How to Secure and Govern AI Agent Identities in Modern Enterprises

Read full article from Okta here:  https://www.okta.com/blog/ai/how-do-you-govern-an-ai-agent-identity-and-why-it-matters/?utm_source=nhimg

AI is evolving at an unprecedented pace. OpenAI has transformed ChatGPT into a platform for autonomous agents. Anthropic released Claude Sonnet 4.5, capable of reasoning across multi-hour tasks. Google’s Gemini can navigate the web like a human, and Microsoft’s Copilot ecosystem now operates as a network of embedded agents across Windows and Office.

These announcements signal a turning point: AI is no longer just reactive, it’s becoming an active participant in work, systems, and workflows.

Unlike humans, AI agents don’t clock out, forget, or expire automatically. They can persist quietly in your environment, retaining access long after their tasks are complete. Without proper governance, these identities operate unseen, and in security, what’s unseen is vulnerable.

Why Traditional Identity Systems Fall Short for AI Agents

Identity infrastructure was built for humans: employees, contractors, and partners. AI agents introduce challenges that don’t fit the traditional model.

The risk: Ungoverned AI agents lead to shadow access, compliance gaps, and accountability issues. Access exists—but no one monitors it, and everyone is ultimately responsible.

Core Principles for Governing AI Agent Identities

To effectively manage AI agents, organizations need visibility, accountability, and control. A strong AI identity security playbook ensures every agent is:

1. Known – Every AI identity in your environment is inventoried.
2. Owned – A human is responsible for each agent’s actions and access.
3. Scoped – Permissions are restricted to the agent’s purpose and timeframe.
4. Auditable – Actions and outputs are logged and traceable.
5. Revocable – Access ends automatically when the agent’s task completes.

Identity governance now extends beyond humans to the AI acting on their behalf.

Common Use Cases and Governance Goals

Despite varying applications, the fundamentals of AI agent identity security remain the same:

Secure AI Agents Using a Maturity Model

Organizations can assess and improve their AI identity governance with a structured AI Agent Security Maturity Model.

Step 1: Inventory AI Identities - Catalog all AI agents, models, and automations that interact with sensitive systems.

Step 2: Assign Human Owners - Map each AI identity to a responsible person or team accountable for its behavior and access.

Step 3: Apply Least Privilege - Grant access only for the agent’s specific purpose, ideally using just-in-time tokens or time-bound credentials.

Step 4: Include AI in Access Reviews - Treat AI accounts like human accounts: regularly review and certify their permissions.

Step 5: Automate Lifecycle Management - Provision, update, and deprovision AI identities using automated workflows to maintain consistent governance.

Governance is continuous. The earlier you implement these practices, the easier it becomes to maintain control as AI scales.

Why Governing AI Agent Identity Matters

Proper governance ensures:

• Security: Minimize risks of rogue or persistent agents accessing sensitive systems.
• Compliance: Meet regulatory requirements for auditing and access control.
• Accountability: Assign responsibility for every AI action.
• Operational Efficiency: Reduce errors, orphaned accounts, and over-permissioned AI.

As AI adoption grows, governance transforms from optional best practice to business-critical necessity.