or government ID numbers). You want users doing large analytics work to be able to join these objects by the identifiers, but simultaneously, you’re obligated to protect the values of those identifiers in the process. Clearly, turning them into a series of “***” won’t do that job.

This is where ALTR’s Tokenization and Format-Preserving Encryption (FPE) enter the story. We could spend hours parsing out the debate about if tokenization is a super class of FPE, vice versa, or neither. There are people with strong arguments on every side of this. We’ll focus on the simpler questions of what each feature is, and when it is best applied. First, let’s define what they are:

-       Tokenization replaces values with tokens in a deterministic way. This means that you can rely on the fact that if there is a value “12345” in a cell and it’s replaced by the token “notin” in one table, then if you encounter that value in another table, it will also be “notin” each time it started as “12345.” So now you can join the two tables by those cells and get the correct result. A key concept here is that the token (“notin” in this example) contains no data about the original values in any way. It is a simple token that you swap in and out.

-       Format-Preserving Encryption (FPE) is like tokenization since you’re also swapping values. However, the “tokens” in this case are created through an encryption process where the resulting value maintains both the information and its format. FPE might replace a phone number value of “(800) 416-4710" with “(201)867-5309.” Like the tokens, that replacement will be consistent so one can use it in joins and other cross-object operations. Unlike the tokens, these values are in the same “format” (hence the name and the phone number token looking exactly like a different phone number), which means they will be usable in applications and other upstream operations without any code changes. In other words, FPE won’t break anything; it only protects information.

ALTR has both Tokenization and Format-Preserving Encryption solutions for Snowflake, which are cloud-native and immensely scalable. In other words, they can both keep up with the insane scale demands of Snowflake workloads. The application-friendly FPE often seems like the only solution you need at first glance. However, there are reasons for choosing to use only Tokenization or perhaps both Tokenization and FPE in combination. The most common reason for going Tokenization only is due to regulatory constraints. Since the ALTR Tokenization solution can be run in a separate PCI scope, it gives folks the power to leverage Snowflake for workloads that need PCI data without having to drag Snowflake as a whole into PCI auditing scope. The most common reason we see folks run both Tokenization and FPE together is to stick to a strict least-privilege model of access. Since Tokenization removes all the information about the data it protects, some will choose to tokenize data while it flows through pipelines into and out of Snowflake and transform it to FPE while inside Snowflake to get the most out of the data in the trusted data platform.

Hopefully, it’s clear by now that the answer to the question “Which one of these should I use?” is: it depends. If you’re already on Snowflake’s Business Critical Edition, then using Tri-Secret Secure seems like a no-brainer. The extra costs involved are nominal, and the extra protection afforded is substantial. The real questions come when applying Snowflake’s Dynamic Data Masking and either or ALTR’s Tokenization and Format Preserving Encryption (FPE). Masking is a great option for many administrative use cases. If you’re not concerned about the user being able to do cross-object operations like joins and need to hide the data from them, then masking is easily the best choice. The moment there is the need for joins or similar operations, then ALTR’s Tokenization and FPE are the right places to turn. Picking between them is mostly a matter of technical questions. If you have concerns about application compatibility with the protected data, then FPE is your choice. If you want to keep the protected data away from the data platform, then Tokenization is the best option since FPE runs natively in Snowflake. And there are clearly times when you may have workloads complex enough that all of these can be used in combination for the best results. You’ve got all the options you could ever need for Snowflake data protection. So now it’s time to get to work making your data safer than ever.

Jun 5
0
min
ALTR Brief: Snowflake Cybersecurity Investigation

Background

On May 31st, 2024, data breaches at Santander Bank and Ticketmaster were confirmed. Attackers claim to have data from around 400 other Snowflake customers, though Snowflake or the other Snowflake customers haven't verified this. As a Data Security Platform provider for Snowflake, ALTR immediately took notice. On June 1st, 2024, Snowflake released the following statement as part of a Snowflake Community article:

“Snowflake and third-party cybersecurity experts CrowdStrike and Mandiant are providing a joint statement related to our ongoing investigation involving a targeted threat campaign against some Snowflake customer accounts.

Our key preliminary findings identified to date:

• No evidence suggests this activity was caused by a vulnerability, misconfiguration, or breach of Snowflake's platform.

• No evidence suggests that compromised credentials of current or former Snowflake personnel caused this activity.

• This appears to be a targeted campaign against users with single-factor authentication.

• Threat actors leveraged credentials obtained through infostealing malware.

Evidence shows a threat actor accessed demo accounts of a former Snowflake employee, which did not contain sensitive data and were not connected to Snowflake's production or corporate systems. The access was possible because the demo account lacked Okta or MFA protection.

No one should be surprised that bad actors constantly use malware to exploit devices and harvest data, including credentials. This is the constant threat to IT infrastructure today. Since many users reuse the same password and username/email, exposed credentials can be used to compromise multiple accounts across various sites.

Security Recommendations

OWASP recommends a list of items that can be used to prevent such attacks, with the number one recommendation being the use of multi-factor authentication for your user accounts. However, they also emphasize the need for layered defense techniques to protect against credential-related breaches.

Additional Recommendations

Active Security can refer to many types of security practices versus passive security, which is generally limited to post-breach log siftingand/or recovery of lost or damaged information. Active security in the context of Snowflake means three things: near-real-time Database Activity Monitoring (DAM), real-time data access rate limiting (Thresholds) and external tokenization or encryption.

Database Activity Monitoring is a suite of tools that can support the ability to identify and report fraudulent, illegal or other undesirable behaviour with minimal impact on user operations and productivity.

Data Access Rate Limiting or Thresholds is an exclusive feature of ALTR's Data Security Platform, which allows administrators to define exactly how much data a particular user can select from a database after they have been given valid access to that data.

Snowflake defines external tokenization as enabling accounts to tokenize data before loading it into Snowflake and detokenize the data at query runtime. Tokenization is the process of removing sensitive data by replacing it with an undecipherable token.

ALTR's purpose-built Snowflake Data Security Platform contains all three critical Active Security components. By using the ALTR Platform with your Snowflake Database, users can automate existing Snowflake security controls and extend their security posture to prevent unauthorized access to data. Defense inlayers is the approach ALTR customers take to ensure data safety in Snowflake. By using the ALTR Platform with your Snowflake Database, users can automate existing Snowflake security controls and extend their security posture with Active Security.

(Example of Snowflake data protected with ALTR’s FPE Native Snowflake Application)

Database Activity Monitoring: First Layer of Active Security for Snowflake

Enabling ALTR's DAM solution for your Snowflake Database is the first step in Active Security. ALTR can integrate with Snowflake queries to accelerate data access logs to Security Incident & Event Management (SIEM) tools used by Security Operation Centers (SOC).

Unlike native Snowflake access logs, which can be delayed, ALTR's logs are delivered near-real-time, usually within seconds. This ensures your security teams are promptly updated on potential threats, allowing businesses to stop bad actors quickly.

Format Preserving Encryption: Second Layer of Active Security for Snowflake

• Format-preserving encryption encrypts the plaintext of a field but preserves its natural format.

• Chosen FPE implementation is NIST FF3-1

• Uses a COTS FF3-1 implementation

• Provides a thin layer of key management and key partitioning.

• Keys can be partitioned by database or tag, allowing data to stay functional (JOINs, UNIONs, etc.) in Snowflake & Snowpark workloads.

(Example architecture of ALTR Format Preserving Native App)

How Does Format Preserving Encryption Help with Credential Access Threats?

ALTR's format-preserving encryption (FPE) enhances Snowflake data security by eliminating it as a single point of failure.

With ALTR's FPE Native App, data is encrypted either in the pipeline or upon arrival in Snowflake. The necessary Wrapper Key (KMS) and Key-Encryption-Key (KEK) are stored externally in ALTR's SaaS platform or a Customer Controlled Key.

Without access to the KMS or KEK, stolen data during a breach is unusable, safeguarding sensitive information in Snowflake.

Data Access Rate Limiting or Thresholds: Third Layer of Active Security for Snowflake

ALTR's exclusive, patent-issued feature, Thresholds, limits how much data an authorized user can access. It monitors data access, and Snowflake enables ALTR to block queries if access exceeds policy limits. Policies can restrict data access by the amount per day/hour or the time of day plaintext information is accessible.

(Example of using a Threshold to limit a user's ability to decrypt only two rows of data)
Jun 3
0
min
ALTR Brings Game-Changing Format-Preserving Encryption to Snowflake Marketplace

We're thrilled to announce that ALTR's Snowflake native app, Format-Preserving Encryption (FPE), is now available on the Snowflake Marketplace. This marks a significant step forward in our mission to make data security seamless, efficient, and scalable for our customers. Let's dive into what this means for you.

What is Format-Preserving Encryption (FPE)?

Imagine encrypting your sensitive data without altering its original structure or format. That's precisely what FPE does. It transforms plaintext data into ciphertext while keeping the same format. For example, a phone number like "(800) 416-4710" might be encrypted as"(201) 867-5309." This means your applications and systems can continue operating smoothly without needing changes to handle encrypted data.

Why is This a Big Deal?

Traditionally, encrypting data involved a lot of headaches. On-premises systems were expensive, costing millions of dollars per license, and they introduced significant lag because of the back-and-forth calls between Snowflake and the on-premises servers. This not only slowed down your queries but also burned a hole in your pocket with monthly costs.

With ALTR's Snowflake Native FPE, all the encryption and decryption happen locally within Snowflake. No more external calls, no more lag—just fast, secure data processing. Plus, your data stays protected at rest within the Snowflake Data Cloud, ensuring it's always secure.

How Does Snowpark Make This Possible?

Snowpark, Snowflake's developer framework, provides the perfect environment for our FPE solution. It supports fully functional applications, enabling us to deliver powerful encryption directly in Snowflake. This means you get top-notch data protection without compromising performance or ease of use.

Why Should You Care About ALTR's FPE on Snowflake?

Here's why this is excellent news for you:

Simplified Data Protection: ALTR's FPE integrates seamlessly with our existing data access control and security solutions. This means you can easily implement and manage comprehensive data security through our SaaS platform, no-code interface, and automated policy enforcement.

Cost Savings and Efficiency: You save millions in licensing fees and monthly operational costs by eliminating the need for on-premises appliances. Plus, faster query response times make your data operations more efficient.

Future-Proof Security: FPE ensures that your sensitive data is always protected, even as you scale and evolve your data ecosystem. It's particularly beneficial for industries like financial services and healthcare, where maintaining data interoperability with legacy systems is crucial.

What Do Our Customers Think?

"ALTR's FPE offering running natively in our Snowflake environment proved to be far more effective, scalable, and affordable than the legacy solutions we considered. Further, with ALTR's cloud-native, SaaS architecture, we could extend FPE upstream into our data pipeline, expanding our compliance footprint to include a staging area prior to workloads landing in Snowflake." 

Craig Hipwell, Customer Platforms Delivery Manager,Shell Energy Customer Platforms Delivery Manager, 

Get Started Today

With ALTR's FPE now available on the Snowflake Marketplace, you have all the tools you need to protect your data efficiently, effectively and at scale. It's time to take your data security to the next level. 

Explore our FPE solution on the Snowflake Marketplace and see how easy it can be to keep your data safe while maintaining top performance.

 

May 22
0
min
ALTR Welcomes New VP of Sales - An Interview with Ed Hand

Q&A with Ed Hand

1. Please share a bit about your background

I’ve spent the last two decades in enterprise software sales, where I’ve had the privilege of building and leading high-performance sales and marketing teams. My career has taken me from established, large-scale organizations to dynamic, ground-zero startups. Throughout this journey, I’ve successfully brought together all facets of Go-To-Market strategies under a cohesive team structure. My expertise lies in navigating complex ecosystems such as ServiceNow and Snowflake, where I’ve developed comprehensive market strategies that drive growth and success. I’ve consistently focused on aligning sales initiatives with broader business goals, ensuring sustainable revenue streams and long-term customer relationships.

