The Rise of AI Agents and the Identity Strategy Gap

Read full article here: https://www.sailpoint.com/blog/governing-ai-agents-identity-security-strategy/?source=nhimg

AI agents don’t behave like humans. They don’t behave like machines. They’re something entirely new and that’s exactly the problem.

These autonomous, goal-seeking entities reason, decide, and act on their own. They spin up in minutes, run 24/7, and can make millions of decisions per hour. With access to sensitive systems and data, they don’t follow predefined workflows or wait for human direction. They execute. Relentlessly.

While human identities are onboarded through HR systems and machines follow structured provisioning rules, AI agents fall outside those lifecycles. They aren’t in your HRIS. They aren’t created by IT tickets. Yet they’re multiplying across enterprises, often unnoticed.

This is creating a crisis of speed and scale in identity governance.

Why Traditional Identity Models Break

Today’s IAM programs were built around two archetypes:

• Humans — predictable onboarding/offboarding tied to employment.
• Machines — service accounts and workloads with structured, rule-based behaviors.

AI agents break this model. They are:

• Ephemeral — spun up and torn down in seconds.
• Dynamic — adapting behavior in real time.
• Self-directed — operating across systems without continuous oversight.

Instead of lifecycle hooks, they authenticate with OAuth tokens, SSO sessions, or ephemeral API credentials—outside the reach of traditional provisioning or access review cycles.

Manual oversight can’t scale. Static RBAC roles and quarterly reviews won’t work when agents are making thousands of API calls per minute.

The New Governance Problem

Two identity questions now keep CISOs awake:

1. How do we bind an AI agent to the same entitlements as the human it represents?
2. How do we prevent humans from “piggybacking” through agents to gain access beyond their approved scope?

Without answers, organizations risk:

• Ambiguous attribution (“who actually accessed this system?”).
• Persistent, over-permissioned tokens.
• Inability to stop agents that drift off mission.
• Audit and compliance gaps.

What a New Model Requires

To secure autonomous agents, organizations must treat them as first-class identities—governed with controls tailored to their unique behaviors:

1. Governing the Entire Access Chain - Map the full path: Human → Agent → Machine → App → Data → Cloud. Visibility into each hop is mandatory.
2. Real-Time Policy Engines - Enforce contextual decisions at machine speed, not human ticket speed.
3. Short-Lived Credentials - Agents should only operate with time-boxed, narrowly scoped tokens that expire quickly.
4. Context-Aware JIT Access - Evaluate attributes (location, device, workload health, user approval) before granting elevated access.
5. Continuous Behavioral Monitoring - Detect anomalous activity in real time and terminate rogue agents before they cause impact.
6. Assigned Accountability - Every agent must have a designated human owner responsible for reviewing activity, governing access, and clean decommissioning.

The Hard Questions

Can you answer these today?

• How many AI agents are active in your environment?
• What systems and data can they access?
• Who owns each agent’s lifecycle and entitlements?
• Could you stop one instantly if it went rogue?

For most enterprises, the answer is still no.

The Agent Economy Has Arrived

This isn’t a future scenario. AI agents are already embedded in enterprise workflows—deployed by business units, SaaS platforms, and vendors. Some are governed. Most are not.

If you can’t govern it, you can’t secure it.

At SailPoint, we believe the next frontier of identity security must extend to AI agents. That means:

• Intelligent automation over manual reviews.
• Real-time enforcement instead of static controls.
• Identity models built for autonomous, dynamic actors.

The agent economy is here. The question is whether your identity program is ready to keep pace.