OneTrust introduces its approach to delivering insights quickly while ensuring compliance
Blair Hutchinson
Principal Product Manager
May 8, 2025
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.
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.
Data governance professionals find themselves in an impossible position:
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.
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:
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.
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.
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.
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.
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.
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 data needed for this AI model includes:
Without policy enforcement, the data governance team faces numerous challenges:
With OneTrust's Data Policy Enforcement capability, the governance team defines policies that are automatically applied whenever the data is accessed:
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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