AI agents are no longer just chatbots that answer questions. Increasingly, they log in to systems, call APIs, read files, send messages, update records and complete multi-step tasks on behalf of a person or a company. A travel agent books a trip, a coding agent changes code, a support agent refunds an order, and a finance agent reconciles invoices. The work is real, the systems are real, and the consequences are real.
That creates a security question that many organisations have not yet answered properly: when an agent acts, whose identity is it using? If the answer is a human employee’s username and password, or a powerful shared service account, the organisation has a problem. The agent may be helpful, but from the system’s point of view it is indistinguishable from the person. This article explains why agents need their own identity and access control, and how to set that up in a practical way.
The Rise of Agents That Act for Us
Earlier software tools mostly waited for a human to click a button. An AI agent is different because it is given a goal and allowed to choose the steps to reach it. It may plan a sequence of actions, use several tools, check results and try again if something fails. That autonomy is what makes agents useful, but it also makes them powerful. An agent that can read a mailbox, search a customer database and send payments is not a passive assistant; it is an actor inside your systems.
Agents also work at a scale and speed that humans do not. One agent can process hundreds of requests in the time a person handles a few, and several agents can run in parallel around the clock. That is a benefit for productivity, but it means a mistake, a confusing instruction or a compromised agent can spread much further, much faster, than a single human error. Access control is therefore not an optional extra for agents. It is the main control that limits what an agent can do when it goes wrong.
Why Giving an Agent a Human’s Credentials Is Dangerous
The quickest way to give an agent access is to hand it a person’s login, an API key belonging to an employee, or a shared administrator account. It works in a demo, but it creates three serious problems. First, there is no accountability. If an agent and its owner share the same credentials, logs will show the person’s name even when the person was asleep. You cannot reliably answer the basic audit question: who, or what, did this?
Second, the access is almost always far too wide. A human employee may need broad access because their job changes from day to day. An agent built for one task does not. When it inherits a person’s full permissions, it can read data, change settings and contact customers that its task never required. Third, revocation becomes messy. If the agent is retired, or the employee leaves, or the credentials leak, you cannot simply disable the agent without affecting the person, or disable the person without breaking the agent. Shared credentials tie two very different things together, and that is exactly what identity management is supposed to avoid.
What Agent Identity Actually Means
Agent identity means that each agent gets its own identity, separate from any human. It has its own account or service identity, its own credentials, and its own set of permissions. Systems should be able to see, in a log, that a specific agent performed a specific action, on behalf of a specific owner, for a specific task. The agent is not pretending to be a person; it is acting openly as an agent, under rules set for that agent.
This is the same idea that already applies to service accounts and automated systems, extended to a new kind of actor. The difference is that agents are more numerous, more flexible and harder to predict than traditional automation. A script does the same steps every time. An agent may choose different tools depending on the situation, so its permissions need to be defined by what it is allowed to touch, not just by a fixed list of steps. Its identity is the anchor for all of those controls: permission, logging, approval and revocation.
Key Principles for Agent Access Control
Least privilege. Give each agent only the tools and data its task needs, and nothing more. If an agent only reads support tickets, it should not be able to delete customers or change billing settings. Start narrow and expand only when there is a clear reason.
Short-lived credentials. Avoid permanent keys that sit in code or configuration files for years. Prefer credentials that expire quickly and are renewed automatically when the agent is still authorised. If a short-lived credential leaks, its useful life is limited, and the damage is contained.
Full audit logging. Log every meaningful agent action: what it accessed, what it changed, what it sent, and when. Keep those logs somewhere the agent itself cannot edit or delete. Review them regularly, and make sure a human can reconstruct what an agent did during an incident.
A human owner. Every agent should have a named human owner who is accountable for it. The owner approves its permissions, reviews its activity and decides when it should be stopped. An agent without an owner is an orphan with access, and orphans are hard to govern.
Approval for sensitive actions. Not every action should be automatic. Sending money, deleting data, sharing information outside the company, or changing security settings should require a human approval step, or at least a clear, pre-approved limit. The agent can prepare the action, but a person confirms the risky ones.
