AI Data Governance: Governing Enterprise Data in the Age of AI
AI changes the access pattern around enterprise data. Models, copilots and agents can become new identities that read, transform or transmit information at machine speed, often across systems that were originally designed around human users and conventional applications.
What is AI data governance?
AI data governance is the application of data governance principles and controls to the data that AI systems can access, use, transform, share or generate. The core governance questions remain familiar; the actor set is broader and the pace of activity can be much higher.
Why traditional governance can be insufficient on its own
- AI systems may access multiple data sources through service identities
- Prompts and context can combine data from different sensitivity classes
- External model providers may become destinations for enterprise data
- Autonomous agents can take actions rather than only return information
- Permissions granted for experimentation can persist after the original need has ended
- Traditional inventories may not record AI systems as governed actors
Keep data at the centre
AI governance should not create a completely separate universe of controls. The organisation still needs to know what data exists, how sensitive it is, who owns it, which policy applies and which identities can reach it. AI systems should be added to that same access and policy model.
Useful controls
- Register AI systems and their owning teams
- Map AI identities to the data they can access
- Restrict sensitive data from unapproved external services
- Require approval for higher-risk data access or movement
- Monitor observed AI access where telemetry exists
- Record policy evaluations and resulting decisions
- Review and remove unnecessary access
AI agents raise the control requirement
An assistant that only generates text creates one class of risk. An agent that can retrieve data, call tools and modify systems introduces another. Governance then needs to cover both data access and the actions the agent is allowed to take after receiving that data.
How Sentinel approaches AI data access
Sentinel's product direction treats AI systems as governed actors within the wider enterprise data graph. That makes it possible to ask the same questions of an AI identity as a human or service identity: what can it access, which policy applies, whether the access is appropriate and what governed change should happen if it is not.
Related reading: Data Access Governance, AI Agent Security and Sentinel.