2. What motivated you to join ALTR?

Several factors influenced my decision to join ALTR. First and foremost, I was thoroughly impressed by the ALTR team. Their deep understanding of current data security challenges and their forward-thinking approach to simplifying and scaling data security stood out to me. Additionally, the opportunity to be at the forefront of the cloud data revolution is incredibly exciting. As businesses increasingly migrate their critical data to the cloud, they adopt advanced technologies like machine learning and artificial intelligence to gain competitive advantages. ALTR's dedication to helping clients balance this technological innovation with robust cloud data security makes it an inspiring endeavour to be part of.

3. What is your vision for ALTR, and how do you see the company evolving under your leadership?

My vision for ALTR is to establish us as the de facto standard for cloud data security. This involves offering a robust, rock-solid platform and leading the industry with unmatched security expertise. Under my leadership, I aim to drive the company past critical growth milestones typical for a thriving SaaS enterprise. This includes expanding our market reach, continuously innovating our product offerings, and maintaining a relentless focus on customer satisfaction. I foresee ALTR evolving into a cornerstone of data security, trusted by organizations worldwide to protect their most valuable asset: their data.

4. How have you seen the data security and governance landscape change throughout your career, and where do you think it is headed in the next five years?

Over the years, data security and governance have undergone significant transformations. The landscape has evolved from simple perimeter defenses to sophisticated, multi-layered security strategies. Despite these advancements, cybercriminals continue to outpace many enterprises due to the high stakes involved. The fundamental principles of data security – knowing where your data is, who has access to it, and ensuring its protection – remain unchanged. However, in the next five years, the challenge will lie in meeting these requirements at the scale and speed of the cloud.

 

5. From your perspective, what makes ALTR the best solution for organizations looking to enhance their data security and governance practices?

Building a successful software company hinges on four key pillars:

Product: It all starts with having a viable and innovative product that addresses real market needs. ALTR excels here with its scalable data security solutions tailored for the cloud era.

Market: Identifying and targeting an addressable market is crucial. The demand for robust data security and governance solutions grows exponentially as more businesses move to the cloud.

Defense: Defending your market position against competitors is essential. ALTR’s advanced technology, coupled with its deep industry expertise, provides a formidable defense.

Team: The most critical element is having a team that can build and execute together effectively. At ALTR, we have a dedicated, talented team committed to our mission of simplifying data security and data access governance.

These pillars make ALTR an exceptional solution for organizations seeking to enhance their data security and governance and make it an attractive place for top talent in the industry. By joining ALTR, professionals can work on the frontlines of data security innovation, contributing to solutions that make a real difference in today’s digital landscape.

Connect with Ed on LinkedIn

May 15
0
min
The DIY Trap: Why Engineers Should Ditch Manual Masking Policies in Snowflake

For data engineers, there's a comforting hum in the familiar, a primal urge to build things ourselves."DIY is better," whispers the voice in their heads. But when it comes to data masking in Snowflake, is building policies from scratch the best use of our time? 

Sure, the initial build of a masking policy might be a quick win. You get that rush of creation, the satisfaction of crafting something bespoke. But here's the harsh reality: that initial high fades fast. Masking policies are rarely static. Data evolves, regulations shift, and suddenly, your DIY masterpiece needs an overhaul.

This is where the actual cost of the"DIY is better" mentality becomes apparent. Let's delve into the hidden complexities that lurk beneath the surface of Snowflake's manual masking policies.

The Version Control Vortex

Ah, version control. The unsung hero of software development. But when it comes to DIY masking policies, it can be atangled mess. Every change, every tweak you make, needs to be meticulously documented and tracked. One wrong move, and you could be staring down the barrel of a data breach caused by an outdated policy.

Imagine the chaos if multiple engineers are working on the same masking logic. How do you ensure everyone is on the same page? How do you revert to a previous version if something goes wrong? While Snowflake recently announced a Private Preview for version control via Git, with a purpose-built UI like ALTR, version control is baked in and highly user-friendly. There is no need for complex terminal commands –just intuitive clicks and menus. Changes are tracked, history is preserved, and rollbacks are a breeze.

The Snowflake Object Management Maze

Snowflake offers a seemingly endless buffet of objects – a staggering 74 and counting, with new additions continually emerging. However, managing these objects poses a central challenge within the Snowflake ecosystem. 

For instance, while masking policies reside within schemas, their impact extends far beyond. A single masking policy can be applied to tables and columns across numerous schemas within your Snowflake account. 

This creates a masking policy headache. Choosing the correct schema for each policy is crucial, as poor placement leads to confusion and complex updates. Furthermore, meticulous documentation is essential to track policy location and impact. Without it, any changes or troubleshooting become a nightmare due to the potential for widespread, unforeseen consequences across your Snowflake environment.

With ALTR, you do not have to consider object management when masking policies. With our unified interface, you can easily create, edit, and deploy policies automatically in seconds, eliminating the need to navigate the intricate web of Snowflake objects and their relationships.

The Update and Maintenance Monster

Data masking policies are living documents. As your data landscape changes, so too should your masking logic. New regulations might demand a shift in how you mask specific fields. A data breach requires you to tighten masking rules.

With DIY policies, every update becomes a time-consuming ordeal. You must identify the relevant policy, modify the logic, test it thoroughly, and then deploy the changes across all affected Snowflake objects. Multiply that process by the number of policies you have, and you've just booked a one-way ticket to Update City – population: you, stressed and overworked.

ALTR simplifies this process. Its intuitive UI allows for quick and easy changes to policies. Updates can be deployed across all relevant objects with a single click, eliminating the need for manual deployment across potentially hundreds of locations.

The Validation Vortex

Let's not forget the critical step of validation. Every change you make to a masking policy must be rigorously tested to ensure it functions as intended. This involves creating test data, applying the new masking logic, and verifying that the sensitive data is adequately protected.

Imagine manually validating dozens of masking policies across hundreds or thousands of tables and columns. It's a daunting task, and relying solely on automated pipelines for testing adds another layer of complexity that needs ongoing maintenance. It's enough to make any data engineer break out in a cold sweat. 

Beyond Time Saving: The BiggerPicture

The benefits of ditching DIY masking policies extend far beyond just saving time. It's about empowerment. With ALTR's easy-to-use UI, even non-technical users can create and edit masking policies. This frees up valuable engineering time, allowing you to focus on more strategic initiatives. It also fosters a culture of data ownership and responsibility, where everyone involved understands the importance of data security.

Let's face it: the "DIY is better" mentality can be a trap in data masking. It might seem like a quick win initially, but the long-term costs – time, complexity, and risk – are too high. Embrace the power of purpose-built tools like ALTR. Free your engineering time, empower your team, and ensure your data is masked effectively and efficiently.

Ready to ditch the DIY trap? Schedule an ALTR demo.

May 7
0
min
Snowflake Arctic & The Future of AI Governance

Snowflake Arctic and the Future of AI Governance If you’re reading this, then it’s certain you saw the news about Snowflake’s Arctic model launch. Machine learning and AI is the next natural step for the Snowflake Data Cloud. Not only because it’s a hot trend, but because the Snowflake story naturally leads you to AI. What makes machine learning better? Lots of data. Where are you putting more and more of your data? Snowflake. Of course, there’s no such thing as a free lunch. While your data scientists, developers, and all the other Snowflake enthusiasts in your orbit are rushing to see how they can start leveraging Arctic (and there are already ways popping out of the Snowflake teams as well), maybe you’re here because you have accountability for your organization’s data. You may have one very important question: how is Arctic going to affect my governance and security stance? We’re here to answer that question, and the answer is mostly good – if you’re going to do the right things right now.

The TL;DR on this is simple. Arctic is like every other thing that runs in the Snowflake Data Cloud. Nothing in Snowflake escapes the watchful eye of Snowflake governance policies. Nothing in Snowflake can skip past the network controls, security checks, encryption, or RBAC (Role-Based Access Control). The simplest way to understand this is that to use all this power in the Arctic LLM you have a list of simple, built-in Snowflake functions. You only have permission to use the AI stuff if you have permission to use those functions. And you only have permission to feed data that you are already allowed to access into those functions. Simple, right? End of the story, right? If that were the end, that would also be the end of this post. Honestly, I probably wouldn’t have bothered to write it if that were the case.

While it’s true that AI access is limited to the Cortex functions and that people will only be able to bring the data they already have access to into those functions, when you combine AI and the huge wells of data that Snowflake tends to have things may get weird. It’s not unusual for people (or services) to be over-provisioned. Just yesterday we were on the line with a prospect who was shocked to see ALTR’s real-time auditing picking up dozens of jobs running under the Snowflake SYSADMIN role. These queries running with too much privilege happened because lots of folks were granted this role through nesting to make it easier for them to get some data that had been put in a database that it probably shouldn’t have been in, and it was easier to grant the role than move the data. (This sort of security gap is exactly why this company is looking at ALTR in the first place!) With that SYSADMIN role, those users could have accessed tons of stuff they weren't supposed to. They didn’t (we know that because ALTR’s auditing would have caught them), but since they had the access, they could have. Humans tend to only query data they know they have access to. But what happens when AI takes the wheel?

Right now, the impact that AI’s power can have in Snowflake is limited. But just like having a model like Snowflake’s Arctic was the next natural step in the Snowflake story, there are more natural steps we can imagine. People are going to throw all the data they have at this thing to attempt to get amazing results. What happens when they have access to data they shouldn’t? What happens when they should have access to a table, but maybe there’s sensitive information in columns and there needs to be advanced data protection in place to make that data usable in the context of Cortex, Arctic, and AI in general? The machines won’t use the same approaches humans will (and vice versa). That’s why humans and AIs make such an effective team when things go right. But that also means these LLMs won’t limit themselves to only what they know. They will crawl through every scrap of data they have access to trying to find the right answer to get that good feedback we’ve programed them to seek. What happens when that machine is mistakenly given SYSADMIN role like the humans were? And, of course, people are going to build fully automated systems where the AI-powered machines will run all the time pushing these boundaries. Humans sleep, take time off, and eat a meal every now and then. What happens when your governance and security must be on watch 24/7 because they’re contending with machines that never step away?

The good news is that we’re only standing on the tip of this iceberg (pun intended). Most of this stuff is still a little while away. But as with everything else related to AI, it’s going to move fast. So now more than ever it's crucial that security and governance be integrated into the data and development pipelines and CI/CD approaches as well as automated as much as possible. Snowflake has all the controls you need to prevent the bad stuff from happening, but you need to use them effectively and automatically. The sensitive information in your data needs special attention more than ever in an AI-powered world. In that conversation yesterday, the customer asked about the new Arctic stuff and how ALTR could address that even though it just dropped this month. The answer is simple: ALTR has been in the proactive security business since the start. Since Snowflake did the right thing by building security directly into the Arctic and AI design, it’s just another thing ALTR can help you lock down as you roll it out. It all fits together perfectly. The next natural step in that company’s story – and maybe in yours – is to decide to let us help them out. We’re ready for AI when you are.

May 1
0
min
Agile Data Governance: Are You Drowning in Rigidity or Thriving in the Data Stream?

The data deluge is absolute. Organizations are swimming in an ever-growing sea of information, struggling to keep their heads above water. With its rigid processes and bureaucratic burdens, traditional data governance often feels like a leaky life raft – inadequate for navigating the dynamic currents of the modern data landscape.  