Instant revocation. When a task ends, a project closes, or something looks wrong, you should be able to disable an agent immediately, without disabling a human or breaking unrelated systems. If revoking an agent is difficult, the identity design is wrong.
How This Differs From Normal User IAM
Traditional identity and access management was designed around people. A person logs in, proves who they are, often with multi-factor authentication, works for a while and logs out. Agents break several of those assumptions, and the differences should be stated plainly. Agents act at machine speed and in parallel, so a single bad decision can repeat hundreds of times before a human notices. They cannot do multi-factor authentication like a human, because there is no person present to approve a prompt for every action. They do not take holidays, get tired or ask for clarification in the same way a colleague might.
That does not mean agents should be trusted less in every case, but it does mean the controls must be different. Instead of relying on a login prompt, you rely on the agent’s own identity, tightly scoped permissions, short-lived credentials and strong logging. Instead of trusting a session because a human authenticated once, you check continuously what the agent is doing and whether it is still within its approved scope. The goal is the same as traditional IAM, to make sure the right actor has the right access for the right reason, but the mechanisms have to fit a non-human actor.
The Risks If Agent Identity Is Ignored
An agent given a person’s full access becomes, in effect, the perfect insider if it is misused or hijacked. It already has legitimate access, it works quickly, and it does not look suspicious in the same way an outside attacker might. A prompt injection, a manipulated document, a leaked key or a simple configuration error can turn a helpful agent into a route to sensitive data, unauthorised changes or large numbers of unwanted transactions.
There is also a quieter risk: loss of trust in your own records. If you cannot tell human actions from agent actions in your logs, incident response becomes guesswork, compliance reporting becomes unreliable, and customers or regulators will reasonably ask how you know what really happened. Good agent identity protects the organisation twice: it limits what an agent can do, and it preserves evidence about what it actually did.
A Starter Checklist for Your First Agents
- List every agent, what it is for, who owns it and which systems it can touch.
- Give each agent its own identity and credentials. Never share a human login.
- Scope permissions to the specific task, and remove access that is not needed.
- Use short-lived credentials wherever possible, and store them securely.
- Turn on detailed logging for agent actions, and protect those logs from tampering.
- Add a human approval step for payments, deletions, external sharing and security changes.
- Set an expiry or review date for each agent, and revoke it promptly when its task ends.
- Test revocation before you need it in a real incident, so you know it works.
AI agents will become a normal part of how work gets done, in the cloud and elsewhere. The organisations that handle them well will not be the ones with the most powerful agents. They will be the ones that can answer, for every agent, three simple questions: who is it, what is it allowed to do, and who is responsible for it? Build that answer into identity and access control from the start, and agents become a tool you can trust rather than a risk you cannot explain.
Frequently Asked Questions
What is AI agent identity?
AI agent identity is a separate identity given to an AI agent, with its own credentials and permissions, so the agent acts under its own name rather than borrowing a human’s login. It makes agent actions visible, controllable and auditable.
Why should an agent not use a human’s credentials?
Using a human’s credentials removes accountability, because logs cannot distinguish the agent from the person, and it usually gives the agent far more access than its task requires. It also makes revocation difficult, since disabling the credentials affects the human as well as the agent.
Can an AI agent use multi-factor authentication?
Not in the same way a human can, because there is no person present to approve each login. That is why agent security relies more on a dedicated identity, scoped permissions, short-lived credentials and strong audit logging than on a one-time login prompt.
What is least privilege for AI agents?
Least privilege means giving an agent only the tools, data and permissions needed for its specific task. If an agent only needs to read certain records, it should not be able to edit, delete or share them, and it should not have access to unrelated systems.
How do you revoke an AI agent’s access?
You disable or delete the agent’s own identity and credentials, which stops that agent immediately without affecting any human user. Good agent identity design makes this a quick, tested action, and it should be done as soon as the agent’s task ends or if its behaviour looks suspicious.
