Data is the fuel propelling modern business. From customer information to financial records, proprietary data forms the foundation upon which businesses operate and innovate. However, as companies grow and data volumes explode, securing this data becomes exponentially more complex. This is where the importance of scalability in data security comes into sharp focus.

The Scalable Security Imperative

Scalability in data security is not a luxury; it is a necessity. As organizations expand, they generate and collect vast amounts of data. This growth demands a data security solution that can scale seamlessly with the volume, velocity, and variety of data. Organizations expose themselves to heightened risks, increased vulnerabilities, and potential catastrophic breaches without scalable security measures.

Core Pillars of Scalable Data Security

To understand the nuances of scalable security, we must delve into its core pillars: flexibility, performance, automation, and comprehensive coverage.

1. Flexibility

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Flexibility is the cornerstone of scalable security. A rigid security solution that cannot adapt to changing needs and expanding data environments is destined to fail. Scalable security solutions must be flexible enough to integrate with a wide array of data sources, applications, and infrastructures, whether on-premises, in the cloud, or hybrid environments.

Flexibility also means accommodating varying security policies and compliance requirements. As regulations evolve and new threats emerge, a scalable security platform must allow for rapid adjustments to policies and controls without disrupting operations.

2. Performance

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As data volumes grow, maintaining performance is crucial. Security measures that introduce latency or degrade performance are counterproductive and can hinder business operations and user experience. Scalable data security solutions must be designed to handle high throughput and large-scale environments without compromising o speed or efficiency.

Performance in scalable security also involves optimizing resource utilization. Efficient use of computational resources ensures that security operations, such as encryption, decryption, and monitoring, do not become bottlenecks as data scales.

3. Automation

Automation is a critical component of scalability in data security. Manual processes are time-consuming, error-prone, and incapable of keeping up with the dynamic nature of modern data environments. For instance, manually writing and maintaining SQL queries for data access control can be labour-intensive and prone to mistakes. Scalable security platforms leverage automation to ensure continuous protection without requiring constant human intervention.

Automated access policies, tokenization, and policy enforcement allow organizations to scale their security operations in line with their data growth. This automation enhances security posture and frees up valuable human resources to focus on strategic initiatives.

4. Comprehensive Coverage

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Scalable security requires comprehensive coverage across all data assets and environments. It is insufficient to secure only certain parts of the data ecosystem while leaving others vulnerable. A genuinely scalable security solution provides end-to-end protection, encompassing data at rest, in transit, and use.

Comprehensive coverage also means detecting and mitigating threats across the entire attack surface. This includes monitoring for insider threats, external attacks, and vulnerabilities within the data infrastructure. Scalable security platforms employ advanced analytics and machine learning to provide real-time insights and proactive threat management.

The Nuances of Scalable Security

The complexity of scalable security lies in its ability to balance the varying demands of growth, performance, and protection. Here are some critical nuances to consider:

Future-Proofing

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Scalable security solutions must be designed with future growth in mind. This involves anticipating the increase in data volume and users, the evolution of threat landscapes, and regulatory requirements. Future-proofing ensures that security investments remain practical and relevant as the organization evolves.

Interoperability

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Interoperability is critical in a diverse data ecosystem. Scalable security platforms must seamlessly integrate with existing tools, applications, and processes. This integration capability ensures that security measures do not operate in silos but rather enhance the overall security posture through cohesive and collaborative defenses.

Cost-Effectiveness

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As data scales, so do the costs associated with securing it. Scalable security solutions must provide a cost-effective approach to protection, balancing the need for robust security with budget constraints. One approach is to leverage native architectures to manage costs effectively.

The Stakes of Inadequate Scalability

The consequences of failing to implement scalable security measures are dire. As data grows unchecked by scalable security, organizations face an increased risk of data breaches, regulatory fines, and reputational damage. Here are some potential pitfalls:

Data Breaches

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Without scalable security, the likelihood of data breaches increases significantly. Cybercriminals exploit vulnerabilities in outdated or inadequate security measures, leading to unauthorized access, data theft, and financial losses.

Regulatory Non-Compliance

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Data protection regulations are becoming increasingly stringent. Organizations that fail to scale their security measures in accordance with these requirements risk non-compliance, which can result in hefty fines and legal repercussions.