Enter agile data governance, the data governance equivalent of a high-performance catamaran, swift and adaptable, ready to tackle any challenge the data ocean throws its way.

What is Agile Data Governance?

Traditional data governance often operates siloed, with lengthy planning cycles and a one-size-fits-all approach. Agile data governance throws this rigidity overboard. It's a modern, flexible methodology that views data governance as a collaborative, iterative process.

Here's the critical distinction: While traditional data governance focuses on control, agile data governance emphasizes empowerment. It fosters a data-savvy workforce, breaks down silos, and prioritizes continuous improvement to ensure data governance practices remain relevant and impactful.

The Seven Pillars of Agile Data Governance

Collaboration

Gone are the days of data governance operating in isolation. Agile fosters a spirit of teamwork, breaking down silos and bringing together data owners, analysts, business users, and IT professionals. Everyone plays a role in shaping data governance practices, ensuring they are relevant and meet real-world needs.

Iterative Approach

Forget lengthy upfront planning that quickly becomes outdated in the face of evolving data needs. Agile embraces a "test and learn" mentality, favoring iterative cycles. Processes are continuously refined based on ongoing feedback, data insights, and changing business priorities.

Flexibility

The data landscape is a living, breathing entity, constantly shifting and evolving. Agile data governance recognizes this reality. It's designed to bend and adapt, adjusting sails (figuratively) to navigate new regulations, integrate novel data sources, or align with evolving business strategies.

Empowerment

Agile data governance is not about control; it's about empowerment. It fosters a data-savvy workforce by prioritizing training programs that equip employees across the organization with the skills to understand, use, and govern data responsibly. Business users become active participants, not passive consumers, of data insights.

Continuous Improvement

Agile data governance thrives on a culture of constant improvement. Regular assessments evaluate the effectiveness of data governance practices, identifying areas for refinement and ensuring that the program remains relevant and impactful.

Automation

Repetitive, mundane tasks are automated wherever possible. This frees up valuable human resources for higher-value activities like data quality analysis, user training, and strategic planning. Data classification, access control management, and dynamic data masking are prime candidates for automation.

Metrics and Measurement

Agile thrives on data-driven decision-making. Metrics and measurement are woven into the fabric of the program. Key performance indicators (KPIs) track the effectiveness of data governance initiatives, providing valuable insights to guide continuous improvement efforts. These metrics can encompass data quality measures, access control compliance rates, user satisfaction levels with data discoverability, and the impact of data insights on business outcomes.

Why Agile Data Governance is Critical in 2024

The data landscape in 2024 is a rapidly evolving ecosystem. Here's why agile data governance is no longer optional but a strategic imperative:

The Ever-Shifting Regulatory Landscape: Regulatory environments are becoming more dynamic than ever. Agile data governance allows organizations to adapt their practices swiftly to ensure continuous compliance with evolving regulations like data privacy laws (GDPR, CCPA) and industry-specific regulations.

Unlocking the Potential of AI: Artificial intelligence (AI) is transforming decision-making across industries. Agile data governance ensures high-quality data feeds reliable AI models. The focus on clear data lineage and ownership within agile data governance aligns perfectly with the growing need for explainable AI.

Democratizing Data for a Data-Driven Culture: Agile data governance empowers business users to access, understand, and utilize data for informed decision-making. This fosters a data-driven culture where valuable insights are readily available to those who need them most, driving innovation and improving business outcomes.

Optimizing for Efficiency and Agility: The iterative approach and automation focus of agile data governance streamline processes and free up valuable resources for higher-value activities. This allows organizations to navigate the complexities of the data landscape with efficiency.  

Is Your Data Governance Agile? Ask Yourself These 10 Questions

Are your current data governance practices keeping pace with the ever-changing data landscape? Here are ten questions to assess your organization's agility:

  1. Do different departments (IT, business users, data owners) collaborate to define and implement data governance practices?
  1. Can your data governance processes adapt to accommodate new data sources, changing regulations, and evolving business needs?
  1. Are business users encouraged to access and utilize data for decision-making?
  1. Do you regularly evaluate the effectiveness of your data governance program and make adjustments as needed?
  1. Are repetitive tasks like data lineage tracking and access control automated?
  1. Do you track key metrics to measure the success of your data governance program?
  1. Do you utilize an iterative approach with short planning, implementation, and improvement cycles?
  1. Does your organization prioritize training programs to equip employees with data analysis and interpretation skills?
  1. Are data governance policies and procedures clear, concise, and accessible to all relevant stakeholders?
  1. Do business users feel confident finding and understanding the data they need to make informed decisions?

By honestly answering these questions, you can gain valuable insights into the agility of your data governance program. If your answers reveal a rigid, one-size-fits-all approach, it might be time to embrace the transformative power of agile data governance.  

Wrapping Up

Agile data governance is not just a trendy buzzword; it's a critical approach for organizations in 2024 and beyond. By embracing its principles and building a flexible framework, organizations can transform their data from a burden into a powerful asset, propelling them toward a successful data-driven future.

Apr 25
0
min
DSPM v DSP v Discovery - Oh My

Our customers are confused. Given the state of the world, it’s safe to say everyone is a little confused now. The confusion we’re concerned with today is about the markets ALTR plays in and how the analysts of the world – particularly Gartner – are breaking those down and making recommendations. What we’ll aim to do here is analyze the analysis. We’ll lay out the questions customers are asking about the markets and solutions for Data Security Posture Management (DSPM) and Data Security Platform (DSP), see what Gartner is saying about those today, offer some reasons why we think they are right, and finally show why the confusion is real.  

Maybe that seems like a contradictory stance to take, but let’s not forget what F. Scott Fitzgerald told us: “The test of a first-rate intelligence is the ability to hold two opposing ideas in mind at the same time and still retain the ability to function.” By the end of this post, it should be clear that Gartner and others have only correctly identified a confusing time in data governance and security; they have not made things any more confusing.  

Let’s start out where customers have told us they get confused. We’ll go right to the source and quote from Gartner’s own public statements on DSPM and DSP. First, let’s look at how they define Data Security Posture Management:  

Data security posture management (DSPM) provides visibility as to where sensitive data is, who has access to that data, how it has been used, and what the security posture of the data stored, or application is.
(Source: https://www.gartner.com/reviews/market/data-security-posture-management as of March 26th, 2024)

We could pick that apart right away, but instead let’s immediately compare it with their definition of a Data Security Platform:

Data security platforms (DSPs) combine data discovery, policy definition and policy enforcement across data silos. Policy enforcement capabilities include format-preserving encryption, tokenization and dynamic data masking.
(Source: https://www.gartner.com/reviews/market/data-security-platforms as of March 26th, 2024)

At first glance, these seem incredibly similar – and they are. However, there are important differences in the definitions’ text, in their implied targets, and in the implications of these factors. The easiest place to see a distinction is in the second part of the DSP definition: “policy definition and policy enforcement." The Data Security Platform does not only look at the “Posture” of that system. It is going to deliver a security solution for the data systems where it’s applied.  

When talking to customers about this, they will often point out two details. First, they will say that if the DSP can’t do the discovery of at least the policy of the data systems then it isn’t much good that they give you ways to manage the protection. The subtlety here is that controlling the data policy implies that the solution would discover the current policy in order to control it going forward. (While it’s possible that some solution may give you policy control without policy discovery, ALTR gives you all those capabilities, so we don't have to worry about that.) The second thing they point out is that many of the vendors who are in the DSPM category also supply “policy definition and policy enforcement” in some way. That brings us to discussing the targets of these systems.  

Something you will note as a common thread for the DSPM systems is how incredibly broad their support is for target platforms. They tend to support everything from on-prem storage systems all the way through cloud platforms doing AI and analytics like Snowflake. The trick they use to do this is that they are not concerned with the actual enforcement at that broad range, and that’s appropriate. Many of the systems they target, especially those on-prem, will have complicated systems that do policy definition and enforcement. Whether that’s something like Active Directory for unstructured data stored on disk or major platforms like SAP’s built-in security management capabilities, they are not looking for outside systems to get involved. However, the value of seeing the permissions and access people use at that broad scope can be very important. Seeing the posture of these systems is the point of the DSPM.  

Of course, a subset of the systems will allow the DSPM to make changes that can be effective easily without requiring them to get too deep. If it’s about a simple API call or changing a single group membership, then the DSPM can likely do it. However, in systems where there are especially complex policies those simple, single API calls become about the “policy definition and policy enforcement" in the Data Security Platform definition. The DSP will get deep within the systems they target. Often, part of the core value of a DSP is that it will simplify what are extremely complicated policy engines and give ways to plug these policy definition steps into the larger scope of systems building or the SDLC. That focus and depth on the actual controls in targeted systems is the main difference between DSPM and DSP. The Data Security Platform narrows the scope, but it deepens the capabilities to control policies and to deliver security and governance results.  

The other important aspect of the distinction between these solutions is the Data Security Platform capabilities for Data Protection. That’s the “format-preserving encryption, tokenization and dynamic data masking” part of the DSP definition. Many data systems will have built-in solutions for data masking. Almost none will have built-in tokenization or format-preserving encryption (FPE). If these capabilities are crucial to delivering the data products and solutions an organization needs, then DSP is where they will look for solutions. This not only impacts data use in production settings, but often is associated with development and testing use cases where use of sensitive information is forbidden but use of realistic data is required.  

Let’s recognize the elephant in the analysis: DSPM and DSP are going to have overlap. If you’ve been around long enough or have read deeply enough, that should be as shocking as the fact that (if you’re in an English-speaking part of the world) the name of this day ends in “y.” Could the DSP forgo all the core capabilities of DSPM and just deliver the deeper policy and data protection features? If the DSM vendors could be sure that every customer will have DSPM to integrate with, sure. That isn’t always the case. Even if it were, it’s not guaranteed that the politics and process at an organization would make such integration possible even if it is technically possible. Could DSPM simply expand to cover all the depth of DSP including the Data Protection features? The crucial word in there is “simply.” If it were simple they would have done it already.  

It’s sure that you will see consolidation of the market over time with players merging, expanding, and being bought to make suites. Right now, organizations have real-world challenges, and they need solutions despite the overlaps. So DSPM and DSP will stay independent until market forces make it necessary for them to change.  

The overlaps, the similar goals, and the limits of language in describing Data Security Posture Management and Data Security Platforms are the source of the confusion. Hopefully, it’s now clear that DSP is the deeper solution that gives you everything you need to solve problems all the way down to Data Protection. DSPM will continue to add more platforms to grow horizontally. DSP will continue to dive deeply into the platforms they support today and cautiously add new platforms to dive more deeply into as the market needs them to. If you started this a little mad at the Gartners of the world, maybe you now see how they are right to give you two different markets with so much in common. Like with many things in life, if you are confused, it only means you are sane and paying attention. You keep paying attention, and we’ll keep helping you stay sane.  

Apr 23
0
min
The 2024 Guide to U.S. Data Privacy Protection Laws

Data privacy laws are not just a legal hurdle – they're the key to building trust with your customers and avoiding a PR nightmare. The US, however, doesn't have one single, unified rulebook. It's more like a labyrinth – complex and ever-changing.

Don't worry; we've got your back. This guide will be your compass, helping you navigate the key federal regulations and state-level laws that are critical for compliance in 2024.

The Compliance Challenge: Why It Matters

Data breaches are costly and damaging. But even worse is losing the trust of your customers. Strong data privacy practices demonstrate your commitment to safeguarding their information, a surefire way to build loyalty in a world where privacy concerns are at an all-time high.

Think of it this way:complying with data privacy laws isn't just about checking boxes. It's about putting your customers first and building a solid foundation for your business in the digital age.

