Governing Agentic AI at Enterprise Speed

The business and technical framework for enterprise leaders deploying AI agents with control built into the data layer.

When an AI agent acts inside your enterprise, the expectation is that it stays within approved boundaries and that someone can account for what it does. Most organizations have no reliable way to verify either.

Only 30% of organizations have reached maturity in agentic AI governance.

The framework you need for closing that gap starts where governance has to live: at the data layer.

“Enforcement at the data layer is what makes governance real. Define policies at the business layer and an agent in motion will never see them. The organizations building this architecture now are laying the foundation for every agentic AI deployment that comes after.”

– Rob Feldman, CLO, EDB

The 3-phase roadmap for agentic AI governance at the data layer

1. FOUNDATION

Start with low-risk, read-only deployments where data is well understood and mistakes are recoverable. The goal is to establish the governance architecture before you scale, with policy enforcement built into the data layer from the start.

2. EXPANSION

Expand to workflow automation and document generation, introducing write actions with reversibility built in. Governance validates intent dynamically at each action, checking that what the agent does aligns with its defined purpose.

3. SCALE

Deploy to customer-facing agents and systems of record with financial or regulatory impact. Anomaly detection flags behavioral drift as it happens, and full session reconstruction is available for any action that needs investigating. At this phase, governance holds end-to-end.

The conversation behind the framework

Priyanka Jain, VP, Product Management – Data & AI Governance, and Rob Feldman, Chief Legal Officer, discuss what the incoming agentic workforce demands from enterprise governance.

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