The physician and psychoanalyst John Lilly once said, “our only security is our ability to change.” While this adage is true in every facet of life, it is especially pertinent in business. Failure to change allows competitors to find faster, better, smarter, and newer ways of doing things – all of which lead to improved outcomes.
The same can be said for any modern data security strategy. Relying on static methods in the face of increasingly complex systems – and threats – poses a very real risk. Adaptability is key to security. In this blog, we’ll explore why an adaptive approach to data governance is essential for future-proof data protection.
What is Adaptive Data Governance?
Adaptive data governance is a dynamic, responsive approach to managing and protecting data assets. In today’s fast-paced environment, adaptive data governance allows you to keep pace with constantly evolving technologies, compliance laws and regulations, and business needs that may impact your data resources and/or users.
Putting adaptive data governance into action requires automated, scalable policy management, continuous data monitoring, and the flexibility to make adjustments on the fly. Prioritizing these capabilities helps facilitate efficient data security and access while maintaining auditable compliance.
Use Cases for Adaptive Data Governance
How exactly do organizations leverage adaptive data governance? Although there are many potential use cases, these are some of the most common:
Regulatory Compliance Management
A survey of 700+ data professionals found that 75% are subject to 10 or more data regulations. With these regulatory requirements constantly being amended or introduced, it’s clear that a static, one-size-fits-all approach to governance cannot feasibly keep up with the pace of change.
Adaptive data governance enables you with the flexibility to incorporate new and evolving regulatory standards by providing transparency into which policies are applied where, and encouraging regular assessments of existing policies to ensure they are up-to-date and compliant.
Secure Cloud Migration
As organizations move more workloads to the cloud, they often adopt multiple best-of-breed technologies in order to reap the cloud’s full potential. This, however, leads to one of the biggest and most common cloud migration challenges – establishing clear, consistent governance across each platform.
A static approach would require you to replicate the same controls within each, which is tedious and error-prone. An adaptive approach to data governance prioritizes centralizing data access controls and dynamically enforcing them across all of your solutions, including those that you have yet to onboard.
Data Analytics & AI
Data analytics has long been an integral business function and priority for data teams, and a surge in the development of AI and LLMs has only added to the importance of this use case. The rapid pace at which AI and analytics tools are advancing, as well as AI risks that are not yet well understood, underscore the need for training data to be used ethically and compliantly.
An acceptable use policy is an important first step in managing how data is used for analytics and AI. Then, implementing an adaptive data governance model helps ensure that organizational policies are effectively enforced, monitored, and able to keep up with ever-changing data or model dynamics.
Data Sharing & Collaboration
Sharing data – whether internally, externally, or via data exchange platforms – is a necessity for modern organizations. But it’s a practice that introduces risks of overexposure and unauthorized access, and it’s inherently fluid – the people, purposes, and platforms involved in data sharing constantly change. Static approaches to governing who can access and use shared data will only slow down operations and hinder insights.
Adaptive data governance simplifies secure data sharing by allowing you to adjust access permissions ad hoc, and monitor how data is used. Granular methods like attribute-based access control and purpose-based access control grant or restrict access based on various factors, including intended purpose, making them both highly scalable and adaptable.
Benefits of Adaptive Data Governance
The common use cases for adaptive data governance highlight its main benefits:
- Ease of Compliance – the ability to adapt to changing data compliance regulations is a core competency for any organization, but particularly those doing business across regions and/or with aspirations for long term growth.
- Greater Data Utilization – consistent policy enforcement and the ability to securely share data means more data can be used for more purposes, increasing its value and business impact.
- Improved Platform ROI – eliminating the need to manually author, enforce, and monitor governance processes across platforms accelerates new platform onboarding and adoption, ultimately unlocking a greater ROI.
How to Build an Adaptive Data Governance Framework
As its name and use case examples suggest, adaptive data governance is not the same for every organization – nor should it be. Gartner’s analysis of governance models reinforced this point, as illustrated in the chart below.
What does it take to build an adaptive data governance framework for the modern data stack?
Prioritize Your Business Needs
The first step in creating a data governance framework that’s easily adaptable is to determine what exactly needs to be governed. What internal and external rules are you subject to? Which tools comprise your data ecosystem? Who needs to access data throughout your organization? These and similar questions will help you prioritize the needs that your governance framework must address.
Be sure to involve cross-functional stakeholders in this step, including representatives from the data platform, security, governance, legal, and analytics teams. This will ensure that your governance model adheres to your organization’s holistic requirements, not just those of specific teams.
Understand Your Data
Once you know your needs, it’s critical to know your data – after all, the policies you put in place must adequately protect these assets. Instead of manually combing your data sources to identify, classify, and tag sensitive information, automating sensitive data discovery is a better approach for adaptive data governance frameworks. This eliminates the need for human intervention on each new data source or table that is added to your ecosystem, making the data discovery process less burdensome, slow, and error-prone.
Implement Dynamic Policies
An adaptive approach to governance requires policies that are equally adaptable. Organizations that move from RBAC to ABAC are better able to make adaptive data governance a reality because of the scalability and granularity that an ABAC policy delivers. Authoring attribute-based policies that are dynamically enforced at query runtime frees you from having to create new roles or manually update policies anytime a change occurs. As a result, access control is both adaptable and resilient, without causing role explosion or management headaches.
For a five-minute look at how attribute-based access control works, check out this video:
Continuously Monitor & Assess
With the average cost of a data breach now well over $4M, it’s never been more important to mitigate risks as much as possible. While adaptive data governance is meant to help ensure data is accessed securely and compliantly, it should not be seen as a set-it-and-forget-it function. The best frameworks are those that prioritize constant monitoring and assessment of how well processes and policies are working.
Incorporating always-on data monitoring into your ecosystem can help improve insider risk management and data security posture by allowing you to proactively identify and remediate potential threats. The ability to understand user behavior analytics and generate a data audit trail simplifies compliance, while helping to pinpoint where your policies are working and where there are gaps or weaknesses.
Next Steps
Today’s rapidly evolving data ecosystems, regulatory environment, and cyber threat landscape underscore the need for a data governance model that can adapt and scale, without hindering data access. With the right capabilities, including data discovery, security, and monitoring, an adaptive data governance framework can help unlock various business-driving use cases, from compliance and data sharing to AI and analytics.
A dynamic data security platform helps consolidate all these features into a single location. But having a blueprint for how to align and deploy stakeholders around building your framework can seamlessly put the process in motion. Check out our playbook on How to Design and Implement a Governance, Risk, and Compliance Framework to get started.