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Empower data governance teams with policy enforcement

OneTrust introduces its approach to delivering insights quickly while ensuring compliance

Blair Hutchinson
Principal Product Manager
May 8, 2025

Two businessmen chatting at a balcony railing

OneTrust is on a mission to accelerate the responsible use of data. We're working with companies that want to scale their use of AI, analytics, and data sharing while respecting individual privacy and ensuring compliance with evolving regulations. This balance — maximizing data value while minimizing risk — has become increasingly challenging as data volumes grow and regulatory landscapes become more complex. 

The traditional approach to governance has focused on documentation and manual processes. We are changing that paradigm with programmatic policy enforcement that makes governance actionable at the point of data use. 

 

Why data governance is failing

Most organizations have invested heavily in cataloging assets, documenting policies, and building governance frameworks. But these investments often result in "governance as documentation" rather than "governance as implementation." The gap between documented policies and actual data usage creates significant risk. 

Data teams face competing pressures: they need to deliver insights quickly while ensuring compliance with a growing web of regulations and internal policies. When governance exists primarily as documentation, disconnected from data workflows, it creates friction that either slows innovation or encourages workarounds. 

The result? Organizations face a critical choice: enforce governance and slow innovation or accelerate data use and accept increased risk. Neither option is sustainable. 

 

The challenges facing data governance teams

Data governance professionals find themselves in an impossible position: 

  1. Implementation gaps: Policies defined by governance councils rarely translate to technical controls.
  2. Limited visibility: Once data access is granted, there's little insight into how data is used 
  3. Scale challenges: Manual review processes can't keep pace with AI-scale data use. 
  4. Technical barriers: Governance teams lack the technical means to enforce controls at the query and processing layer.
  5. Organizational friction: Governance teams are seen as the "department of no" undermines governance adoption.

The most concerning reality is that governance teams often have no practical way to enforce their policies at the point of data use. They can document what should happen but lack the technical means to ensure orchestrate consistent enforcement.

 

Empowering data governance professionals 

Policy enforcement bridges the gap between governance intent and operational execution. It transforms static documentation into automated, real-time enforcement where the data is accessed. OneTrust’s approach brings together multiple contexts — business, regulatory, consent, and data — so teams can make enforcement decisions faster. Policies can be managed centrally, unifying privacy, consent, and compliance policies within one platform, that can be enforced across an organization’s entire data estate. This leads to:

  • Automated enforcement: Policies are translated into technical controls that are applied based on changes in the data
  • Developer-friendly: Enforcement occurs without disrupting existing workflows 
  • Governance-defined: Governance teams remain in control of policy creation while automation ensures consistent implementation 
  • Context-rich governance: Enforcement leverages business, regulatory, consent, and data contexts to ensure precision and compliance

For governance professionals, this means that the policies they define are enforced automatically. They gain visibility into how policies are being enforced and can measure policy effectiveness across the organization, allowing them to focus on policy design rather than manual enforcement. Policy enforcement turns governance into a business enabler, allowing organizations to move fast without breaking trust. 

 

How policy enforcement works  

OneTrust’s Data Policy Enforcement product integrates directly with modern data platforms. The policy enforcement engine programmatically applies native controls to each platform. For example, Snowflake leverages Snowflake's external functions and row access policies, while Databricks integrates through Unity Catalog's access control and table ACLs. 

 

Screenshot of the Data Governance violations summary page

Unmasked personal identifyers found in the Violations Summary

 

First, the data must be classified and the metadata made available in OneTrust. If you haven’t already done this, OneTrust’s Data Discovery includes an in-depth library of classifiers for structured and unstructured data. Governance teams can flag violations — in most cases where sensitive data does not have the necessary technical controls in place. 

 

Screenshot of the Data Governance create and apply policy action screen

Creating and applying a policy with user-defined rules

 

To enforce a policy, a person will define the rules using human-readable language, such as “Mask this column with ****** for all user groups except for HR_MANAGERS, SYSADMIN, ACCOUNTADMIN”. Human-readable policies are then translated into platform-specific technical controls that are applied directly to the data assets in the system. The result is that individuals and agents who query the data have the appropriate fine-grain access based on who they are.

 

Screenshot of the Data Governance resolved violations screen

Summary of resolved violations

 

Enforcement is added to the immutable audit log, and data governance teams can manage the definition of enforcement in the context of the policy it derives from.

 

Real-world example: AI model development with responsible data use

Consider a data science team developing a customer churn prediction model using sensitive customer data across multiple sources. The team needs to build an accurate model while complying with GDPR, CCPA, and internal data ethics policies. 

 

The challenge 

The data needed for this AI model includes: 

  • Customer demographics with personal data 
  • Transaction history with financial details 
  • Customer service interactions, including verbatim comments 
  • Website behavioral data, including consent flags for marketing purposes 

Without policy enforcement, the data governance team faces numerous challenges: 

  • Manual reviews of data access requests create week-long bottlenecks 
  • Inconsistent application of masking rules across different data platforms 
  • Inability to enforce consent preferences in real-time as data is accessed 
  • No audit trail linking data usage to specific modeling purposes 

 

How policy enforcement transforms the process

With OneTrust's Data Policy Enforcement capability, the governance team defines policies that are automatically applied whenever the data is accessed: 

  1. Consent-based row filtering: When the data scientist queries customer data, rows are automatically filtered based on consent status.
  2. Column masking: Sensitive personal data is automatically masked based on the user's role and declared purpose.
  3. Purpose-based access: The governance team sets policies based on the declared purpose of "churn prediction model development".
  4. Cross-platform consistency: Whether data resides in Snowflake, Databricks, or through a BI tool, the same policies apply consistently. 

This way, the data science team can iterate quickly on their AI model while maintaining compliance with complex regulatory requirements. Governance becomes an enabler of innovation rather than a bottleneck. 

 


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