Operational Disruptions

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Inadequate security stability can lead to operational disruptions. Performance bottlenecks, system downtime, and compromised data integrity can impede business operations, leading to loss of productivity and revenue. Additionally, when security measures fail to scale, legitimate users may be unable to access critical data, causing further delays and hindering decision-making processes. This not only frustrates employees but also hampers overall business efficiency and agility.

Wrapping Up

In a world where data is both a valuable asset and a potential liability, the importance of scalable security cannot be overstated. As businesses continue to expand and generate more data, the need for robust, scalable security measures will only become more critical. Embracing scalable security is about protecting data today and preparing for tomorrow's challenges. The time to act is now.

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Imagine waking up to the news that your company's sensitive data has been compromised, all due to stolen credentials. With recent high-profile data breaches making headlines, this nightmare scenario has become all too real for many organizations. The stakes are higher than ever, and ensuring robust security measures to protect your sensitive data in Snowflake is not just important—it's essential.

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Snowflake's white paper, "Best Practices to Mitigate the Risk of Credential Compromise," is your roadmap to fortified security. This comprehensive guide reveals how to leverage Snowflake's native platform features to enforce strong authentication and mitigate the ever-present risks associated with credential theft. This blog will dive into the key takeaways and best practices recommended by Snowflake to safeguard your organization's data.

The Pillars of Security

Snowflake's approach to security is built on three key pillars:  

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Prompt

Encourage users to adopt security best practices, such as configuring multifactor authentication (MFA). This proactive approach ensures that users are aware of security protocols and actively engage with them. It's about creating a culture of security and mindfulness.

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Enforce

Enable administrators to enforce security measures by default. This means implementing policies that automatically apply security best practices across the board, reducing the likelihood of human error or oversight.

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Monitor

Provide visibility into security policy adherence. Monitoring ensures that security measures are not just in place but are being followed and are effective. Continuous visibility allows for timely adjustments and responses to potential threats.

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By grounding its security framework in these pillars, Snowflake ensures a comprehensive approach to protecting sensitive data from unauthorized access.

Best Practices for Enforcing Authentication and Network Policies

To safeguard your Snowflake account, it's crucial to follow these essential steps:

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1. Create Authentication Policies for Service Users

Use key pair or OAuth for programmatic access and enforce this through authentication policies. Service accounts, which are often targeted by attackers, should have the most stringent security measures. By using key pairs or OAuth, you ensure a higher security level than traditional username/password combinations.

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2. Enforce MFA for Human Users

Leverage your own SAML identity providers with MFA solutions. For added security, enforce Snowflake's native MFA for users relying on native passwords. MFA adds an additional layer of security, making it significantly harder for attackers to gain access using stolen credentials.

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3. Establish Robust Password Policies

Implement stringent password requirements and regular password changes. Strong passwords and regular updates reduce the risk of password-based attacks. Policies should include guidelines on password complexity and the frequency of changes.

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4. Implement Session Policies

Define policies to enforce reauthentication after periods of inactivity. This helps to minimize the risk of unauthorized access from inactive sessions. Policies should specify session timeout periods and conditions for reauthentication.

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5. Apply Account-level Network Policies

Restrict access to authorized and trusted sources only. By defining network policies, you can ensure that only trusted IP addresses and networks can access your Snowflake account, reducing the attack surface.

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6. Protect Service Users

Differentiate between human and service users by setting user types, which helps in applying appropriate security measures. Service users often have elevated permissions, making them prime targets for attacks. By categorizing them appropriately, you can apply stricter security controls.

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7. Apply and Test Policies

Apply password and session policies at the account level and test service users to ensure their effectiveness. Regular testing and validation of policies help identify potential gaps and ensure that security measures are working as intended.

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8. Enforce Account-Level MFA

Apply MFA enforcement policies to ensure all human interactive users use MFA. This universal application of MFA ensures that every user accessing the system is authenticated through multiple factors, significantly enhancing security.

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9. Leverage Snowflake's Trust Center

Utilize Snowflake's Trust Center to monitor MFA and network policy enforcement continuously. Monitoring helps maintain a robust security posture by providing insights into policy adherence and identifying areas for improvement. Additionally, consider CIS benchmarks for industry-standard security practices and guidelines.

