Key points
- Agentic AI security is the practice of authenticating, authorizing, and auditing autonomous AI agents as first-class identities — adapted for their ephemeral lifespans, delegated authority, and cross-domain execution.
- Non-human identities outnumber humans ~50:1 in the average enterprise, and 80% of IT leaders report agents acting outside expected behavior — making legacy IAM frameworks insufficient.
- The 8 core strategies span treating agents as first-class identities, just-in-time provisioning, runtime access control, Zero Trust OAuth, full action traceability, cross-cloud policy, and adopting an agent-native identity model.
What happens when your help desk copilot spins up 100 agents to solve support tickets, and one of them starts issuing refunds outside of policy?
Or when your marketing bot connects to production data to generate “insights,” without audit logs, user delegation, or any trace of why it did what it did?
These aren’t hypothetical edge cases. They’re already happening.
Agentic AI is here. And we aren’t ready.
AI agents are no longer passive tools waiting for instructions. They reason, act, and delegate, with real access, real authority, and real consequences. And while your security team is still managing joiner-mover-leaver workflows, agents are making decisions at machine speed across clouds, apps, and networks.
Here’s the truth: our identity systems were built for humans. Not this.
In this post, we’ll walk through five core strategies for securing AI agents, and introduce a new way to think about identity in the age of autonomy. These ideas are drawn from real-world deployments, frontline conversations with CISOs, and what we’ve learned helping enterprises build identity orchestration.
It’s also your first stop in our multi-part series on Agentic Identity. If your IAM stack wasn’t designed for agents — and most weren’t — this is where you start.
What is agentic AI security?
Agentic AI security is the discipline of securing autonomous AI agents by treating them as first-class identities with the same rigor, controls, and auditability as human users — but adapted for their unique attributes like ephemeral lifespans, delegated authority, and cross-domain execution. It enables organizations to authenticate, authorize, observe, and govern AI agents in real time, across hybrid and multi-cloud environments, ensuring Zero Trust enforcement and traceable accountability for every agent-initiated action.
What is an AI agent, really?
AI agents are not just chatbots or background scripts. They’re autonomous software entities designed to take action — often without human intervention.
Think of them as digital teammates that can:
- Interpret context and intent
- Make decisions in real time
- Execute tasks across systems
- Act on behalf of a user, system, or even another agent
They might schedule meetings, generate reports, submit expenses, manage infrastructure, or approve transactions. Some are based on large language models (LLMs), others are purpose-built bots embedded in enterprise platforms or CI/CD pipelines.
How are AI agents different from traditional users?
What makes AI agents different from traditional automation is their ability to reason, adapt, and delegate. They’re not just executing workflows—they’re participating in them.
And that’s where identity becomes critical.
Unlike human users or long-lived machine identities, agents are often:
- Ephemeral: Created on-demand for a specific task, then destroyed
- Delegated: Acting on behalf of someone else
- Autonomous: Making decisions without direct supervision
- Distributed: Running across clouds, on-prem systems, and disconnected environments
If an agent can take action, it needs to be governed like any other identity. That means authentication, access control, policy enforcement, and auditability. Otherwise, you’re giving system-level powers to something with no identity record, no accountability, and no traceability.
That’s why agentic identity is becoming its own category — and why the old identity playbook no longer applies.
Why does identity for agentic AI matter?
Yes, AI agents are super cool technology. But they introduce new security headaches we couldn’t have even imagined a few years ago. AI agents are quickly outnumbering human users, making traditional identity systems creaky at best and dangerous at worst.
Quick reality check: A survey from Sailpoint found that 80% of IT pros have seen AI agents act unexpectedly or perform unauthorized actions.
The risks are multiplying faster than agents
Enterprise AI adoption is outpacing governance. That’s not just a pacing issue—it’s a design flaw. According to Orca Security, non-human identities (NHIs) already outnumber humans by 50:1 in the average environment. Some analysts project that ratio will hit 80:1 within two years.
Meanwhile, 80% of IT leaders say they’ve witnessed AI agents act outside their expected behavior.
And finally, Gartner predicts that “33% of enterprise software applications will include agentic AI by 2028″. And by 2029, “80% of common customer service issues without human intervention.”