US Data Privacy Laws: A Multi-Layered Maze

The US regulatory landscape is an intricate web of federal statutes and state-specific legislation. Here's a breakdown of some of the key players:

Federal Protections

These laws set the baseline for data privacy across the country. 

Privacy Act of 1974 restricts how federal agencies can collect, use, and disclose personal information. It grants individuals the right to access and amend their records held by federal agencies.

Health Insurance Portability and Accountability Act (HIPAA) (1996) sets national standards for protecting individuals' medical records and other health information. It applies to healthcare providers, health plans, and healthcare clearinghouses.

Gramm-Leach-Bliley Act (GLBA) (1999): Also known as the Financial Services Modernization Act, GLBA safeguards the privacy of your financial information. Financial institutions must disclose their information-sharing practices and implement safeguards for sensitive data.

Children's Online Privacy Protection Act (COPPA) (2000) protects the privacy of children under 13 by regulating the online collection of personal information from them. Websites and online services must obtain verifiable parental consent before collecting, using, or disclosing personal information from a child under 13.

Driver's Privacy Protection Act (DPPA) (1994) restricts the disclosure and use of personal information obtained from state motor vehicle records. It limits the use of this information for specific purposes, such as law enforcement activities or vehicle safety recalls.

Video Privacy Protection Act (VPPA) (1988) prohibits the disclosure of individuals' video rental or sale records without their consent. This law aims to safeguard people's viewing habits and protect their privacy.

The Cable Communications Policy Act of 1984 includes provisions for protecting cable television subscribers' privacy. It restricts the disclosure of personally identifiable information without authorization.

Fair Credit Reporting Act (FCRA) (1970) regulates consumer credit information collection, dissemination, and use. It ensures fairness, accuracy, and privacy in credit reporting by giving consumers the right to access and dispute their credit reports.

Telephone Consumer Protection Act (TCPA) (1991)combats unwanted calls by imposing restrictions on unsolicited telemarketing calls, automated dialing systems, and text messages sent to mobile phones without consent.

Controlling the Assault of Non-Solicited Pornography and Marketing Act of 2023 (CAN-SPAM Act) establishes rules for commercial email, requiring senders to provide opt-out mechanisms and identify their messages as advertisements.

Family Educational Rights and Privacy Act (FERPA) (1974) protects the privacy of students' educational records. It grants students and their parents the right to inspect and amend these records while restricting their disclosure without consent.

State-Level Action

Many states are taking matters into their own hands with comprehensive data privacy laws. California, Virginia, and Colorado are leading the charge, with more states following suit. These laws often grant consumers rights to access, delete, and opt out of the sale of their personal information. Here are some of the critical state laws to consider:  

California Consumer Privacy Act (CCPA) (2018) was a landmark piece of legislation establishing a new baseline for consumer data privacy rights in the US. It grants California residents the right to:

  • Know what personal information is being collected about them.
  • Know whether their personal information is sold or disclosed and to whom.
  • Say no to the sale of their personal information.
  • Access their data.
  • Request a business to delete any personal information about them.
  • Not be discriminated against for exercising their privacy rights.

Colorado Privacy Act (2021): Similar to the CCPA, it provides consumers with rights to manage their data and imposes obligations on businesses for data protection.

Connecticut Personal Data Privacy and Online Monitoring Act (2023) specifies consumer rights regarding personal data, online monitoring, and data privacy.

Delaware Personal Data Privacy Act (2022) outlines consumer rights and requirements for personal data protection.

Florida Digital Bill of Rights (2023) focuses on entities generating significant revenue from online advertising, outlining consumer privacy rights.

Indiana Consumer Data Protection Act (2023) details consumer rights and requirements for data protection.

Iowa Consumer Data Protection Act (2022) describes consumer rights and requirements for data protection.

Montana Consumer Data Privacy Act (2023) applies to entities conducting business in Montana, outlining consumer data protection requirements.

New Hampshire Privacy Act (2023): This act applies to entities conducting business in New Hampshire, outlining consumer data protection requirements.

New Jersey Data Protection Act (2023): This act applies to entities conducting business in New Jersey, outlining consumer data protection requirements.

Oregon Consumer Privacy Act (2022): This act details consumer rights and rules for data protection.

Tennessee Information Protection Act (2021) governs data protection and breach reporting.

Texas Data Privacy and Security Act (2023) describes consumer rights and data protection requirements for businesses.

Utah Consumer Privacy Act (2023) provides consumer rights and emphasizes data protection assessments and security measures.

Virginia Consumer Data Protection Act (2021) grants consumers rights to access, correct, delete, and opt out of their data processing.

Beyond US Borders: The Global Reach of Data Privacy

Data doesn't respect borders. The EU's General Data Protection Regulation (GDPR) is a robust international regulation that applies to any organization handling the data of EU residents. Understanding the GDPR's requirements for consent, data security, and data subject rights is essential for businesses operating globally.

Your Path to Compliance

Conquering the data privacy maze requires vigilance and a proactive approach. Here are some critical steps:

Map the Maze: Identify which federal and state laws apply to your business and understand their specific requirements. Conduct a comprehensive data inventory to understand what personal information you collect, store, and use.

Empower Your Customers: Develop clear and concise data privacy policies that outline your data collection practices and how you safeguard information. Make these policies readily available to your customers.  

Embrace Transparency: Give your customers control over their data by providing mechanisms to access, delete, and opt out of data sharing. Be upfront about how you use their data and respect their choices.  

Invest in Security Measures: Implement robust security measures to protect customer data from unauthorized access, disclosure, or destruction.

Stay Agile: The data privacy landscape is constantly evolving. Regularly review and update your policies and procedures to comply with emerging regulations. Appoint a team within your organization to stay abreast of these changes.

Wrapping Up

The data privacy landscape is complex and constantly evolving, but it doesn't have to be overwhelming. By understanding the key regulations, taking a proactive approach, and building a culture of compliance, you can emerge as a more vital, trusted organization. In today's data-driven world, prioritizing data privacy isn't just good practice – it's essential for building lasting customer relationships and achieving long-term success.

Apr 4
0
min
Free Your A-Team from Data Janitorial Duties

Data has undeniably become the new gold in the swiftly evolving digital transformation landscape. Organizations across the globe are mining this precious resource, aiming to extract actionable insights that can drive innovation, enhance customer experiences, and sharpen competitive edges. However, the journey to unlock the true value of data is fraught with challenges, often likened to navigating a complex labyrinth where every turn could lead to new discoveries or unforeseen obstacles. This journey necessitates a robust data infrastructure, a skilled ensemble of data engineers, analysts, and scientists, and a meticulous data consumption management process. Yet, as data operations teams forge ahead, making strides in harnessing the power of data, they frequently encounter a paradoxical scenario: the more progress they make, the more the demand for data escalates, leading to a cycle of growth pains and inefficiencies.  

The Bottleneck: Data Governance as a Time Sink

One of the most significant bottlenecks in this cycle is the considerable amount of time and resources devoted to data governance tasks. Traditionally, data control and protection responsibility has been shouldered by data engineers, data architects and Database Administrators (DBAs). On the surface, this seems logical – these individuals maneuver data from one repository to another and possess the necessary expertise in SQL coding, a skill most tools require to grant and restrict access. But is this alignment of responsibilities the most efficient use of their time and talents?  

The answer, increasingly, is no. 

While data engineers, DBAs and data architects are undoubtedly skilled, their actual value lies in their ability to design complex data pipelines, craft intricate algorithms, and build sophisticated data models. Relegating them to mundane data governance tasks underutilizes their potential and diverts their focus from activities that could yield far greater strategic value.

Imagine the scenario: A data scientist, brimming with the potential to unlock groundbreaking customer insights through advanced machine learning techniques, finds themself bogged down in the mire of access control requests, data masking procedures, and security audit downloads.

This misallocation of expertise significantly hinders the ability of data teams to extract the true potential from the organization's data reserves.

The Solution: Embracing Data Governance Automation

Enter the paradigm shift: data governance automation. This transformative approach empowers organizations to delegate the routine tasks of data governance and security to dedicated teams equipped with no-code control and protection solutions.

Solutions like ALTR offer a platform that empowers data teams to quickly and easily check off complex data governance task including:

  • Implementing data access policies: Leverage automated, tag-based, column and row access controls on PII/PHI/PCI data.
  • Dynamic data masking: Protect sensitive data with column-based and row-based access policies and dynamic data masking and scale policy creation with attribute-based and tag-based access control.
  • Generating audit trails: Maintain a comprehensive data access and usage patterns record, facilitating security audits and regulatory compliance.
  • Activity monitoring: Receive real-time data activity monitoring, policy anomalies, and alerts and notifications.

Freed from the shackles of routine data governance tasks, data teams can pivot towards more strategic and value-driven initiatives. Here are some of the compelling opportunities that could unfold:

Advanced-Data Analytics and Insights Generation

With more time at their disposal, data teams can delve deeper into data, employing advanced analytics techniques and AI models to uncover previously elusive insights. This could lead to breakthrough innovations, more personalized customer experiences, and data-driven decision-making across the organization.

Data Democratization and Literacy Programs

Data teams can spearhead initiatives to democratize data access, enabling a broader base of users to engage with data directly. Organizations can cultivate a data-driven culture where insights fuel every department's decision-making processes by implementing intuitive, self-service analytics platforms and conducting data literacy workshops.

Data Infrastructure Optimization

Attention can be turned towards optimizing the data infrastructure for scalability, performance, and cost-efficiency. This includes adopting cloud-native services, containerization, and serverless architectures that can dynamically scale to meet the fluctuating demands of data workloads.

Innovative Data Products and Services

With the foundational tasks of data governance automated, data teams can focus on developing new data products and services. This could range from predictive analytics tools for internal use to data-driven applications that enhance customer engagement or open new revenue streams.

Collaborative Data Ecosystems

Finally, data teams could invest time in building collaborative ecosystems and forging partnerships with other organizations, academia, and open-source communities. These ecosystems can foster innovation, accelerate the adoption of best practices, and enhance the organization's capabilities through shared knowledge and resources.

Wrapping Up

Automating data governance tasks presents a golden opportunity for data teams to realign their focus toward activities that maximize the strategic value of data. By embracing this shift, organizations can alleviate the growing pains associated with data management and pave the way for a future where data becomes the linchpin of innovation, growth, and competitive advantage. The question then is not whether data teams should adopt data governance automation but how quickly they can do so to unlock their full potential.

Mar 4
0
min
Step Into the Next Generation of Data Governance

Let's face it: your current data governance strategy is probably as outdated as a dial-up modem. You're still relying on clunky, manual processes, struggling to keep pace with ever-evolving regulations, and dreading the thought of a potential data breach. It's time to ditch the Stone Age tools and step into the ALTR era.

ALTR isn't just another data security platform; it's a game-changer. It's the excalibur you've been searching for, ready to slay the dragons of data security challenges and protect your kingdom (read: organization) from the ever-present threats.

Here's why ALTR is the ultimate upgrade for your data governance arsenal:

1. Classification: No More Guessing Games

Data classification is where the battle lines are drawn in data security. Yet, many organizations are stuck with rudimentary checkbox approaches that barely scrape the surface of what's needed. ALTR challenges this status quo by offering an intelligent, dynamic data classification system that doesn't just identify sensitive data but understands it. With ALTR, you're not just tagging data; you're gaining deep insights into its nature, usage, and risk profile. This isn't just classification; it's a strategic reconnaissance of your data landscape, enabling precise, informed decisions about access and security policies.

2. Dynamic Data Masking: Hide and Seek, Reinvented

In data protection, static defenses are as outdated as castle moats. ALTR brings the agility and adaptability of dynamic data masking to the forefront. Imagine your sensitive data cloaked in real-time, visible only to those with the right 'magical' keys. This isn't just about hiding data; it's about creating a flexible, responsive shield that adjusts to context, user, and data sensitivity, ensuring that your data remains protected in storage and in use.