Wrapping Up

The digital landscape is fraught with threats, and credential compromise remains a top concern for organizations. Implementing the best practices outlined here is your first line of defense. However, it's not enough to set these measures and forget them. Continuous vigilance, regular updates, and a proactive stance are crucial.  

Snowflake is your ally in this ongoing battle, providing the necessary tools and insights to effectively monitor and enforce security policies. By leveraging Snowflake's robust security framework, you can ensure your organization stays ahead of potential threats.

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In today's hyper-connected world, businesses thrive on data. Every transaction, customer interaction, and strategic decision is driven by the vast amounts of information collected and stored. This data fuels innovation, enhances customer experiences, and propels growth. Yet, with this immense power comes a chilling reality: data breaches are an ever-present threat. From stolen customer information to compromised intellectual property, the consequences for businesses can be catastrophic. As these threats escalate, the burning question remains - how much data security is truly enough for your business?

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Unfortunately, the answer is frustrating – there might not be a magic number. Here's why:

The Impenetrability Illusion

Imagine a bank vault guarded by the most advanced security system. This is the traditional security mindset – an impenetrable fortress. However, cyberattacks are a relentless foe, constantly evolving to exploit new vulnerabilities faster than patches can be deployed. No system is truly invincible.

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The Security-Usability Tightrope

The ideal security system for a business might resemble Fort Knox, but that's not practical for everyday operations. Requiring retinal scans, fingerprints, voice verification, and a complex 30-character password just to access your company's internal systems would be excessively secure but also frustrating and inefficient for employees. Striking a balance between robust security and user-friendly access controls is crucial for businesses to navigate the security-usability tightrope effectively. Companies must implement security measures that protect sensitive data without impeding productivity or causing undue stress for users.

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The Cost Conundrum

Investing in a million-dollar security system might make sense for a financial institution safeguarding sensitive data, but it would be overkill for a small business.Security measures come with a price tag – software, hardware, and trained personnel. The cost of these measures must be weighed against the potential damage of a breach. Prioritizing security investments based on the specific risks and needs of the business is crucial to ensure that resources are used effectively and efficiently. Companies must find the right balance between adequate protection and financial feasibility.

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The Insider Threat

Imagine a trusted employee leaking sensitive data. Even the most sophisticated security cannot defend against disgruntled employees or social engineering attacks. Human error and malicious intent are ever-present dangers. Security awareness training and a culture of data responsibility are essential.

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The Evolving Threat Landscape

Hackers continuously shift tactics from brute-force attacks to phishing campaigns exploiting software vulnerabilities. As these threats evolve, security measures must also be dynamic and adaptable. Businesses must treat security as a fluid process, constantly changing to counter new and emerging threats effectively. This continuous adaptation is essential for staying ahead in the ever-changing landscape of cyber threats.

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The Data Value Spectrum

Not all data is created equal. Financial records, medical information, and intellectual property require the highest level of security. Less sensitive data, like movie preferences, can be protected with less stringent measures. Security needs to be tailored based on data value.

So, what's the answer?

Perhaps it's not about achieving "enough" security but adopting a proactive security posture. This posture acknowledges the inherent risks, prioritizes data based on value, and employs a multi-layered defense strategy.

The Pillars of a Proactive Security Posture

While absolute security may be a myth, building a robust security posture can significantly reduce the risk of breaches and minimize damage if one occurs. Here are the key pillars of this approach, expanded for a deeper understanding:

Defense in Depth

Imagine a castle with a moat, drawbridge, and heavily fortified walls. This layered approach is the essence of in-depth defense. It involves deploying a variety of security controls at different points within a system. Firewalls act as the first line of defense, filtering incoming and outgoing traffic. Access controls ensure that only authorized users can access specific data. Encryption scrambles data at rest and in transit, making it unreadable even if intercepted.

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This layering creates redundancy. If one control fails, others can still impede attackers. Additionally, it makes a complete breach significantly more difficult. Hackers must bypass multiple layers, considerably increasing the time and effort required for a successful attack.

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Assume Breach

Security needs a"fire drill" mentality. We must assume a breach will occur and have a well-defined incident response plan in place. This plan outlines the steps to take upon detecting a breach, such as isolating compromised systems, containing the damage, notifying authorities, and restoring affected data. A well-practiced plan minimizes downtime, data loss, and reputational damage.