The stakes are clear. If an agent deletes data or triggers a financial transaction, who’s responsible? Can you trace the delegation? Can you prove it stayed in scope?
In most environments, the answer is no.
IAM teams are discovering they lack visibility, auditability, and control. Agents are operating without identity context — no policies, no human linkages, no session traceability. And they’re doing it across cloud, on-prem, and air-gapped environments where existing IAM tools can’t follow.
The identity gaps slowing AI adoption
Before we get into how to solve it, here’s what’s standing in the way of securing AI agents today:
- Disjointed infrastructure: Agents span clouds, edge, and on-prem, but IAM still assumes centralized cloud IDPs.
- Air-gapped environments: Agents operating in defense, manufacturing, or financial systems often have no connectivity to cloud identity services.
- Policy enforcement blind spots: There’s no consistent runtime policy layer across hybrid environments.
- Non-portable identities: Agent credentials don’t move with them across regions or platforms.
- No traceability: Agent actions can’t be reliably mapped to users, intent, or outcomes.
- Lack of orchestration: There’s no abstraction layer to unify human, app, and agent identities in real time.
Each of these gaps undermines trust, creates audit failures, and leaves AI initiatives vulnerable to both risk and regulatory exposure.
The 8 strategies to close the gap for AI agent security
The following eight strategies reflect what identity leaders are already implementing to regain control:
1. Treat agents as first-class identities
Most IAM systems treat agents like anonymous scripts or service accounts. But agents don’t just call APIs—they interpret instructions, chain decisions, and operate across boundaries.
Without identity governance, these actors become invisible threats.
2. Build identity for hybrid and disconnected environments
AI agents often operate outside the reach of cloud-based IAM—on ships, factory floors, or in financial systems with strict latency constraints. When identity stops at the cloud boundary, agents go unauthenticated or unmanaged.
Identity has to follow the agent, even when the cloud can’t.
3. Modernize provisioning with just-in-time (JIT) identity
Static service accounts don’t scale when agents spin up by the hundreds. Pre-provisioning every possible task and actor leads to credential sprawl and massive over-permissioning.
JIT provisioning gives agents scoped, ephemeral identities that match their role—and nothing more.
4. Rethink access control for runtime decision-making
Most IAM stacks enforce policy once—at login. But agents don’t log in. They act continuously, often adapting their actions mid-execution.
Access needs to be dynamic, context-aware, and enforced at runtime.
5. Extend Zero Trust to OAuth, not just users
OAuth was designed for humans. It assumes persistent sessions and user consent—not fast-moving, autonomous systems.
To support agents, we need proof-of-possession tokens, delegation chains, and real-time revocation tied to risk.
6. Establish traceability for every action and delegation
Who asked the agent to act? Was it within scope? If a task went wrong, can you trace what happened?
Without detailed logs, delegation graphs, and policy context, your ability to respond—or comply—falls apart.
7. Design identity policies that span clouds and runtimes
Agent identity shouldn’t break when crossing platforms. Yet today, credentials, tokens, and policies don’t travel well.
Your identity orchestration needs to span Azure, AWS, on-prem, and edge—not force re-authentication at every hop.
8. Go beyond NHIs; adopt an agent-native model
Agents aren’t just another category of non-human identity. They’re ephemeral, autonomous, and delegated. Managing them like long-lived apps or VMs is a mismatch.
We need a new playbook — one built for runtime actors, not static objects.
Why Identity Resilience is the foundation for securing agentic AI
AI agents are rewriting the rules of identity. They operate at machine speed, across distributed systems, with real authority and often without direct oversight. Securing them demands more than bolt-on policies or updated tokens. It requires a shift in architecture.
Ideally, you need:
- Dynamic identity provisioning for agents based on task, delegation, and context
- Zero Trust enforcement across public cloud, on-prem, and air-gapped environments
- Full traceability through logging, session chaining, and delegated authorization
- Interoperability with any IDP, agent framework, or application infrastructure
- Resilience with support for disconnected environments and failover-ready architectures
Whether your agents are enabling customer support, managing infrastructure, or making real-time financial decisions, Rubrik gives you a way to secure, monitor, and govern agent actions.
Identity Resilience is how we shift from human-first IAM to agent-native security.