Agent Security Is an Identity Problem (Not an AI Problem)
TL;DR
AI agents add a new execution layer that uses access at machine speed. Small permission mistakes turn into big risk fast.
The most common early failure is delegation implemented as impersonation, which destroys accountability.
The practical path forward: human ownership + separation of authority from execution + dynamic guardrails (JIT, least privilege, purpose-based access).
AI agents are already landing in real workflows. They triage tickets, open pull requests, run tests, push code through CI/CD, move data between SaaS apps, and even trigger other automations.
That’s the upside. It’s also the risk.
Most organizations are trying to govern agents with an IAM model built for two identity types:
Humans (imperfect, but accountable)
Machines/NHIs (service accounts and keys that behave predictably)
Agentic AI breaks that second assumption. Agents adapt. They retry. They chain actions together. They run nonstop. And they execute intent at machine speed.
So this isn’t mainly an “AI safety” debate. It’s an identity and access security problem, and more specifically an authorization governance problem. Authentication tells you who something is. Authorization decides what it can do across your environment.
If you lead IAM, security, or GRC, the goal is simple: capture the productivity gains without losing accountability.
Here’s a practical, opinionated way to do it.
The uncomfortable truth: agents change how access gets used
In traditional IAM programs, access decisions matter occasionally. Someone gets a role. A group membership changes. A service account receives a permission. That access gets used here and there.
Agents change the math. The same permission can be exercised hundreds of times in minutes.
That shift rewrites the risk model. Excess access stops being theoretical. It becomes a risk multiplier because agents repeat actions quickly and reliably. They also don’t bring human brakes to the workflow. They don’t pause because something feels off. They don’t apply judgment or ethics. They pursue the objective they were given.
So the question isn’t whether you should use agents. You probably will.
The real question is whether you can govern agent access in a way that stays auditable, safe, and still fast.
What breaks first: old IAM assumptions don’t hold up
Most teams stumble early because they focus on the wrong thing.
They focus on authentication (“Can this agent log in?”) instead of authorization (“What should it be allowed to do, under what constraints, with what evidence?”).
Once an attacker (or a misconfigured automation) gets past authentication, authorization becomes the keys to the castle. Agents then amplify the impact by using those keys constantly and at scale.
The biggest failure mode: delegation implemented as impersonation
In the near term, most companies will use delegated agents: agents that act on behalf of humans. That’s normal. It can also be safe.
The mistake is turning delegation into impersonation:
Sharing human credentials with an agent
Letting the agent inherit standing human access
Assigning a broad human role just to avoid friction
Do that, and accountability evaporates. You won’t be able to answer basic questions with confidence:
Who approved this access?
Who owns the outcome?
What controls limited blast radius?
If you want governance that stands up to scrutiny, you can’t let agent work become human shadow access.
Delegated vs autonomous agents: one distinction that clarifies everything
A simple split helps teams stay grounded.
Delegated agents (today’s phase)
The human remains the source of authority
The agent executes human intent
The governance goal is clear: preserve accountability and prevent impersonation
Autonomous agents (next phase)
The agent acts toward objectives with limited human involvement
Risk rises because failures become systematic, not occasional
Governance shifts to boundaries, stop conditions, and escalation paths
The practical takeaway: earn your way into autonomy. If you can’t govern delegation cleanly, autonomy will break you.
A practical framework: authority, execution, guardrails
If you want this to work in the real world, you need a model your team can reuse. Use it in design reviews, access requests, and audit prep.
1) Anchor governance in humans: ownership + business purpose
Every agent needs:
A named human owner (accountability)
A clear business purpose (why it exists, what it’s for)
An approval context (what it can do by default, what requires review)
Ownership sounds basic, but it’s where many programs die. If nobody owns the agent, nobody owns the exceptions. Controls drift. Reviews get skipped. Risk piles up quietly.
Make it concrete: create a lightweight “agent registration” process. If an agent can touch production or sensitive data, it shouldn’t exist without an owner and purpose.
