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The Shifting Lines Between Data Governance and Data Security

Evolving data management practices are leveraging automation to simplify governance programs and move traditional data security enforcement into the governance realm

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The Shifting Lines Between Data Governance and Data Security

Evolving data management practices are leveraging automation to simplify governance programs and move traditional data security enforcement into the governance realm

Published on May 27, 2021
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One of the greatest things about working in product marketing is the ability to study the market you live in and the industry around it, and identify not just the trends affecting us today, but where things are heading.  

In ALTR’s case, we straddle the environment between data governance and data security, an area that until recently has had some pretty distinct lines. Over the past year, we’ve seen a shifting of these lines that is giving us some important data about the way organizations are evolving their data management practices, especially around data governance and data security.

Historically, data governance has been mainly about policy: defining what data an organization needs to protect, how it should do it, who should have access to it, and more. Data security on the other hand has been about enforcement: controlling access to data; detecting, responding to and investigating potential threats; and preventing data breaches. The line here is pretty clear, but it gets a bit less clear when you dive into how you go from creating policy to actually enforcing that policy.  

To go from policy creation to implementation (and then enforcement), governance, compliance, and even security teams have needed to pass the baton to other departments, oftentimes to data engineers as they are closest to the data and can implement controls. These teams then had to translate policy, apply it, maintain it, prove their controls were working on a regular basis, and revisit this whole process when new data sources were added into the mix.

The ugly gaps

When you step back to look at it, this process has a lot of gaps. To start off, no one person or team can do the job. Instead, it requires communication around what needs to be done (a problem that could use its own article), handoffs between departments, follow ups to ensure tasks have been completed, and audits after the fact to address the ever-present risks of human error. In larger organizations, you can imagine the sheer amount of time this absorbs.  

To top this all off, there’s still the problem of threat detection and prevention. Organizations are trying to solve a seemingly simple problem today: controlling access to sensitive data at scale. However, the risks of granting unimpeded access to data are larger than ever. With new and changing privacy regulations like CCPA, you can now be fined thousands of dollars per record in the event of a data breach. In an organization with hundreds of millions of records or more, that number gets career-ending pretty quickly. Going forward, you need to control not just who can access what data. You also need to take context into account for each request, asking questions like “Why do you need it?”, “How much do you need?”, and the operational question of “How can I make this easy?”

A logical path toward simplifying data governance

The rise of governance platforms like OneTrust, Collibra, BigID, and Alation has made it easier to understand data and create governance policy. Unfortunately, a gap still exists in translating that policy into action.  

In our conversations, we see forward-thinking organizations walking the logical path toward simplifying their data governance program by automating away the steps between policy creation and implementation. This would not only make managing their governance program easier, it would save time, money, and effort by removing manual steps and the reliance on multiple departments to implement and maintain policy. Bonus points if you can unify governance and security in a single platform by being able to detect and respond to threats as well.

The good news is that ALTR has built that single platform. ALTR’s tool automatically implements and enforces policy to control access to sensitive data while detecting and responding to potential threats. By integrating ALTR with your organization’s existing governance and security tools, you can automate away the creation/implementation gap.  

I think you see where this is going, but how does it impact the relationship between data governance and data security? Well, if governance policies can be automatically applied, including who should have access to data, who owns access control?  

Automated policy enforcement means everyone wins

Data governance and data security are tightly intertwined. One creates policy, the other enforces that policy, and there’s a gray implementation area in the middle where things have traditionally been blurry—where multiple departments had to work together in a painful, manual process. With automation, we see data governance taking ownership of the implementation role, subsequently moving access control into the governance realm, and helping clear up this complicated process. With this change, the focus of data security can move to actively monitoring for and responding to threats.  

With this small shift, a huge opportunity opens up. By automating away the implementation process, everyone wins: data engineers save time, data access becomes simplified, multiple tasks prone to human error are eliminated, audits are easier to perform, and you can just plain move faster.  

Automated governance policy. Unified governance and security. Open access policies so data consumers have access to the data they need while you stay confident in its privacy, security, and risk. This is exactly what needs to happen for companies to succeed in the years to come. It’s also exactly what we’re here for.

Interested in how ALTR can help simplify your data governance program through automation? Request a demo here.

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