Cloud Data Security Governance Without the Compromise

Balancing robust security mandates with flexible and centralized data security and access controls

As we enter what many are calling the Age of Intelligence, it seems clear that many organizations have yet to fully master what MIT-CISR Director Stephanie Woerner calls this  “industrializing data”. At Privacera, we have the privilege of working with some of the world’s largest organizations across various industries—such as fintech, gaming, and life sciences. The size of these companies, while impressive, also brings complexity, particularly when it comes to managing their vast data estates.

The shift to the cloud is unstoppable, driven by the need for agility and the promise of lower costs. Much of this shift responds to the increasing demand for data and analytics within businesses. However, there is also the need for security, privacy, and legal oversight, which often creates tension.

This is where many organizations face a significant compromise. They must answer a crucial question: How open should our data be, or conversely, how tightly locked down should it be? This article will explore these often conflicting priorities.

Understanding Cloud Data Security Governance

First, let’s define cloud data security governance. Broadly, data governance involves the people, processes, and technologies that make data useful within an organization. Data security governance, however, focuses specifically on access controls and data protection, whereas aspects like data catalogs, data quality, and business glossaries focus more on understanding and consuming data.

Traditionally, data security followed a simpler model. Data resided in a single, on-premise data warehouse, carefully curated with only the essential elements for the business. Adding new data took months, and access was tightly controlled by IT, which handled most reporting. As my friend and expert in all things data, Derek Strauss from Gavroshe articulate that the data personas at this time that were addressed were rather pedestrian. For instance, operators that live in the business (eg  e call center) need some repeatable facts with a fast response time.

The transition to the cloud changed this paradigm, introducing concepts like multi-cloud, storage, compute, and data lakes. The “data mesh” is now central to this landscape, bringing together disparate data sources into a unified framework.

To make this new, complex data ecosystem useful and trusted by the business, robust data security governance is essential. Specifically, cloud data security governance must address:

• Data discovery and classification
• Data access policies and controls, masking, and encryption
• Monitoring and auditing
• Dashboards, reports, and analysis to identify data security risks

Common Challenges in Cloud Data Security Governance

One of the biggest challenges in cloud data security governance is the sheer diversity of data sources, often spread across multiple clouds and vendors. Many organizations manage data from 15 or more sources, each with its own security and permission models. This means organizations need experts for Databricks, AWS S3, Snowflake, Redshift, and more. One company we spoke to had over 200 full-time employees managing access requests and data sharing alone.

Creating consistent security policies across all these platforms, such as those required for GDPR or CCPA, is another major hurdle. How can you ensure that the same policy is applied consistently across Snowflake, S3, and Databricks, producing the same results?

Additionally, the role of business users has evolved. Power users can now spin up clusters independently, copy data, and launch new projects without waiting for IT. This shift introduces security risks, as the security team may not know where sensitive data—such as PII or HIPAA-protected information—resides within the data mesh.

Monitoring and auditing are also significant challenges. Many security teams struggle to complete forensic investigations quickly after a breach, as it can take weeks to gather the necessary information.

These technical and operational complexities often lead organizations to define their data security posture in one of two ways:

1. Open up everything internally so everyone has access to all data—though this poses massive risks.
2. Lock everything down—which creates friction, causing delays in business processes.

After conducting over 50 prospect presentations last year, one thing was clear: neither strategy works well, and most organizations aim for a middle ground—a “Goldilocks” approach that offers the best of both worlds.

Best Practices for Cloud Data Security Governance

To achieve this balance, I recommend the following best practices:

1. Establish Clear Governance Policies

• Define roles and responsibilities, such as data stewards and security officers.
• Create a policy framework that aligns with business objectives.

Advice: A centralized, command-and-control approach is outdated. Instead, adopt models that empower the business while maintaining centralized oversight and control.

2. Leverage Centralized Tools for Discovery, Access, Masking, and Encryption

• Invest in solutions that automate cloud data security governance.

Advice: Many point solutions exist, but they can create a fragmented view of your data estate. Look for tools that provide a unified view and allow consistent policy enforcement across all data sources.

3. Continuous Monitoring and Auditing

• Ensure every access event—approved or denied—is logged.
• Conduct regular audits to identify vulnerabilities early.

Advice: Automation and centralization are critical. Cloud environments change rapidly, and without an automated system in place, you won’t be able to respond quickly to emerging risks.

4. Cloud Vendor Risk Management

• Assess cloud providers’ security postures regularly.
• Ensure SLAs cover your data security and compliance needs.

Advice: Compromised credentials remain a common entry point for breaches. Proper access provisioning is key to limiting exposure when breaches occur.

Introducing Privacera’s Unified Data Security Platform

Privacera, founded in 2016 by the creators of Apache Ranger and Apache Atlas, continues the journey from Hadoop into the world of cloud data. Our platform is built on two core premises:

1. Data security must be centralized across diverse, multi-cloud environments.
2. Security should be transparent to analytical queries to ensure scalability and availability.

Our modular platform at the core integrates the following key capabilities:

• Data discovery that can scan and classify using built-in classifiers for sensitive data types like PII, GDPR, HIPAA.
• Access control and policy management that provide a centralized way to create Role, Attribute or Tag-based policies to provide fine grained controls on top of your traditional identity management solutions.
• Data masking and encryption.
• Consolidated auditing, monitoring, reporting in a central location across all your data.

In addition, Privacera has recently released Privacera AI Governance (PAIG) that focuses on a centralized platform to deliver security and safety guardrails for generative AI (GenAI) applications that organizations might build. Our approach remains that your choice of LLM or vector databases or cloud platforms should not result in siloed security and access management constructs.

Parting Thoughts

When I joined Privacera three years ago, security teams were rarely involved in data security governance conversations. And these are not limited to the highly regulated industries either, but rather across the entire spectrum.  Today, thanks to generative AI, high-profile breaches, and evolving regulations, security teams are now key stakeholders. I’m encouraged by their demand for a unified platform to manage security across data estates.

Privacera continues to lead the way with the most comprehensive data security platform available, and with the release of PAIG, we’ve extended our reach to the emerging threats posed by generative AI. To foster a stronger community approach, we’ve decided to open-source PAIG, ensuring that data security remains at the forefront of innovation in this new era.

If you are interested in learning more about Privacera, reach out to our team and schedule a demo or conversation.