Agent Identity and Least Privilege: Governing What Your AI Can Do

For years, identity and access management was about two kinds of actor: people, and the services they run. AI agents are now a third kind, and they do not fit neatly into either box. An agent acts with initiative like a person, runs continuously like a service, and can be talked into doing things neither would. Most organisations are deploying agents faster than they are governing them.

The fix is not to slow down, but to extend a discipline we already understand. An AI agent that can take actions in your systems is an identity, and it deserves to be treated like one: with its own credentials, its own permissions, and its own accountability.

Why agents need their own identity

A common shortcut is to let an agent run under a human's account or a shared service credential. It is convenient, and it is a mistake. If the agent acts under a person's identity, you cannot tell agent actions from human ones in your logs, you cannot scope its permissions separately, and you cannot revoke its access without disrupting the person. Giving each agent its own identity is the foundation everything else rests on.

There is a maturity angle too. The moment agents have their own identities, all the tooling your organisation already trusts for people, provisioning, reviews, revocation, reporting, starts to apply to them. You are not inventing a parallel security world for AI; you are extending the one you already have. That is both less work and far easier to reason about when something eventually goes wrong.

Least privilege, applied to agents

An agent should be able to do exactly what its job requires and nothing more. This is the oldest principle in security and the most powerful lever you have with agents specifically, because an agent can be manipulated through prompt injection or simply make a mistake. If it lacks the permission to do something dangerous, neither an attacker nor an error can make it happen. Scope credentials narrowly, per agent and per task, and resist the temptation to hand over broad access just because it is easier.

A helpful exercise is to write down, for each agent, the smallest set of actions it needs to do its job, then grant exactly that and nothing more. It feels restrictive at first and almost always turns out to be enough. When the agent genuinely needs more later, you add it deliberately, with a reason recorded, rather than discovering after an incident that it could do far more than anyone intended.

Lifecycle management

Human accounts are created, reviewed and eventually deactivated. Agents need the same lifecycle, and it is easy to forget because nobody leaves the company to prompt the cleanup. An agent spun up for a project can linger for months with live credentials and no owner. Treat agents as joiners and leavers: provision them deliberately, review their access periodically, and decommission them and their credentials the moment they are no longer needed.

Ownership is the part teams forget most often. Every agent should have a named human or team responsible for it, the way every service has an owner. Without that, agents accumulate quietly, nobody is quite sure what they still do, and decommissioning feels risky because no one can say what might break. A clear owner turns the whole lifecycle into someone's actual job.

Monitoring behaviour, not just access

With people, you mostly care who can access what. With agents, you also care what they actually do, because their behaviour can drift, be hijacked, or simply surprise you. Behavioural monitoring, watching the pattern of an agent's actions and flagging the anomalous ones, catches problems that permission checks alone will miss. An agent that suddenly starts touching systems it never used before is worth a closer look, even if every individual action was technically allowed.

This matters more for agents than for people precisely because agents act quickly and at scale. A person misusing access does so at human speed; a hijacked or malfunctioning agent can take the same wrong action hundreds of times in minutes. Watching behaviour, and being ready to pause an agent that starts acting out of character, is what keeps a small problem from becoming a large one.

Audit logs and approvals

Two more controls turn governance from an aspiration into a practice.

• Audit logs: record what every agent did, when, and with which credentials, so any action can be traced and explained.
• Approvals for sensitive actions: require a human sign-off before an agent does anything high-stakes or irreversible.

Together these give you both hindsight and a brake. Logs let you reconstruct what happened after the fact; approval gates stop the most damaging actions from happening unsupervised in the first place.

The combination is what regulators, customers and your own future self will eventually ask for. When someone needs to know what an agent did last Tuesday and on whose authority, a clear log and a record of approvals turns an anxious investigation into a quick lookup.

Agent identity governance is arriving

This is not a niche concern for much longer. In 2026, agent identity governance is emerging as a recognised category inside identity and access management, as the same vendors and teams who manage human and service identities turn their attention to agents. Getting ahead of it now, by treating your agents as first-class identities from the start, means you will not be retrofitting governance onto a sprawl of ungoverned agents later.

You do not need to wait for a product to buy. The principles here, distinct identities, least privilege, lifecycle, monitoring, audit and approvals, can be applied today with the tools you already run. Adopting them early simply means that when the category matures, you are refining a system you already trust rather than scrambling to impose order on a mess after the fact.

Building agents that are both powerful and well-governed, with scoped identities, least privilege and the right human checks, is central to how we design AI agents for production. An agent you can trust with real work is one you have also designed to be controlled.