2) Separate authority from execution: delegate without impersonating
Agents can derive authority from humans. They should not become humans.
That means:
The agent has its own identity
Access is granted via explicit delegation (tokens, scoped roles, policy constraints)
The agent does not inherit standing human permissions
This is the difference between “human intent, machine execution” and “untraceable access sprawl.”
If you do one thing this quarter: ban agent impersonation for any workflow that touches sensitive systems.
3) Replace static access with dynamic guardrails
Static access becomes dangerous when the executor never sleeps.
Guardrails need to be dynamic:
Just-in-time access (time-boxed permissions)
Least privilege (scoped to the task)
Purpose-based access (tied to workflow intent)
Context constraints (environment, resource scope, approved windows)
Instead of “the agent can deploy code,” you want “the agent can deploy code only for these repos, during these windows, after these checks.”
A real example: an agent that ships code from Zendesk tickets
Here’s a workflow many teams want:
The agent monitors Zendesk
It identifies P1 issues
It pulls relevant code
It runs tests
It deploys through CI/CD
This can be a big productivity win. It can also be a huge risk if you treat the agent like a permanent admin.
The difference is guardrails.
A safer pattern looks like this:
A named senior engineer authorizes the agent (not a generic team)
The agent acts only on tickets explicitly assigned to that engineer
Deployments run only during approved windows
The agent is restricted to specific repos and services
It uses short-lived credentials and scoped access to each system involved (Zendesk, GitHub, CI/CD)
You still get speed. You also get evidence, boundaries, and a cleaner audit story.
The change-management trap: owners move, agents persist
Even strong governance designs can fail in the real world.
People change roles. People leave. Reorgs happen. Meanwhile, automations keep running.
That’s why ownership alone isn’t enough. You need dynamic controls that remain safe when people and org charts change.
Practical mitigations:
Use JIT and tight time windows for high-risk actions (prod deploys, financial systems, admin roles)
Auto-expire delegated grants by default
Trigger ownership re-attestation when the owner changes roles or exits
Default to safe behavior when ownership becomes unclear (pause sensitive actions, require re-approval)
You don’t need perfect data to start. You need a system that stays safe when the data is imperfect.
Authorization is the battleground (and audits will arrive sooner than you think)
The first pressure probably won’t be abstract litigation. It will be familiar governance scrutiny:
SOX compliance
user access reviews (UARs)
audit evidence requirements
cyber insurance questionnaires
Boards and auditors will ask the obvious question: What can your agents access, and who approved it?
If your answer is “it runs as a developer” or “it uses a shared service account,” you’ll struggle to defend accountability.
That’s why authorization matters more than ever. It defines blast radius.
A 9–12 month plan IAM teams can execute
You don’t need a moonshot. You need a phased rollout you can actually run.
Phase 1: control delegated agents (start now)
Inventory agents and where they run (SaaS agents, internal automations, agent-to-agent triggers)
Require named ownership + purpose for every agent
Eliminate impersonation patterns
Apply JIT/time-boxing to the most sensitive actions first
Pick 2–3 crown-jewel workflows and harden them end-to-end
Phase 2: earn the right to autonomy
Expand autonomy only after guardrails are proven in production
Define stop conditions and escalation paths (when does a human step in?)
Track exceptions and reduce them over time
A few simple metrics help you steer:
% of agent actions that are time-bound (JIT)
% of agents with named owners and purpose statements
mean time to revoke agent access
policy exception rate
Conclusion: don’t let productivity erase accountability
Agentic AI changes execution speed. That alone forces a new access-control mindset.
To capture the upside without chaos, anchor your program on three decisions:
Human ownership is mandatory
Delegation is not impersonation
Guardrails must be dynamic (JIT, least privilege, purpose-based access)
Do that, and you can give your board, your auditors, and your engineering teams the same confident answer:
“We’re moving fast, and we’re still in control.”
