Enterprise identity governance has evolved significantly over the past two decades. We've moved from simple username/password systems to sophisticated identity and access management (IAM) platforms, implemented least privilege principles, and built mature governance processes around access reviews and certifications.
But a new challenge is emerging that threatens to make all of this progress insufficient: AI agents. These autonomous systems don't fit the mental models we've built for identity governance, and organizations that don't adapt will face growing security and compliance risks.
The Identity Categories We Know
Traditional identity governance recognizes two main categories:
Human Identities
- Employees with user accounts
- Contractors with time-limited access
- Partners accessing shared systems
- Customers using applications
Human identities are predictable: they join, move within, and leave organizations. They work during certain hours, from certain locations, on certain tasks. Access can be right-sized based on job roles. Behavior can be baselined and monitored.
Service Accounts (Machine Identities)
- Applications accessing databases
- Scripts running scheduled jobs
- CI/CD pipelines deploying code
- Integrations connecting systems
Service accounts are deterministic: they do exactly what they're programmed to do. Permissions can be scoped precisely because the required access is known in advance. Behavior is essentially static – the same account does the same things on the same schedule.
The Third Category: Agentic Identities
AI agents don't fit neatly into either category. They have characteristics of both:
Like humans, agents make decisions. They have goals and choose actions to achieve them. Their behavior varies based on context and instructions. They may access different resources based on what they're trying to accomplish.
Like service accounts, agents are software. They operate without direct human oversight. They can work continuously at machine speed. They use API credentials rather than interactive logins.
But agents also have unique characteristics:
Autonomous Decision-Making: Unlike scripts that follow code, agents interpret goals and choose actions. An agent told to "resolve customer complaints" might decide to issue refunds, escalate issues, or modify account settings based on its understanding of the situation.
Unpredictable Behavior: Because agents make decisions, their exact actions aren't predetermined. The same agent with the same permissions might take different actions in different contexts.
Context-Dependent Access Needs: Agents might need different access depending on their current task, not just their role. The access needed to handle one customer request might differ from another.
Scale and Speed: Agents can make thousands of decisions per hour, each potentially involving access to resources. This velocity exceeds human-scale governance processes.
Why Traditional Governance Fails
Role-Based Access Doesn't Fit
Traditional IAM uses roles to grant permissions: a "Database Admin" role gets database admin permissions. This works when job functions map cleanly to permission sets.
But what role does an AI agent have? An agent that helps with customer service might need to read customer records, update tickets, send emails, and occasionally escalate to finance. Is it a "customer service" role? A hybrid role? Something new?
And the access an agent needs varies by task. When handling a refund, it needs payment system access. When updating contact info, it doesn't. Static roles don't capture this dynamism.
Periodic Reviews Don't Scale
Identity governance typically includes periodic access reviews: quarterly or annually, managers certify that users' access is still appropriate. This is challenging enough for human accounts – but it's fundamentally broken for AI agents.
An AI agent might be deployed on Tuesday and need completely different access by Friday based on how it's being used. Quarterly reviews are meaningless for entities that evolve weekly.
Moreover, the scale of AI agent activity makes periodic review impractical. If an agent makes thousands of access decisions daily, what does a quarterly review even examine? The aggregate permissions? Random samples of actual access? Neither provides meaningful governance.
Ownership Is Unclear
Every user account has an owner: the user themselves, and their manager. Every service account should have an owner: the team responsible for the application.
But who owns an AI agent? The team that deployed it? The vendor that provides the model? The users who give it instructions? The data it was trained on?
This ambiguity creates accountability gaps. When something goes wrong with an agent, there's no clear responsible party. When access needs review, there's no defined owner to certify it.
Audit Trails Are Insufficient
Traditional audit logs capture who did what: "User X accessed file Y at time Z." This provides accountability and investigation capability.
But for AI agents, knowing what actions occurred is only part of the picture. We also need to know:
- Why did the agent take this action?
- What goal was it pursuing?
- What instructions led to this decision?
- Was this action appropriate given the context?