3. Database Activity Monitoring: Big Brother, But for Good

With ALTR, database activity monitoring evolves from a passive logbook to an active, all-seeing eye that watches over your data landscape. This feature isn't just about tracking access; it's about understanding behavior, detecting anomalies, and preempting threats before they manifest. ALTR doesn't just alert you to breaches; it helps prevent them by offering insights into data access patterns, ensuring that any deviation from the norm is detected and dealt with in real-time.

4. Tokenization: The Ultimate Escape Artist

In a world where data breaches are a matter of when, not if, ALTR's tokenization vault offers the ultimate sleight of hand—making your sensitive data vanish, replaced by indecipherable tokens. This is more than encryption; it's a transformation that renders data useless to thieves, all while maintaining its utility for your business processes. With ALTR, tokenization isn't just a security measure; it's a strategic move that protects your data without compromising performance or functionality.

5. Format Preserving Encryption (FPE): Security Without Headaches

ALTR's Format Preserving Encryption (FPE) challenges the traditional trade-offs between data usability and security. With FPE, your data remains operational, retaining its original form and function, yet securely encrypted to ward off prying eyes. This feature is a game-changer, ensuring that your data can continue fueling business processes and insights while securely locked away from unauthorized access.

6. Data Access Governance: Take Back Control

Data access governance with ALTR is not about looking back at what went wrong; it's about looking ahead and preventing breaches before they happen. This is governance with teeth, offering not just oversight but foresight, enabling you to anticipate risks, enforce policies proactively, and ensure that every access to sensitive data is justified, monitored, and compliant with the highest security standards.

Ready to Ditch the Stone Age and Embrace the ALTR Era?

It's time to shed the cumbersome, outdated tools and strategies holding your data governance efforts back. The era of treating data security and compliance as burdensome chores is over. With ALTR, you're not just upgrading your technology stack; you're revolutionizing your entire approach to data governance. This isn't just a step forward; it's a leap into a new realm of possibilities where data security becomes your strength, not your headache.

Enhanced Data Security

Your data is the prize in the digital battlefield, and ALTR is your ultimate defence mechanism. By embracing ALTR, you're not just mitigating the risk of data breaches; you're rendering your data fortress impregnable. With dynamic data masking, tokenization, and format-preserving encryption, sensitive information becomes a moving target, elusive and indecipherable to unauthorized entities. This is data security reimagined, where your defences evolve in real-time, staying several steps ahead of potential threats.

Simplified Compliance

The labyrinth of data protection regulations can be daunting, with every misstep risking heavy penalties and reputational damage. ALTR transforms this maze into a clear path, simplifying compliance with its intelligent data governance framework. Whether GDPR, HIPAA, CCPA, or any other regulatory acronym, ALTR equips you to meet and exceed these standards with minimal effort. Say goodbye to the endless compliance checklists and welcome a solution that embeds regulatory adherence into the very fabric of your data governance strategy.

Improved Operational Efficiency

In the past, enhancing data security often meant compromising efficiency, but ALTR changed the game. By automating data classification, access governance, and policy enforcement, ALTR frees your teams from the quagmire of manual processes. This means less time spent on routine data governance tasks and more time available for strategic initiatives that drive business growth. Operational efficiency isn't just about doing things faster; it's about doing them more innovative, and that's precisely what ALTR enables.

Greater Data Insights

Knowledge is power, especially when managing and protecting your data. ALTR doesn't just secure your data; it shines a light on it, offering unprecedented insights into how, when, and by whom your data is accessed. These insights aren't just numbers and graphs; they're actionable intelligence that can inform your data governance policies, identify potential security risks, and uncover opportunities to optimize data usage. With ALTR, data insights become a strategic asset, driving informed decision-making across the organization.

Stop struggling with the relics of the past. It's time to embrace the future of data governance with ALTR, where data security, compliance, efficiency, and insights converge to propel your organization into a new era of digital excellence. 

Feb 28
0
min
Is It Time to Revisit Your Data Security Policy?

In an era where digital footprints are more significant than ever, the question isn't whether you should revisit your data security policy but how urgently you need to do so. With escalating cyber threats, evolving compliance landscapes, and sophisticated hacking techniques, the sanctity of data security has never been more precarious. As we navigate this digital dilemma, it's imperative to ask: Is your data security policy robust enough to withstand the challenges of today's cyber ecosystem?

The Alarming Surge in Cyber Threats

Recent years have witnessed an unprecedented spike in cyberattacks, targeting not just large corporations but small businesses and individuals alike. From ransomware attacks that lock out users from their own data to phishing scams that trick individuals into handing over sensitive information, the arsenal of cybercriminals is both vast and evolving. The question remains: Is your current data security policy equipped to fend off these modern-day digital marauders?

The Compliance Conundrum

As if the threat landscape wasn't daunting enough, businesses today also grapple with a labyrinth of regulatory requirements. GDPR, CCPA, and HIPAA - the alphabet soup of data protection laws- are confusing and comprehensive. Each of these regulations mandates stringent data protection measures, and non-compliance can result in hefty fines and irreparable damage to reputation. It's crucial for your data security policy to not only protect against cyber threats but also ensure compliance with these ever-changing legal frameworks.

The Human Element

Perhaps the most unpredictable aspect of data security is the human element. Studies suggest that many data breaches result from human error or insider threats. Whether a well-meaning employee clicking on a malicious link or a disgruntled worker leaking sensitive information, the human factor can often be the weakest link in your data security chain. A robust data security policy must address this variability, incorporating comprehensive training programs and strict access controls to mitigate the risk of human-induced breaches.

Emerging Technologies and Their Implications

The rapid advancement of technology brings with it new challenges in data security. The rise of IoT devices, the proliferation of cloud computing, and the advent of AI and machine learning have opened new frontiers for cybercriminals to exploit. Each of these technologies, while transformative, also introduces new vulnerabilities. Data security policies must evolve in tandem with these technological advancements, ensuring they address the unique challenges posed by each new wave of innovation.

The Road Ahead: Strengthening Your Data Security Posture

So, what does a robust data security policy look like today? Here are the key elements:

Purpose and Scope

  • Purpose: Clearly defines the reasons behind the policy, such as protecting sensitive information, ensuring privacy, and complying with legal and regulatory requirements.
  • Scope: Outlines the extent of the policy's applicability, specifying which data, systems, personnel, and departments are covered. It should clarify whether the policy applies to all data types or only specific classifications and whether it includes both digital and physical data formats.

Data Classification

  • Sensitivity Levels: Establishes categories for data based on its sensitivity and the level of protection it requires. Common classifications include Public, Internal Use Only, Confidential, and Highly Confidential.
  • Handling Requirements: Specifies handling requirements for each classification level, including storage, transmission, and sharing protocols. This ensures that more sensitive data receives higher levels of protection.

Roles and Responsibilities

  • Data Ownership: Identifies individuals or departments responsible for different types of data, outlining their responsibilities regarding data accuracy, access control, and compliance with the security policy.
  • Security Team: Defines the role of the security team or Chief Information Security Officer (CISO) in overseeing and enforcing the data security policy.
  • User Responsibilities: Clarifies the responsibilities of general users, including adherence to security practices, reporting suspected breaches, and understanding the implications of policy violations.

Access Control and Authentication

  • Access Control Policies: Details the mechanisms for granting, reviewing, and revoking access to data, ensuring that individuals have access only to the data necessary for their role.
  • Authentication Methods: Outlines the authentication protocols required to access different types of data, including multi-factor authentication, passwords, and biometric verification.

Data Protection Measures

  • Encryption: Specifies when and how data should be encrypted, particularly for sensitive information in transit and at rest.
  • Physical Security: Addresses the protection of physical assets, including servers, data centers, and paper records, outlining measures like access control systems and surveillance.
  • Endpoint Security: Covers security measures for user devices that access the organization's network, including antivirus software, firewalls, and secure configurations.

Data Retention and Disposal

  • Retention Schedules: Defines how long different types of data should be retained based on legal, regulatory, and business requirements.
  • Secure Disposal: Details methods for securely disposing of no longer needed data, ensuring that it cannot be recovered or reconstructed.

Incident Response and Management

  • Incident Response Plan: A clear, step-by-step guide for responding to data security incidents, including identification, containment, eradication, recovery, and post-incident analysis.
  • Reporting Structure: Outlines the procedure for reporting security incidents, including who should be notified and in what timeframe.

Training and Awareness

  • Regular Training: Mandates ongoing security awareness training for all employees, tailored to their specific roles and the data they handle.
  • Awareness Programs: Includes initiatives to keep data security in mind for employees, such as regular updates, posters, and security tips.

Policy Review and Modification

  • Review Schedule: Establishes a regular schedule for reviewing and updating the data security policy to ensure it remains relevant in changing threats, technologies, and business practices.
  • Amendment Process: Describes the process for proposing, reviewing, and implementing amendments to the policy, ensuring that changes are documented and communicated to all relevant parties.

Compliance and Legal Considerations

  • Regulatory Compliance: Identifies relevant legal and regulatory requirements that the policy helps to address, such as GDPR, HIPAA, or PCI DSS.
  • Legal Implications: Outlines the legal implications of policy violations for the organization and individual employees, including potential penalties and disciplinary actions.

Wrapping Up

In light of the evolving threat landscape and the complex regulatory environment, revisiting your data security policy is not just advisable; it's imperative. The cost of complacency can be catastrophic, ranging from financial losses to a tarnished reputation and legal repercussions. The time to act is now. By fortifying your defenses, staying abreast of regulatory changes, and fostering a culture of security, you can safeguard your organization against the multifaceted threats of the digital age. Remember, in data security, vigilance is not just a virtue; it's a necessity.

Feb 22
0
min
Format-Preserving Encryption vs Tokenization

Protecting sensitive data is paramount in today's digital landscape. But choosing the proper armor for the job can be confusing. Two major contenders dominate the data governance and data security ring: Format-preserving Encryption (FPE) and Tokenization. While both seek to safeguard information, their mechanisms and target scenarios differ significantly.

Deciphering the Techniques

Format-preserving Encryption (FPE)

Format-preserving encryption is a cryptographic technique that secures sensitive data while preserving its original structure and layout. FPE achieves this by transforming plaintext data into ciphertext within the same format, ensuring compatibility with existing data structures and applications. Unlike traditional encryption methods, which often produce ciphertext of different lengths and formats, FPE generates ciphertext that mirrors the length and character set of the original plaintext.

Why Is This Important

Compatibility: FPE allows companies to encrypt sensitive data while preserving the format required by existing systems, applications, or databases. This means they can integrate encryption without needing to extensively modify their data structures or application logic, minimizing disruption and avoiding potential errors or system failures arising from significant changes to established data formats or application workflows.

Preserving Functionality: In some cases, the functionality of applications or systems may rely on specific data formats. FPE allows companies to encrypt data while preserving this functionality, ensuring that encrypted data can still be used effectively by applications and processes.

Performance: FPE algorithms are designed to be efficient and fast, allowing for encryption and decryption operations to be performed with minimal impact on system performance. This is particularly important for applications and systems where performance is critical.

Data Migration: When migrating data between different systems or platforms, maintaining the original data format can be essential to ensure compatibility and functionality. FPE allows companies to encrypt data during migration while preserving its format, simplifying the migration process.

