There’s a blind spot showing up in AI programs across every industry right now. Organizations deploy a Microsoft Copilot, spin up an autonomous agent to handle IT tickets, or connect an AI assistant to their CRM — and treat the whole thing as a feature rollout. The AI gets configured, users get trained, and the project gets marked as complete. What never gets asked: What identity does this agent have? What can it access? Who reviews its permissions next quarter?
That gap is where the risk lives. AI agents aren’t passive tools waiting to be clicked. They read files, write records, query databases, trigger workflows, and in some cases, approve transactions. An agent with access to your ERP or HR system is, functionally, a user — and it should be governed like one.
What It Means to Treat an Agent Like an Identity
Most organizations already have a framework for managing who can access what. Someone joins the company, gets provisioned with the right accounts and permissions, and those permissions get adjusted or removed as their role changes. That process exists because access to sensitive systems is a risk that needs to be controlled and tracked.
AI agents carry the same risk. When an agent can read your files, write to your systems, or trigger actions on behalf of your organization, it has real access with real consequences. The fact that it’s software doesn’t change the exposure — in some ways it increases it, because agents run continuously, don’t take days off, and won’t hesitate before touching something they probably shouldn’t.
The core idea is simple: if an AI system can read, write, query, trigger, approve, or transact, it should be governed the same way you govern a user or a system account. That means asking the same questions you’d ask about any employee getting access to a sensitive system — and having clear answers.
Every Agent Should Have Its Own Identity
The first question to ask about any AI agent in your environment is: what identity is it using to connect to your systems?
The answer is often uncomfortable. Many agents authenticate through a shared account, a borrowed set of credentials, or an informal integration that IT didn’t formally approve. That might work technically, but it creates a governance problem: if something goes wrong, or if you simply want to audit what the agent has accessed, you have no clean record to look at. Shared identities obscure accountability.
The better approach is to give each agent its own named identity — separate from any human user, separate from other agents, and visible to whoever manages your IT environment. That way, you know exactly what each agent is, what it can reach, and who is responsible for it. If an agent is no longer needed, you can remove its access cleanly. If it behaves unexpectedly, you can investigate it specifically.
This is one of the first things we help organizations set up when building out an AI governance program. For most of our clients running Microsoft 365, this is done through Microsoft Entra ID — Microsoft’s identity platform that’s already managing user logins and application access in their environment. Registering an AI agent in Entra gives it a formal identity that your IT team can see, manage, and control just like any other account.
Access Should Be Scoped to What the Agent Actually Needs
Having a unique identity is only the starting point. What matters just as much is what that identity is allowed to do.
The principle here — called least privilege — is that any user or system should have access to only what it needs to perform its specific function, and nothing more. It’s a standard security concept, but it’s one that’s easy to shortcut when deploying AI tools quickly.
In practice, this means being deliberate about permissions at the time an agent is set up. An agent that summarizes documents doesn’t need to edit them. An agent that answers questions about HR policies doesn’t need access to payroll records. An agent that generates reports doesn’t need to be able to send emails. Each of those extra permissions might seem harmless individually, but they represent exposure — and they add up.
Separation of duties is worth thinking about here too. If an AI agent can both initiate and complete an action — say, flagging a record for deletion and then deleting it — that’s a control gap. The same logic that prevents a single employee from both submitting and approving their own expense report applies to automated workflows. Building in a human review step, or requiring a separate system to approve what the agent initiates, closes that gap.
Agent Access Needs to Be Reviewed Over Time
Access that’s appropriate today can become inappropriate six months from now — and with AI agents, that drift tends to happen quietly.
A Copilot that was scoped narrowly for one project gets expanded when the project grows. An agent built for a workflow that was later discontinued keeps running with permissions no one thinks about. A credential that was set up during a pilot never gets cleaned up after the pilot ends. This is how over-privileged agents accumulate in an environment, and it’s one of the most common governance failures we see.
The fix is a regular review cadence. Someone with accountability should periodically look at each agent in your environment — what it has access to, whether that access still makes sense, and whether the agent is still actively needed. This doesn’t require a new process; it’s an extension of the access reviews many organizations already run for user accounts.
For organizations using Microsoft Entra ID, this kind of review is built into the platform. The same access review workflow used for human users can be applied to AI agents — reviewers get notified, confirm or revoke access, and the record is captured automatically.
Alongside access reviews, agent credentials should have expiration dates. Any key or secret an agent uses to authenticate should be rotated on a defined schedule rather than left open-ended. It’s a small operational habit that closes a persistent risk.
Where to Start
None of this requires a new platform or a new team. The governance principles — unique identities, scoped access, regular reviews — are the same ones that apply to human users. What’s needed is the discipline to apply them to AI agents with the same rigor.
A good starting point is a simple inventory: list every AI agent, copilot, or automation currently running in your environment. For each one, ask three questions. Does it have its own identity, separate from any human account? Is its access limited to what it actually needs? When was it last reviewed?
Most organizations find gaps when they go through that exercise — agents running on shared credentials, permissions that were never scoped down after a pilot, integrations that nobody currently owns. Those gaps are the roadmap. Closing them, one agent at a time, is how you build an AI program that’s genuinely accountable rather than just approved on paper.
If you’re not sure what your current AI agent footprint looks like — or how it maps to your identity governance program — we can help. We work with IT and security teams to build out AI governance frameworks grounded in the tools and infrastructure they already have. We’ll help you take stock of where things stand and what to prioritize.