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Continuous Monitoring

Security isn't a one-time fix; it's a continuous process requiring constant vigilance. This entails regularly scanning systems for vulnerabilities, updating software with the latest security patches, and educating employees about cybersecurity best practices. By continuously monitoring systems and fostering a culture of security awareness, businesses can significantly reduce the risk of successful attacks and ensure their data security remains robust and adaptive to evolving threats.

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Security by Design

Integrating security considerations into every stage of the product or system development life cycle is crucial. Security features shouldn't be an afterthought bolted onto a finished product but should be an integral part of the design and development process from the very beginning. This proactive approach ensures that security is woven into the fabric of the system, providing a more robust, more resilient defense against potential threats.

Wrapping Up

In an era where data breaches are not a matter of if but when, businesses must adopt a proactive and holistic approach to data security. The question of how much data security is enough is not about reaching an endpoint but about creating a resilient and adaptive security posture. It's about balancing cost with risk, leveraging technology while addressing the human element, and continuously evolving to meet new challenges. In the end, the right amount of security is the amount that protects your business, your customers, and your reputation in an increasingly hostile digital landscape.

Recently, a significant data exfiltration event targeting Snowflake customer databases came to light, orchestrated by a financially motivated threat actor group, UNC5537. This group successfully compromised numerous Snowflake customer instances, resulting in data theft and extortion attempts. It's important to note that Mandiant's thorough investigation found no evidence suggesting that the cyber threats originated from Snowflake's own environment. Instead, every incident was traced back to compromised customer credentials. 

In this blog post, we’ll dive into the key takeaways from Mandiant’s investigation. We’ll also share some actionable insight to bolster your data security – because staying alert and proactive is your best defense in safeguarding your organization’s data integrity.  

Key Findings 

Credential Compromise

The attacks primarily involved the use of stolen customer credentials, leading to unauthorized access and data theft. 

Threat Hunting Guidance

Mandiant provided comprehensive threat hunting queries to detect abnormal and malicious activities, which are crucial for identifying potential incidents. 

Common Attack Patterns

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‍Roles and Permissions Changes: Attackers frequently used the SHOW GRANT command to enumerate resources and adjust permissions, enabling broader access. ‍

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Abnormal Database Access: Unusual spikes in access to databases, schemas, views, and tables were noted, indicating potential reconnaissance or data exfiltration activities. 

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‍User and Query Analysis: Identifying patterns in user creation, deletion, and query frequencies helped in detecting anomalous behaviors. ‍

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Error Rate Analysis: High error rates in query executions often indicated brute force attempts or misconfigured accounts used by attackers. 

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‍High Resource Consumption: Large volumes of data queries and compression activities were linked to data staging and exfiltration efforts. 

4 Critical Recommendations to Enhance Snowflake Security 

Given these findings, it's imperative forSnowflake users to bolster their security measures. Here are some critical steps: 

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‍Implement Multi-Factor Authentication (MFA): Ensure MFA is enabled for all user accounts to prevent unauthorized access even if credentials are compromised. ‍

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Regular IAM Reviews: Conduct frequent reviews of roles and permissions to detect and mitigate any unauthorized changes. ‍

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Enhanced Monitoring: Use advanced monitoring tools such as database activity monitoring (DAM) to track abnormal access patterns, high error rates, and unusual resource consumption. ‍

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Threat Hunting Practices: Regularly perform threat hunting exercises using the guidance provided by Mandiant to stay ahead of potential issues. 

Ask Yourself these Questions  

As you reflect on the recent incidents, it’s crucial to reflect on the broader implications to your organization’s security. To ensure you are well-prepared and resilient against emerging threats, consider the following questions: 

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1. Are your current security measures sufficient to detect and prevent unauthorized access, especially from compromised credentials? 

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2. How often do you review and update your access controls and permissions? Is this easy to do for your business? 

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3. Do you have robust monitoring in place to detect unusual activities and high error rates in real-time? 

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4. What proactive threat detection strategies are you employing to identify potential issues before they cause significant damage? 