Tokenization

Tokenization is a data protection technique that replaces sensitive information with randomly generated tokens. Unlike format-preserving encryption, which uses algorithms to transform data into ciphertext, tokenization uses a non-mathematical approach. Instead, it generates a unique token for each piece of sensitive information and stores sensitive information in a secure database or token vault (read more about ALTR's PCI compliant vaulted tokenization offering). The original data is then replaced with the corresponding token, removing any direct association between the sensitive information and its tokenized form.  

Why Is This Important

Enhanced Security: Tokenization helps improve security by replacing sensitive data such as credit card numbers, bank account details, or personal identification information with tokens. Since tokens have no intrinsic value and are meaningless outside the system they're used in, malicious actors cannot exploit them even if intercepted.

Scalability: Scalability is a crucial strength of tokenization systems, stemming from their straightforward mapping of original data to tokens. This simplicity enables easy management and facilitates seamless scalability, empowering companies to manage substantial transaction volumes and data loads without compromising security or performance, all while minimizing overhead. This scalability is especially vital in sectors with high transaction rates, like finance and e-commerce, where robust and efficient data handling is paramount.

Interoperability: Tokenization can facilitate interoperability between different systems and platforms by providing a standardized method for representing and exchanging sensitive data without compromising security. 

System Integration: Tokenization systems often offer straightforward integration with existing IT infrastructure and applications. Many tokenization solutions provide APIs or libraries, allowing developers to incorporate tokenization into their systems easily. This ease of integration can simplify adoption and reduce development time drastically.  

Real World Scenarios

Using Tokenization over FPE

Consider a financial institution that needs to securely store and process credit card numbers for various internal systems and applications.  Instead of encrypting the credit card numbers, which could potentially disrupt downstream processes that rely on the original format, the company opts for tokenization.

Here's how it could work: When a credit card number is created or updated, the unique and identifiable numbers are replaced with randomly generated tokens. These tokens are then used to reference the original sensitive information, securely stored in a separate database or system with strict access controls.

When authorized personnel need to access or use the encrypted credit card numbers for legitimate purposes, they can retrieve the tokens and use them to access the stored sensitive information.  This allows the company to maintain compatibility with existing systems and processes that rely on the specific format of credit card numbers, such as payment processing or customer account management.

By implementing tokenization in this scenario, the organization can streamline access to data while ensuring that sensitive information remains protected.  

Using FPE over Tokenization

One scenario where a company might choose format-preserving encryption (FPE) over tokenization is in the context of protecting sensitive data while preserving its format and structure for specific business processes.

Imagine a healthcare organization that needs to securely store and share patient records containing personally identifiable information, such as names, addresses, and medical histories. Instead of tokenizing the entire document, which could slow down access and processing times, the organization decided to encrypt specific fields within the documents containing sensitive information.  

Here's how it could work: When a patient record is entered into the system, FPE is applied to encrypt sensitive fields, such as patient name, address, and medical record number, while preserving its original format. The encrypted data maintains the same structure, length, and validation rules as the original fields.

When authorized personnel need to access the patient records for legitimate purposes , they can decrypt them using the appropriate encryption keys.  This allows for efficient retrieval and processing of data without compromising security.

By using FPE in this scenario, the company can ensure that sensitive data remains protected while maintaining the integrity and usability of the data within its business operations. This approach balances security and functionality, allowing the company to meet data protection requirements without sacrificing operational efficiency or compatibility with existing systems.

Wrapping Up

Format-Preserving Encryption (FPE) and Tokenization offer practical strategies for securing sensitive data. By understanding each technique's unique advantages and considerations, organizations can make informed decisions to safeguard their data, protect against potential threats, and foster trust with customers and stakeholders.

Feb 8
0
min
Vaulted Tokenization vs Vaultless Tokenization: Key Points to Consider

In the ever-evolving landscape of data security, the debate between Vault and Vaultless tokenization has gained prominence. Both methods aim to protect sensitive information, but they take distinct approaches, each with different sets of advantages and limitations. In this blog, we will dive into the core differences that organizations consider when choosing an approach and how ALTR makes it easier to leverage the enhanced security of Vault Tokenization while still allowing for the scalability you'd typically find with Vaultless Tokenization. This decision ultimately comes down to performance, scalability, security, compliance, and total cost of ownership.  

Tokenization (both Vaulted and Vaultless), at its core, is the process of replacing sensitive data with unique identifiers or tokens. This ensures that even if a token is intercepted, it holds no intrinsic value to the interceptor without the corresponding key, which is stored in a secure vault or system.   

Vaulted Tokenization

Vaulted (or “Vault”) tokenization relies on a centralized repository, known as a vault, to store the original data. The tokenization process involves generating a unique token for each piece of sensitive information, while securely storing the actual data in the vault. Access to the vault is tightly controlled, ensuring only authorized entities can retrieve or decrypt the original data. For maximum security, the token should have no mathematical relationship to the underlying data; thus, preventing brute force algorithmic hacking, as can be possible when purely relying on encryption. Securing data in a vault helps reduce the surface area of systems that need to remain in regulatory compliance (ex. SOC 2, PCI- DSS, HIPAA, etc.), by ensuring the sensitive data located in the source system is fully replaced with non-sensitive values, thus requiring no compliance controls to maintain security.

The primary technical differentiator between Vaulted and Vaultless Tokenization is the centralization of data storage in a secure vault. This centralized storing method guarantees security and simplifies management and control, but may lead to concerns around scalability, and performance.

Vaulted tokenization shines in scenarios where centralized control and compliance are paramount. Industries with stringent regulatory requirements often find comfort in the centralized security model of vaulted tokenization.

Vaultless Tokenization

Vaultless tokenization, on the other hand, distributes the responsibility of tokenization across various endpoints or systems all within the core source data repository. In this approach, the generation and management of tokens occurs locally, eliminating the need for a centralized vault to store the original data. Each endpoint independently tokenizes and detokenizes data without relying on a central authority. While Vaultless Tokenization has a technically secure approach, this solution relies on tokenizing and detokenizing data from within the same source system. Similarly, this solution is less standardized across the industry and may result in vulnerability to compliance requirements around observability and proving that data stored locally is sufficiently protected.

Technical Differences

The decentralized nature of Vaultless tokenization enhances fault tolerance and reduces the risk of a single point of failure from a compromised vault. However, it introduces the challenge of ensuring consistent tokenization across distributed systems and guaranteeing data security and regulatory compliance.

Striking the Balance

While each approach has its merits, the ideal data security solution lies in striking a balance that combines the security of Vaulted Tokenization with the performance and scalability of Vaultless Tokenization. A hybrid model aims to leverage the strengths of both methods, offering robust protection without sacrificing efficiency, performance, industry norms, or compliance regulations.

ALTR’s Vault Tokenization Solution

ALTR’s Vault tokenization solution is a REST API based approach for interacting with our highly secure and performant Vault. As a pure SaaS offering, utilizing ALTR’s tokenization tool requires zero physical installation, and enables users to begin tokenizing or detokenizing their data in minutes. ALTR’s solution leverages the auto-scaling nature of the cloud, enabling on-demand performance that can immediately scale up or down based on usage.  

ALTR’s Vaulted Tokenization enhances the security and performance of sensitive data by being a SaaS delivered tool and having an advanced relationship with Amazon Web Services. Because of ALTR’s interoperability, many constraints of Vaulted Tokenization have been removed by properly building a scalable vault using cloud resources. ALTR can perform millions of tokenization and detokenization operations per minute per client basis without having the need for a Vaultless type of local implementation.  

Conclusion

In conclusion, the relative differences between Vaulted and Vaultless Tokenization underscore the importance of a nuanced approach to data security. The evolving landscape calls for solutions that marry the robust protection of a vault with the agility and scalability of a cloud-native SaaS model. ALTR’s Vault tokenization solution enables this unique offering by combining cloud-native scalability and ease-of setup / maintenance, with a tightly controlled, compliance optimized vault (PCI Level 1 DSS and SOC 2 type 2 certifications). Striking this balance ensures that organizations can navigate the complexities of modern data handling, safeguarding sensitive information without compromising performance or scalability.

Feb 7
0
min
10 Signs Your Data Access Control Is Falling Apart

In today's digital age, data is the lifeblood of businesses and organizations. Safeguarding its integrity and ensuring it stays in the right hands is paramount. The responsibility for this critical task falls squarely on the shoulders of effective data access control systems, which govern who can access, modify, or delete sensitive information. However, like any security system, access controls can weaken over time, exposing and making your data vulnerable. So, how can you spot the warning signs of a deteriorating data access control process? In this blog, we'll uncover the telltale indicators that your data access control is on shaky ground.

  1. Data Breaches and Leaks

It's undeniable that a data breach or leak is the most glaring and alarming indicator of your data access control's downfall. When unauthorized parties manage to infiltrate your sensitive information, it's akin to waving a red flag and shouting, "Wake up!" The unmistakable sign points to glaring vulnerabilities within your access control systems. These breaches bring dire consequences, including reputational damage, hefty fines, and the substantial erosion of customer trust. With the global average cost of a data breach at a staggering USD 4.45 million, it's most certainly something you want to avoid.

  1. Data Isolated in the Shadows

Do you find yourself with pockets of data hidden in different departments or applications, making it inaccessible to those who genuinely need it? This phenomenon creates data silos that obstruct collaboration and efficiency. Moreover, it complicates access control management, as each data silo may function under its own potentially inconsistent set of rules and protocols.

  1. Unclear Ownership and Accountability

Does anyone within your organization "own" data, ensuring its proper use and security? Vague ownership fosters a culture where everyone feels entitled to access, making it difficult to track user activity, identify responsible parties in case of misuse, and enforce access control policies.

  1. Manual Granting of Access

If access permissions are manually granted and updated, it's a clear sign that your access control system is outdated. Manual processes are time-consuming, error-prone, and hardly scalable. They create bottlenecks that delay legitimate users' access while increasing the risk of inadvertently granting unauthorized access. It's high time to transition to automated access control solutions to keep pace with the evolving demands of data security.

  1. Lack of User Reviews and Audits

According to recent data, IT security decision-makers say 77% of developers have excessive privileges. This concerning statistic underscores the importance of scrutinizing our data access control practices. Are access permissions infrequently reviewed and adjusted to align with evolving roles and responsibilities? Failing to conduct regular reviews results in outdated permissions persisting, needlessly granting access to individuals who no longer require it. Hence, conducting frequent audits becomes imperative, not only for identifying potential vulnerabilities but also for ensuring compliance with stringent regulations.  

  1. Weak Password Practice

Weak password practices, such as using easily guessable passwords, sharing passwords, or infrequently updating them, undermine the very foundation of data security. Data breaches often begin with compromised credentials, underscoring the critical importance of robust password policies and multi-factor authentication.

  1. Frequent Privilege Escalation

If users frequently request elevated access privileges to carry out their tasks, it suggests a deficiency in role-based access control (RBAC). RBAC assigns permissions based on roles and responsibilities, minimizing the need for escalated access and reducing the risk of misuse.

  1. Shadow IT and Unsanctioned Applications

Are employees using unauthorized applications or cloud storage solutions to access and share data? Shadow IT bypasses established security controls, creating blind spots and escalating the risk of data leaks. The implementation of sanctioned alternatives and enforcement of their use is paramount.

  1. Non-Compliance with Regulations

Does your organization handle sensitive data subject to stringent regulations like HIPAA, GDPR, or PCI DSS? Failure to comply with these regulations can result in substantial fines and reputational harm. Aligning your access controls with regulatory requirements is imperative to avoid hefty penalties.

  1. Difficulty Responding to Incidents

Is it challenging to track user activity and pinpoint the source of data breaches or leaks? How long after an incident or breach is your team notified? Without proper logging and auditing, investigating incidents becomes a time-consuming and frustrating endeavor. Effective logging and monitoring are prerequisites for quickly identifying and responding to security threats.