Standard logs don't capture intent, making it difficult to determine whether agent behavior was legitimate or problematic.
The Governance Gaps
These limitations create specific governance gaps:
Privilege Creep
Without role-based constraints, AI agents tend to accumulate permissions. Teams add access to make the agent work for new use cases. Unlike human accounts that go through periodic review, agent permissions keep growing.
Over time, agents end up with far more access than any single human would have – precisely because they're handling tasks that would normally be distributed across many humans.
Shadow AI
Teams deploy AI agents without going through formal provisioning processes. A developer connects a coding assistant. Marketing sets up a content generation tool. Sales integrates an email automation agent.
These Shadow AI deployments create untracked identities with unknown access. Security teams can't protect what they don't know exists.
Inconsistent Controls
Different teams apply different standards to their AI agents. One team might use short-lived credentials while another embeds long-lived API keys in code. One team might log all agent activity while another has minimal visibility.
Without enterprise-wide governance, agent security becomes inconsistent and gaps proliferate.
Compliance Failures
Regulations increasingly require controls over automated decision-making. GDPR requires explanations for automated decisions affecting individuals. The EU AI Act mandates governance for high-risk AI systems. Industry regulations require audit trails and access controls.
Traditional governance processes don't produce the documentation and controls that AI agent compliance requires. Organizations face regulatory risk they may not even recognize.
Adapting Governance for AI Agents
Addressing these challenges requires evolving identity governance:
Agent-Aware Identity Categories
Identity governance must explicitly recognize AI agents as a distinct identity type with specific requirements:
- Unique identities for each agent (not shared with users or other agents)
- Metadata capturing purpose, owner, and intended scope
- Lifecycle management from provisioning through retirement
- Integration with existing identity infrastructure
Intent-Based Access Control
Move beyond static permissions to access control that considers what the agent is trying to accomplish:
- Agents declare intent when requesting access
- Policies evaluate whether the intent is appropriate
- Access is scoped to the specific task and context
- Escalation occurs when intent is unclear or sensitive
Continuous Governance
Replace periodic reviews with continuous governance:
- Real-time monitoring of agent activity
- Automated detection of policy violations
- Continuous comparison against behavioral baselines
- Immediate alerting on anomalies
Just-in-Time Provisioning
Eliminate standing access in favor of just-in-time credentials:
- Credentials issued for specific tasks
- Automatic expiration when tasks complete
- No accumulation of persistent access
- Reduced blast radius from compromised credentials
Comprehensive Audit Trails
Capture the information needed to understand agent behavior:
- What action was taken
- What was the intended goal
- What triggered the action
- What context was available
- What was the outcome
Clear Ownership
Establish accountability for every AI agent:
- Designated human owner responsible for governance
- Clear escalation paths for issues
- Regular attestation of appropriate configuration
- Transfer of ownership when teams change
The Path Forward
Organizations can begin adapting their governance now:
Inventory Existing Agents: Discover what AI agents already exist in your environment. You likely have more than you think.
Assess Governance Gaps: Evaluate how well current governance processes cover AI agents. Where are the blind spots?
Extend Policies: Update identity governance policies to explicitly address AI agents. Don't assume existing policies apply.
Implement Agent-Specific Controls: Deploy access controls, monitoring, and audit capabilities designed for AI agent characteristics.
Train Teams: Ensure identity governance teams understand AI agents and their unique requirements.
Prepare for Regulation: Anticipate compliance requirements for AI systems and build capabilities now.
Conclusion
AI agents represent a fundamental shift in enterprise identity. They're not human users, and they're not traditional automation. They occupy a new space that demands new governance approaches.
Organizations that recognize this and adapt will be positioned to harness AI safely. Those that try to force AI agents into existing governance models will face growing risk, compliance failures, and security incidents.
The challenge is significant, but it's also an opportunity: to build identity governance that's ready for an AI-powered future. The time to start is now, while AI agent deployment is still in its early stages. The alternative is playing catch-up with an ever-growing population of ungoverned autonomous systems.