By addressing these questions and strengthening your security posture, you can better protect your Snowflake environment from similar threats. If you're looking to enhance your data security capabilities or you are not confident in your answers to the above questions, consider investing in advanced data security software purpose-built for Snowflake. ALTR’s solutions offer comprehensive protection, continuous monitoring, and proactive threat detection to safeguard your valuable data assets. 

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Would you like to explore how our data security solutions can help you secure your Snowflake environment? Contact ALTR today to learn more and schedule a demo. 

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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.

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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.

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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.

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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.  

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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.

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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.

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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.  

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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.

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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.

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‍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.

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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.

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‍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.

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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.

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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.

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.

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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.

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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.

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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:

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-       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.

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-       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.

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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.

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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.

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On June 10, 2024, cybersecurity research and response firm Mandiant published its findings on the ongoing security investigation of stolen customer data.  This news was first broken to the public about Ticketmaster and Santander Bank on May 31, 2024.

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Mandiant reports, “Mandiant’s investigation has not found any evidence to suggest that unauthorized access to Snowflake customer accounts stemmed from a breach of Snowflake's enterprise environment. Instead, every incident Mandiant responded to associated with this campaign was traced back to compromised customer credentials.”

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If there is any relief for Snowflake customers, it’s that Snowflake’s platform itself had not been compromised - which could have led to the exposure of more than 9,000+ customer data sets. Instead, Mandiant is reporting that 165 potential companies were exposed. Why is this good news? This means Snowflake is a safe platform to store and use your data. Like any other cloud-based service, you must take steps to protect your data beyond what the vendor does for you. Understanding what you can do to strengthen your defenses is crucial.

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There are many ways to understand how to approach data security in cloud-based SaaS systems. We’ll borrow Gartner’s. The above diagram breaks down the responsibilities of the customer and vendor for IaaS, PaaS and SaaS. Snowflake fits best in the SaaS pilar, and Snowflake’s nine security responsibilities for data and systems are shown in green, indicating they are unaffected by this incident. The two responsibilities in blue, People and Data, remain under the control of Snowflake’s Customers.

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Customers are responsible for what data they put in Snowflake, which users they allow access to this data, and how that access is controlled. But Snowflake does not entirely leave the People and Data responsibilities squarely on their customers. They recognize the importance of keeping data safe and have built industry-leading security and governance capabilities that they provide to customers of all sizes. From role-based access controls (RBAC) to dynamic data masking, network access restrictions, and more, Snowflake helps customers with the remaining two security responsibilities of People and Data.

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So why did this data exposure happen if Snowflake is fulfilling its responsibilities and assisting customers with theirs?  Managing People, Data, and security is challenging regardless of an organization’s size or maturity. This is where ALTR comes in.

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About ALTR

ALTR is a Data Security Platform specifically designed to help customers with their two data security responsibilities. ALTR does two things to help customers manage their People and Data: automate and scale the powerful Snowflake-provided native security and governance capabilities mentioned above and extend Snowflake’s security capabilities with Active Security measures.

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For the first part, ALTR can connect to your Snowflake, leverage data classification or Snowflake Object Tagging, and ensure that only authorized users can access data according to company policy. All this happens without writing a single line of code. Your data people don’t have to become security experts, and your security people don’t have to learn SQL. This does not replace the built-in Snowflake capabilities –it depends on them. Snowflake’s enforcement layer is still the engine for applying the advanced ALTR capabilities. This includes RBAC, dynamic data masking and row-access policies, to name a few.

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ALTR also provides detailed information and reporting for data and infosec teams to prove they follow data access compliance rules and deliver that reporting in near real-time.

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ALTR’s Active Security capabilities are used by Snowflake’s most sensitive and regulated business to ensure Snowflake is safe for PII, PHI,  and PCI information. However, these capabilities are not limited to only large or mature businesses. Active Security can help a small or young company secure one row of customer data in Snowflake.

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Active Security includes Database Activity Monitoring, Data Access Rate Limiting (Thresholding), and cell-level data protection in the form of encryption or tokenization.

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Database Activity Monitoring

Database Activity Monitoring adds near-real-time logging and alerting capabilities to Snowflake, where Snowflake logging can be delayed as much as four hours after access. ALTR can send data access logs in seconds to security teams for analysis and processing. This dramatic reduction of time is difficult to do at the scale of Snowflake but is necessary to keep the most sensitive data in Snowflake. Customers can be alerted in near-real-time, within seconds of access, to check if these accesses are valid or seem suspicious.