Addressing the Warning Signs

If you recognize any of these red flags within your data access control system, it's time to take decisive action. Here are some steps to strengthen your data access control:

  • Conduct a comprehensive security assessment to identify vulnerabilities and gaps in your existing controls.
  • Opt for an automated access control platform that lets you turn on access controls, apply data masking policies, and set thresholds with just a few clicks.
  • Get auditable query logs to prove privacy controls are working correctly.
  • Use a rate-limiting data access threshold technology to alert, slow or stop data access on out-of-normal requests - in real-time.
  • Enforce strong password policies and multi-factor authentication to make it harder for unauthorized individuals to gain access.
  • Educate users on data security to foster a culture of security awareness to minimize human error.
  • Stay updated on evolving threats and regulations and adapt your access controls to address new risks and compliance requirements.

Wrapping Up

Remember, data access control is an ongoing process, not a one-time fix. By heeding the warning signs and taking proactive measures, you can ensure that your data remains secure, protected from unauthorized access, and in the right hands, safeguarding your organization and its stakeholders.  

Feb 2
0
min
Health and Well-Being Technology Redefines Data Governance at Scale with ALTR

One of ALTR’s customers, a health, well-being, and navigation company, operates in over 190 countries worldwide, offering employee well-being and engagement solutions to organizations and their employees across the globe. Their mission is to transform workplace culture, promoting physical, mental, and emotional well-being to foster healthier and more productive work environments. 

In their quest to maintain the highest data governance and security standards, this organization embarked on a mission to securely store Personal Health Information (PHI) and Personally Identifiable Information (PII) data within Snowflake. This endeavor aimed to empower internal users with insightful access to data while simultaneously ensuring the establishment of a robust, closed-loop audit trail to meet stringent compliance requirements.

The Challenge

  • Ensuring Data Security and Privacy
  • Establishing Scalable Data Governance
  • Implementing a Compliance-Centric Audit Trail

Data Security and Privacy Assurance

The InfoSec Team at this company grappled with the critical necessity of securely and confidentially housing their sensitive PHI and PII data. This imperative arose from their unwavering commitment to conforming to stringent regulatory frameworks and compliance mandates that govern handling such sensitive information within the health and wellness industry. The integrity and confidentiality of this data were paramount.

Scalable Data Governance

With an expansive and intricate data landscape sprawling across multiple Snowflake databases, they faced the formidable challenge of implementing and enforcing data governance policies at scale. The sheer volume of tagged columns, numbering in the thousands, necessitated an innovative approach to ensure the consistent and efficient application of governance protocols.

Compliance-Centric Audit Trail

To align comprehensively with evolving data privacy regulations, this organization recognized the need to establish a meticulous and all-encompassing audit trail. This trail would serve as an indisputable record of every instance of access to sensitive data. Achieving full compliance required not just meeting the letter of the law but also demonstrating dedication to transparency and accountability in the data handling practices.

The Solution

  • Cloud-Native Integration with Snowflake
  • Efficient Automated Column Controls
  • Query-level Governance

Cloud-Native Integration with Snowflake

Implemented as a cloud-native solution and utilizing Snowflake's native governance and security features, ALTR offered the highest level of data protection—all with no code required to implement, maintain, or manage. Removing the roadblocks to protecting sensitive data ensures this organization’s data team can extract the most value from their data and maximize their investment in the platform.

Automated and Scalable Tag-Based Masking

ALTR introduced automated tag-based column control to govern PII and PHI data security at scale. With ALTR's user-friendly point-and-click interface and management API, this organization was able to harness the power of Snowflake object tagging and enable the automatic application of data masking to thousands of tagged columns spanning multiple Snowflake databases. As a result, they were able to apply policies uniformly to corresponding tagged columns quickly and easily and instantly enforce policies as soon as sensitive data is tagged.

Query-Level Governance

ALTR's auditable query logs emerged as an indispensable tool, meticulously documenting every instance of sensitive PHI and PII data access to prove privacy controls were effective. This company can now govern each user down to the individual query, track and log all activity, including administrative actions and implement rules and thresholds to govern the flow of data.

The Result

  • Complete Data Access Observability
  • Data Governance at Scale
  • Comprehensive Compliance-Ready Audit Trail
  • A Visionary Leader  

Complete Observability

This organization meticulously achieved a state of complete data observability, which has become the cornerstone of their data security framework. This heightened level of transparency not only fortified their data security infrastructure but also enabled them to proactively monitor, track, and respond to all instances of access to sensitive PHI and PII data. As a result, no unauthorized or suspicious activities go unnoticed, providing an invaluable layer of protection for their most critical information assets.

Data Governance at Scale

ALTR's solution empowered this customer to seamlessly automate data masking policies across a sprawling landscape of tagged columns spanning multiple Snowflake databases. This automation substantially reduced manual efforts and contributed to policy consistency and effectiveness.

Comprehensive Compliance-Ready Audit Trail

The solution delivered an exhaustive audit trail that meticulously documented every instance of sensitive data access. This comprehensive audit trail played a pivotal role in this organization’s ability to fully satisfy the requirements of data privacy regulations and compliance standards.

A Visionary Leader

This company's proactive embrace of tag-based policies and their astute utilization of automation exemplified their forward-thinking approach to data governance and significantly influenced the evolution of ALTR's data governance capabilities.

ALTR's easy-to-use solution allows our Data, Reporting and Analytics teams to leverage Snowflake object tagging to automatically apply data masking to thousands of tagged columns across multiple Snowflake data bases. We're able to store PII/PHI data securely and privately with a complete audit trail. Our internal users gain insight from this masked data and change lives for good. 
- Director of Data Governance and Management
Jan 31
0
min
The Anatomy of AI Governance

In an era where artificial intelligence (AI) wields unprecedented power and influence, the need for comprehensive AI governance has never been more urgent. As AI technologies continue to evolve, they hold immense promise but also harbor significant risks. To harness the potential of AI while safeguarding against its potential pitfalls, organizations must embrace a robust framework for AI governance that goes beyond mere compliance and extends into proactive stewardship. In this blog, we'll delve into the depths of AI governance, exploring its technical intricacies, its role in securing data, and its vital importance in a world increasingly dominated by AI.

The Rise of AI

AI is no longer a futuristic concept but a reality that permeates our daily lives. From autonomous vehicles and virtual assistants to medical diagnosis and financial analysis, AI is revolutionizing industries across the globe. But this transformative power comes with a dark side. The same AI systems that enable groundbreaking discoveries and operational efficiencies also introduce new risk vectors, including privacy breaches, algorithmic bias, and ethical dilemmas.

The Complex AI Ecosystem

Before diving into the nuances of AI governance, it's crucial to understand the complexity of the AI ecosystem. AI systems are comprised of multiple layers, each demanding careful attention:

Data: The lifeblood of AI, data is the raw material from which AI algorithms derive insights. Data governance involves collecting, storing, and protecting data, ensuring its quality, accuracy, and ethical use.

Algorithms: AI algorithms, often called "black boxes," make decisions and predictions based on data. These algorithms can be prone to biases, necessitating careful auditing and transparency.

Infrastructure: The hardware and software infrastructure supporting AI models must be secure and compliant with regulatory standards.

Deployment: AI models must be deployed with a clear understanding of their impact on users and society, mitigating potential risks.

The Need for AI Governance

As AI's influence grows, so do the risks associated with it. Governance is the linchpin that holds together the pillars of AI security, ethics, and compliance. Here's why robust AI governance is imperative:  

Mitigating Bias: AI algorithms can inadvertently reinforce existing biases present in the training data. Governance frameworks, like fairness audits, can help identify and rectify these biases.

Protecting Privacy: AI systems often handle sensitive personal data. Governance ensures compliance with data protection laws and safeguards against unauthorized access.

Ensuring Accountability: AI decision-making can be inscrutable. Governance demands transparency and accountability in AI system behavior, enabling users to understand and challenge decisions.

Ethical Considerations: As AI makes decisions with profound societal impact, governance frameworks help organizations navigate ethical dilemmas, from autonomous vehicles' moral choices to the responsible use of AI in warfare.

AI Governance Best Practices

IAPP found that 60% of organizations with AI deployments have established or are developing AI governance frameworks. While there's no one-size-fits-all approach, some best practices are emerging in the ever-evolving landscape of AI governance:  

Focus on Explainability and Transparency

  • Prioritize XAI techniques: Shed light on how AI algorithms reach their decisions, building trust and enabling human oversight. Tools like feature importance analysis and decision trees can be helpful.
  • Document data provenance: Track the origin and evolution of data used to train and operate AI systems, ensuring its validity and traceability.
  • Communicate effectively: Proactively engage stakeholders with clear and concise explanations about AI usage, its purpose, and potential implications.

Mitigate Bias and Ensure Fairness

  • Conduct data audits: Regularly analyze training data for potential biases related to race, gender, age, or other sensitive attributes. Tools like fairness analysis algorithms can help identify and address disparities.
  • Employ diverse development teams: Incorporate individuals from various backgrounds and perspectives into the design and development process to minimize biases inherent in homogenous teams.
  • Implement counterfactual testing: Simulate scenarios where AI decisions differ based on protected attributes, revealing potential bias and prompting corrective action.

Protect Privacy and Security

  • Adopt privacy-preserving AI techniques: Utilize methods like differential privacy and federated learning to train and operate AI models without compromising individual data privacy.
  • Implement robust data security measures: Employ encryption, access control mechanisms, and regular security audits to safeguard sensitive data used by AI systems.
  • Develop transparent data governance policies: Establish explicit guidelines on data collection, storage, usage, and disposal, fostering responsible data handling practices within the organization.

Promote Accountability and Auditability

  • Define clear lines of responsibility: Establish who is accountable for the development, deployment, and outcomes of AI systems, ensuring individual ownership and facilitating remediation processes.
  • Maintain audit trails: Record critical decisions, data flows, and model performance metrics to enable retrospective analysis and identify potential issues.
  • Implement feedback mechanisms: Establish channels for users and stakeholders to report concerns or raise questions about AI decisions, enabling course correction and continuous improvement.

Continuously Monitor and Manage Risk

  • Conduct regular risk assessments: Proactively identify potential risks associated with AI systems, ranging from technical faults to ethical concerns.
  • Develop mitigation strategies: Implement safeguards and contingency plans to address identified risks, minimize potential harms, and ensure robust system operation.
  • Embrace a "learning by doing" approach: Continuously monitor AI systems in real-world settings, gather feedback, and adapt governance practices based on emerging challenges and opportunities.

Remember…

  • Collaboration is critical: Engage with diverse stakeholders, including policymakers, researchers, and civil society, to create and refine AI governance frameworks.
  • Flexibility is essential: Be prepared to adapt and iterate on your governance approach as technology advances and societal expectations evolve.
  • Prioritize human oversight: Don't abdicate responsibility to algorithms; humans must remain in the driver's seat, guiding AI towards ethical and beneficial applications.

A Provocative Proposition: Self-Governing AI  

As the AI landscape continues to evolve, one provocative idea is gaining traction: self-governing AI. Imagine AI systems capable of monitoring their behaviour, identifying biases or ethical concerns, and taking corrective action in real time. While this may seem like science fiction, researchers are actively exploring AI mechanisms for self-awareness and self-regulation.

Self-governing AI is a fascinating prospect but also a complex technical challenge. It requires the development of AI algorithms that can introspect, detect deviations from ethical norms, and even modify their decision-making processes when necessary. While this technology is in its infancy, it represents a powerful vision for the future of AI governance.

