Data Governance for Enterprise AI Agents

New Governance Requirements for AI Agents

Traditional data governance frameworks address data at rest (stored in systems) and data in motion (moved between systems). AI agents create a third state: data in context — data that enters the agent's reasoning process. This state requires governance that most organizations haven't defined yet.

Data in Context: What It Means

When an agent retrieves a customer record to answer a question, that record enters the LLM's context window. Depending on your configuration, this data may also appear in LLM provider logs, evaluation systems, or model training data. Define explicitly which data categories are permitted to enter agent context and which are not, before deploying agents that access sensitive data.

Access Governance

Agent data access should follow the same governance model as human data access, but enforced differently. A sales rep can access their accounts; the agent acting on behalf of that rep should access the same accounts — no more. Implement connection-level scoping so the agent's data access inherits the user's access rights rather than having its own broadened access.

Retention and Deletion

Define retention policies for: conversation logs (what the agent and user said), audit logs (what actions the agent took), and memory data (what the agent has stored persistently). Apply the same deletion requirements to agent data as to application data — GDPR, CCPA, and similar regulations apply.

Data Classification for Agents

Extend your data classification framework to include agent context suitability. For each data class (public, internal, confidential, restricted): can this data appear in agent context? Can it be stored in agent memory? Can it be referenced in audit logs? Define this before your agents encounter the data.