Data Access Rate Limiting, or Thresholding

Data Access Rate Limiting, or Thresholding, is a patent-issued feature exclusive to ALTR which can stop data access in real-time, even with valid credentials. Customers can set a policy indicating how much data a particular user can consume in a period. Once a user reaches their limit, their access to that data is blocked.

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No other data access is limited for that user, and no other users are impacted by a single user reaching their limit. Users can log in to Snowflake, but if the limit has been met for the day, no more data will flow to that user. When combined with ALTR’s Database Activity Monitoring, customers can be alerted instantly when a user has reached their limit and decide what to do with that user.

Cell-level Data Protection

Cell-level data protection takes the same type of on-disk data protection that Snowflake provides with Tri-Secret-Secure (TSS) and extends it deeper into the data. The purpose of cell-level protection with encryption or tokenization is to remove the single-party risk of Snowflake holding the data and encryption keys by adding a second (or even third) party to the equation. In this way, compromising a Snowflake user account does not necessarily mean the data can be compromised, making Snowflake safer.

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With ALTR’s tokenization or Format-Preserving Encryption Native App, the data or the keys to decrypt the data are stored outside of Snowflake. When authorized users request access to the plain text, Snowflake and ALTR interact in real-time to provide the plain text data. This operates at the scale of Snowflake and uses ALTR’s SaaS platform in the mix.

How Could ALTR Have Prevented Customer Data Exposure?

Customers should follow all recommended Snowflake security best practices for user accounts, such as multifactor authentication and network access limitations for user accounts. But sometimes that’s not enough.

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In this case, we have a simple answer to the above question of how ALTR could have helped stop or limit the exposure.

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1. Security teams were unable to see the data exfiltration in near real-time. They were limited to the default delays of up to four hours after the data had been stolen. Installing ALTR’s Database Activity Monitor into your Snowflake account and hooking up the output of ALTR’s real-time logs to your email, chat system or SIEM tool would have notified the business to investigate the user accounts immediately. “Why would someone from outside the country be accessing all our customer data at this time of night? We should investigate right away.”

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2. Cell-level protection, like ALTR’s FPE Native App, would have rendered the data access useless as the accounts likely would not have been given access to the decryption keys. ALTR’s FPE Native app is format-preserving and deploys determinism – meaning an email will still look like an email, and the protected values remain operational downstream without your users needing to decrypt and see them in plaintext. This means as the bad actors ran SELECT statements over the data, they would have received encrypted data back without receiving the encryption key. This makes data exfiltration useless and is why having a two-party system of data security is so widely used because it's effective.

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3. In the case the impacted user accounts did have access to the decryption key by compromising an elevated permission user, ALTR’s Thresholds could have been configured to do two things: alert in real-time when more than 100,000 rows have been accessed by a single user in 1hr for example, and then cut off access to that same data after 500,000 rows of data were accessed by a single user. The user would be authenticated to Snowflake and allowed to access the table, but no data would come out. That impacted user would then be in the ‘penalty box’ without the ability to decrypt information further.

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Active Security is the best way to ensure sensitive data is safe in Snowflake no matter what happens. Active Security can detect and stop a breach, not just notify you. All three Active Security features are in GA and running in production across many Snowflake user accounts today. Our product and team focus on one thing only: safeguarding sensitive data.

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.

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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.

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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.

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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.

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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:

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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.

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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.

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

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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.

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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.

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Connect with Ed on LinkedIn

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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.

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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.

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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?

2. Can your data governance processes adapt to accommodate new data sources, changing regulations, and evolving business needs?

3. Are business users encouraged to access and utilize data for decision-making?

4. Do you regularly evaluate the effectiveness of your data governance program and make adjustments as needed?

5. Are repetitive tasks like data lineage tracking and access control automated?

6. Do you track key metrics to measure the success of your data governance program?

7. Do you utilize an iterative approach with short planning, implementation, and improvement cycles?

8. Does your organization prioritize training programs to equip employees with data analysis and interpretation skills?

9. Are data governance policies and procedures clear, concise, and accessible to all relevant stakeholders?

10. 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.

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