Wrapping Up

As we journey into the age of AI, we must strive for compliance and aspire to become stewards of responsible AI. The tantalizing prospect of self-governing AI beckons, promising a future where AI systems learn from data and their own ethical compass. Until that day arrives, organizations must commit to robust AI governance to navigate the AI abyss and secure a brighter, more responsible AI-powered future.

Jan 25
0
min
Data Democratization: Building a Culture of "Data Citizens" for Faster, Smarter Decisions

The reign of data overlords is ending. Gone are the days when insights were hoarded by tech wizards, and the "regular people" were left in the dark, their decisions guided by gut instinct and wishful thinking. The new frontier? Data democratization: a revolution where everyone, from the marketing intern to the CEO, wields the power of information to forge better decisions faster.

Why embrace this democratic approach? Because, in today's data-driven landscape, companies clinging to centralized data control are like monarchs clinging to crumbling castles – vulnerable, slow, and ultimately destined to be overtaken by nimbler, more decentralized forces.

Here's the truth: we don't need a data scientist in every room. We need data citizens in every room. People who understand the language of data can ask the right questions and can use insights to drive innovation and growth. The beauty of data democracy is that it unleashes the collective intelligence of an entire organization, tapping into the unique perspectives and expertise of individuals who wouldn't otherwise have a voice.

But democratization isn't just about throwing open the data vaults and yelling "free-for-all!" It's about creating a culture where data literacy is encouraged, where people feel empowered to ask questions, and where there's a safety net to catch those venturing into unfamiliar territory. It's about providing the right tools and training, not just access to raw numbers. It's about building trust and transparency, ensuring everyone understands the rules of the data game.

Benefits of Data Democratization

The benefits of this shift are tangible and transformative:

Faster, more agile decision-making

No more waiting for the oracle in the data lab. With everyone empowered to analyze and interpret data, decisions can be made closer to the action, with real-time insights guiding every step.

Unleashing hidden innovation

Data isn't just for bean counters anymore. When everyone becomes a data citizen, new ideas and opportunities blossom from unexpected corners. The marketing team might discover a hidden customer segment, the sales team might uncover a surprising competitor weakness, and the janitor might even suggest a data-driven way to save energy costs.

Boosting employee engagement

When people feel they have a say in data use, they're more invested in the outcome. Data democracy builds trust and ownership, leading to a more engaged and productive workforce.

Let's delve into some real-world examples:

Sales: Imagine a salesperson armed with real-time customer purchase history and sentiment analysis from social media. They can identify high-value leads, personalize their approach, and close deals with laser-like precision. Data becomes their secret weapon, guiding them towards the most promising opportunities.

Marketing: Marketers crave insights into customer behavior and campaign effectiveness. Data democratization grants them access to website traffic patterns, A/B testing results, and social media engagement metrics. This empowers them to craft targeted campaigns, optimize ad spend, and predict future trends with newfound accuracy.

Finance: For finance professionals, data is the lifeblood of responsible decision-making. With real-time access to financial performance metrics, budgeting tools, and risk analysis dashboards, they can confidently make informed investments, optimize resource allocation, and navigate market fluctuations.

Human Resources: HR teams can leverage data to identify top performers, predict employee churn, and tailor training programs to individual needs. Analyzing employee performance data, engagement surveys, and skills assessments can create a more dynamic and productive work environment.

Product Development: Data is the fuel for innovation in product development. By analyzing customer feedback, usage patterns, and competitor analysis, teams can identify unmet needs, refine product features, and prioritize development efforts based on real-world demand.

These are just a few examples, and the possibilities are endless. Data democratization empowers every department to become a data-driven powerhouse, unlocking insights that were once hidden in the shadows.  

The Road to Data Democratization

Tear down the walls

Let the data breathe! Smash the silos that trap information within departments, fostering a web of interconnected sources. Invest in user-friendly platforms that banish jargon and replace it with intuitive dashboards and vibrant visualizations. Data shouldn't be a cryptic language reserved for the tech elite; it should be a vibrant conversation accessible to all.

Ignite curiosity

Don't simply hand people tools; equip them with the knowledge to wield them effectively. Invest in data literacy programs, not just for analysts but for everyone. From understanding basic statistics to interpreting trends, equip your workforce with the skills to ask the right questions and extract meaningful insights.

Empowerment isn't just about access; it's about ownership

Encourage self-service exploration. Let your employees dive into the data, experiment, and discover connections no algorithm could predict. Foster a culture of data-driven decision-making, where insights guide every step, from marketing campaigns to operational optimizations.

But remember, with great power comes great responsibility

Data democratization promises a data-driven utopia, but without a robust set of principles guiding its execution, it can descend into chaos. Here are some essential data governance principles to build a foundation of trust and responsibility in your open data environment:

  • Transparency and Accountability: To enable data democratization, it's crucial to establish clear roles and responsibilities, ensuring that every data user comprehends their rights and responsibilities. Promoting open communication encourages questions and feedback, fostering transparency. Additionally, tracking and auditing data access helps monitor utilization and detect potential misuse or unauthorized access, ensuring accountability.  
  • Data Quality and Consistency: For effective data democratization, organizations should set data quality standards, specifying accuracy, completeness, and timeliness requirements for reliable insights. Regular data cleansing and validation processes are essential to address inconsistencies and errors and preserve data integrity. Encouraging a data-driven culture among users prompts them to question data validity, reducing the risk of biased or inaccurate decisions.
  • Security and Privacy: To maintain security and privacy in a democratized data environment, data should be classified by sensitivity, determining access levels based on confidentiality and potential impact if compromised. Robust security measures, such as format-preserving encryption and data tokenization, protect sensitive data from unauthorized access and malicious attacks. Compliance with data privacy regulations like GDPR and CCPA is crucial to safeguard individual privacy and prevent misuse of personal data.

Wrapping Up

Data democratization is a journey, not a destination. Monitor your progress, gather feedback, and constantly adapt. Celebrate successes, learn from failures, and encourage open dialogue. Remember, a truly data-driven organization is one where information flows freely, fueling innovation, collaboration, and, ultimately, unstoppable growth.

Jan 18
0
min
Essential Data Governance Metrics You Should Be Tracking

In today's data-driven world, organizations hold a vast treasure trove of information. But with great power comes great responsibility. Effectively managing, securing, and leveraging this data demands a robust framework: data governance. And just like any successful journey, it requires a map – a set of metrics to guide the way.  

Data governance metrics are vital instruments, providing objective insights into the effectiveness of your program. They illuminate strengths, expose weaknesses, and ultimately steer you towards data-driven decision-making. But with many metrics available, navigating the landscape can feel overwhelming. This blog will equip you with the knowledge and tools to build a clear and valuable data governance metrics framework.  

Why Measure? The Value of Data Governance Metrics

Data governance is not just a box to tick; it's a continuous journey of improvement. Tracking progress through metrics offers tangible benefits:  

1. Demonstrating ROI

To truly showcase the value of your data governance program, it's essential to quantify its impact on the organization's bottom line. One powerful way to do this is linking metrics to tangible business outcomes. For instance, showing a 20% reduction in data-related errors since implementing your data governance measures speaks volumes about the program's effectiveness. Similarly, quantifying a 15% increase in data-driven revenue demonstrates how data governance can directly contribute to the company's financial success. These concrete numbers impress stakeholders and justify the investment in data governance. Using metrics to demonstrate ROI, you can communicate that data governance isn't a cost center, but a strategic asset that delivers measurable returns.

2. Gaining Buy-in

Securing and sustaining executive support for data governance initiatives can be challenging without irrefutable evidence of progress. Metrics play a pivotal role in gaining buy-in from top-level decision-makers. When you can present quantifiable data points that showcase data governance's positive impact, garnering support becomes much more accessible. Executives are more likely to invest time and resources when they see their decisions yield tangible results. Metrics provide a compelling argument and help maintain this support over the long term. The ability to track and report on progress ensures that executives remain engaged and committed to the success of your data governance program.

3. Optimizing Performance

Data governance is an ongoing process, and improving and adapting to changing circumstances is crucial. Metrics are invaluable in this regard because they allow you to identify areas for improvement. For example, suppose you track user adoption rates after implementing a new data access policy and find that they haven't increased as expected. In that case, it's a clear signal that adjustments may be needed. Metrics help pinpoint inefficiencies and roadblocks, enabling you to refine your data governance strategies and policies. By constantly optimizing performance based on data-driven insights, your organization can stay agile and ensure that its data governance efforts remain effective and aligned with evolving business needs.

4. Enhancing Accountability

In a successful data governance program, accountability is critical. Clear and well-defined metrics can assign ownership and responsibility to individuals or teams, ensuring that everyone contributes to data governance success. When people know they are held accountable for specific data-related outcomes, they are more likely to take their responsibilities seriously. Metrics provide a way to measure and track progress, making it evident when goals are met, or actions need to be adjusted. This accountability fosters a culture of responsibility within the organization. It ensures that data governance is not seen as a mere theoretical concept but as a practical and integral part of daily operations. As a result, the entire organization becomes more invested in maintaining data quality and integrity.

Key Data Governance Metrics to Track

Now, let's delve into the specific metrics that can illuminate your data governance path. Remember, there's no one-size-fits-all approach – tailor your selection to your organization's unique goals and challenges. Here are some key categories to consider:

Data Quality

  • Completeness: What percentage of data is missing? Are critical fields empty? Aim for minimal null values for reliable analysis.
  • Accuracy: Does the data represent reality? Compare it to trusted sources to validate its integrity.
  • Timeliness: Is data fresh and up-to-date? Stale data hinders informed decision-making. Track average data age and set freshness targets.
  • Consistency: Do data elements follow defined formats and rules? Inconsistent data leads to confusion and errors. Monitor rule compliance and address inconsistencies.
  • Relevance:  Does the data align with intended business use cases? Ensure data serves its purpose effectively by evaluating its contextual appropriateness.

Data Security and Privacy

  • Breach frequency: Track the number of data breaches and near-misses. A decreasing trend signals improved security posture.
  • Access control effectiveness: Measure unauthorized access attempts. Monitor user access logs and refine access controls based on the principle of least privilege.
  • Data privacy compliance rate: Assess compliance with relevant regulations like GDPR or CCPA. Track the percentage of data requests fulfilled accurately and on time. 

Data Availability and Usability

  • Downtime incidents: Track the frequency and duration of data system outages. Minimize downtime for uninterrupted data access.
  • Data discovery rate: How easily can users find the data they need? Measure search success rates and refine data catalogs and metadata management practices.
  • Data utilization rate: Are users actively leveraging data for analysis and decision-making? Track data usage patterns and identify opportunities to increase adoption.

Data Governance Maturity

  • Policy adoption rate: Measure the percentage of users adhering to data governance policies. High adoption indicates effective communication and training.
  • Data lineage completeness: Track the origin, transformations, and destination of data across your systems. Clear lineage facilitates data trust and troubleshooting.
  • Business unit engagement: Assess the involvement of different business units in data governance initiatives. Broad participation fosters a data-driven culture.

Beyond the Numbers: Building a Holistic Framework

Remember, metrics are tools, not the destination. Effective data governance requires a holistic approach that considers not just the "what" but also the "why" and "how." Contextualize your metrics:

Align with Business Goals

Tie data governance metrics to broader business objectives. How does improved data quality impact customer satisfaction? Does efficient data access drive revenue growth?

Balance Quantitative and Qualitative Measures

Supplement objective data with qualitative insights from user surveys, interviews, and feedback. Understand the human side of data governance.

Communicate Effectively

Share your metrics with stakeholders in a clear, concise, and actionable manner. Visualize data to enhance understanding and drive engagement.

Wrapping Up

Data governance is not a static endeavor, and neither should your metrics. Regularly review and refine your framework to adapt to evolving needs and ensure it remains a relevant and valuable guide on your data journey.

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