Enterprise identity and access management has traditionally been built around people.
An employee signs in, proves their identity, receives access based on their role and uses that access to perform a task. Identity teams manage the user's permissions, security teams monitor activity and access is reviewed periodically.
That model worked reasonably well when humans were the primary actors inside an organization.
AI agents are changing that assumption.
Modern AI agents can do much more than generate text or answer questions. They can interact with applications, call APIs, retrieve information, use enterprise tools and execute multi-step workflows.
In some environments, they may operate continuously with limited human intervention.
That creates a new identity problem.
The question is no longer only "Who has access?"
Organizations also need to ask: What is the AI agent allowed to do, why is it doing it, what identity is it using, and how long should that access remain available?
NIST recently highlighted this challenge, noting that enterprises are rapidly deploying agentic AI while traditional security guardrails may not be sufficient for the new risks created by autonomous systems.
This does not mean traditional IAM is becoming irrelevant. Instead, enterprises need to extend identity security to account for a new class of digital actors.
- AI agents are not traditional service accounts
- Traditional IAM answers "who?" but agents add "why?"
- The growth of non-human identity changes the IAM landscape
- Least privilege becomes even more important
- Standing privileges create a new risk
- Human identity still matters
- IAM and security operations need to work together
- What enterprises should do now
- The future of enterprise IAM is more contextual
- Final thoughts
AI agents are not traditional service accounts
Organizations have dealt with non-human identities for years.
Service accounts, API keys, application identities and workload identities already access enterprise systems without a person sitting behind every request.
AI agents introduce an important difference.
A traditional service account generally performs a defined function. An AI agent can interpret instructions, choose tools, retrieve information and potentially decide which actions to take to achieve a goal.
That creates more variability.
For example, consider an internal AI agent designed to help a finance team.
The agent might be allowed to:
- Read financial reports
- Search an internal document repository
- Retrieve customer information
- Create reports
- Send information to another application
The security question becomes more complicated if that same agent can also modify records, approve transactions or access sensitive customer data.
Giving the agent broad permissions simply because it needs to complete its tasks can create unnecessary risk.
This is why AI agents should not simply inherit the same access model used for traditional applications.
Traditional IAM answers "who?" but agents add "why?"
Traditional IAM focuses heavily on identity and access.
A user authenticates, the system determines what resources they are allowed to access, and policy controls what they can do.
AI agents require an additional layer of context.
Imagine an agent has permission to access a customer database.
That does not necessarily mean every database operation should be allowed.
The security system may also need to understand:
- Who initiated the task?
- What is the agent trying to accomplish?
- Which application or tool is being accessed?
- What information is being requested?
- How sensitive is that information?
- Is the request consistent with the agent's normal behavior?
- How long should the permission remain active?
This moves identity security toward context-aware and intent-aware access decisions.
Recent research into agentic identity has similarly highlighted the gap between traditional IAM models and agents that operate at machine speed using non-human identities and delegated permissions.
The growth of non-human identity changes the IAM landscape
Enterprise identity teams are already dealing with an expanding number of non-human identities.
Cloud workloads, applications, APIs, automation platforms and service accounts can all require credentials.
AI agents add another category.
An organization could eventually have hundreds or thousands of agents performing different functions across SaaS applications, cloud environments and internal systems.
This creates several practical problems.
1. Identity inventory
Security teams need to know which AI agents exist.
That sounds simple, but AI systems can be created by different business units, development teams and individual employees.
An organization cannot effectively secure identities that it does not know exist.
2. Ownership
Every agent should have an accountable owner.
If an AI agent has access to sensitive information, someone needs to be responsible for approving that access and reviewing whether it remains necessary.
3. Permission management
Agents should receive only the permissions they need.
An agent that reads customer records should not automatically receive permission to delete them.
4. Credential management
Long-lived credentials create additional risk.
Where possible, organizations should consider short-lived credentials, workload identities and other mechanisms that reduce unnecessary standing access.
5. Activity monitoring
Identity management cannot stop at authentication.
Security teams need visibility into what agents actually do after receiving access.
Least privilege becomes even more important
Least privilege has always been an important security principle.
AI agents make it more important because they can operate quickly and repeatedly.
A human user might make a mistake while performing a task. An autonomous system could potentially repeat the same mistake across hundreds or thousands of records.
Consider an AI agent with access to a company's CRM.
If it only needs to update contact information, giving it permission to export the entire customer database creates unnecessary exposure.
A better model would restrict the agent to:
- Specific applications
- Specific data
- Specific actions
- Specific time periods
- Specific business purposes
This can significantly reduce the potential impact if an agent is compromised or behaves unexpectedly.
Standing privileges create a new risk
Another important consideration is how long an AI agent retains access.
Traditional environments often contain service accounts with permissions that remain active for months or years.
That model becomes increasingly uncomfortable when the identity belongs to an autonomous system.
Instead of asking only:
"Should this agent have access?"
security teams should also ask:
"Does this agent need access right now?"
Temporary or just-in-time access can reduce the amount of time an attacker or compromised agent has to abuse a credential.
It also creates a clearer relationship between the task being performed and the permissions required to perform it.
Human identity still matters
The rise of AI agents does not remove humans from the identity chain.
In many enterprise environments, an agent will still be acting on behalf of a person, team or business process.
That relationship needs to remain visible.
For example:
Employee → Approved Task → AI Agent → Application → Data
Security teams should ideally be able to trace an important action back through that chain.
If an AI agent downloads sensitive information, the organization should be able to determine which agent performed the action, what triggered it, what permissions it used and, where appropriate, which human or business process initiated the task.
This improves accountability and makes incident investigation easier.
IAM and security operations need to work together
AI-agent identity cannot be treated as an IAM-only problem.
Identity systems can establish permissions, but security operations teams need to understand what happens when those permissions are used.
This is where identity telemetry and security monitoring become increasingly connected.
An unusual sequence could look like this:
- An AI agent authenticates normally.
- It accesses an application it has previously used.
- It suddenly requests a large volume of sensitive data.
- It attempts to access another system outside its normal workflow.
- The behavior triggers a security alert.
None of the individual authentication events may appear suspicious.
The sequence could be.
That is why organizations need visibility across identity, application, cloud and security operations data.
What enterprises should do now
Organizations do not need to completely redesign their IAM architecture overnight.
A practical starting point is to establish a clear foundation.
1. Build an AI agent inventory
Identify AI agents currently operating across the organization.
Include agents created by IT, security, engineering and business teams.
2. Identify every agent's identity
Document the service accounts, API credentials, workload identities and other authentication mechanisms used by each agent.
3. Map permissions
Determine exactly what each agent can access.
Look specifically for excessive permissions and unnecessary standing privileges.
4. Assign ownership
Every production AI agent should have a clearly identified business and technical owner.
5. Reduce standing access
Where practical, use short-lived or task-specific permissions rather than permanent broad access.
6. Monitor agent behavior
Security teams should monitor authentication, data access, API calls and unusual activity associated with AI agents.
7. Connect identity to incident response
When suspicious activity occurs, security teams should be able to quickly identify the agent involved, revoke or restrict its access and investigate the associated activity.
8. Review AI agents like other privileged technologies
An AI agent connected to sensitive enterprise systems should receive the same level of security attention as other privileged identities and critical applications.
The future of enterprise IAM is more contextual
AI agents are not necessarily replacing traditional IAM.
They are exposing some of its assumptions.
For years, identity systems were largely designed around predictable human behavior. People sign in, perform tasks and sign out. Applications have defined functions. Service accounts perform relatively specific jobs.
AI agents can operate differently.
They can interpret instructions, interact with multiple systems and perform actions at machine speed.
As a result, enterprise identity management will increasingly need to understand not only who or what is requesting access, but also what the actor is trying to do, what it is allowed to do and whether that behavior remains trustworthy at the time of the action.
The organizations that address this early will be in a stronger position to adopt AI without turning autonomous systems into uncontrolled privileged identities.
The goal should not be to slow down AI adoption.
It should be to make sure that every AI agent has an appropriate identity, limited access, accountable ownership and enough monitoring to keep its actions within acceptable boundaries.
Final thoughts
AI agents represent an important change in the enterprise technology landscape.
They can improve productivity and automate complex workflows, but their ability to access systems and act autonomously introduces a new dimension to identity security.
Traditional IAM principles such as authentication, authorization and least privilege remain essential. The difference is that organizations now need to apply those principles to a growing population of machine-driven actors.
The next stage of IAM will therefore be less about simply controlling logins and more about controlling digital identities, permissions, intent and behavior across the enterprise.
Also read: Threat Intelligence in the Age of AI Starts With What You Already Have explores why security teams should prioritize actionable intelligence, repeatable processes, and effective automation before adopting agentic AI in their security operations.