Your security team knows how many employees have access to your systems.
But do you know how many AI agents, service accounts, API keys, and automated workflows have access?
That’s becoming a much harder question in 2026.
Organizations are rapidly adopting AI agents to automate everything from software development and customer support to data analysis and security operations.
These agents don’t just generate text.
They authenticate.
They access applications.
They call APIs.
They retrieve data.
They execute workflows.
And increasingly, they make decisions and take actions without a human sitting in front of every transaction.
That creates a new cybersecurity challenge:
AI agents need identities too.
And those identities need to be secured.
The Identity Problem Is Getting Bigger
For years, identity security focused primarily on human users.
Employees had accounts.
Administrators had privileged accounts.
Contractors had temporary access.
Security teams built policies around those identities.
Now add:
- AI agents
- Service accounts
- API keys
- Machine identities
- Bots
- Automated workflows
- Cloud workloads
The number of identities inside an organization can grow far beyond its number of employees.
And unlike humans, these identities can operate 24/7.
That’s where the risk becomes interesting.
An employee might access an application a few times during the day.
An automated agent might make thousands of API calls while nobody is watching.
So the question becomes:
How do you know when an AI agent is behaving abnormally?
What Happens When an AI Agent Gets Too Much Access?
Imagine an organization deploys an AI agent to help developers.
The agent has access to:
GitHub → Cloud APIs → CI/CD → Internal Documentation → Databases
Everything works normally for months.
Then something changes.
The agent suddenly starts:
- Accessing repositories it never used before
- Calling unfamiliar APIs
- Requesting elevated permissions
- Downloading unusual amounts of data
- Communicating with an external service
- Triggering workflows outside its normal pattern
None of these events necessarily means the agent has been compromised.
But together, they should raise a question:
Is this normal behavior for this identity?
That’s where traditional identity monitoring starts becoming insufficient.
The New Security Question: What Is the Identity Doing?
Authentication tells you who or what accessed a resource.
Authorization tells you what it is allowed to access.
But security operations also need to understand:
What is it actually doing?
That’s where behavioral analytics becomes important.
Consider two scenarios.
Scenario 1
An AI agent accesses the same API it uses every day.
The request volume is normal.
The destination is expected.
The action matches its assigned workflow.
Probably normal.
Scenario 2
The same agent suddenly accesses a sensitive database, requests elevated privileges, and sends data to an unfamiliar external endpoint.
The credentials may still be valid.
The authentication may still be legitimate.
But the behavior is unusual.
That’s a security signal.
Why Traditional SIEM Visibility Isn’t Always Enough
A SIEM can collect identity events.
It can collect API logs.
It can collect endpoint telemetry.
It can collect cloud activity.
It can collect network events.
But collecting everything doesn’t automatically mean you understand what’s happening.
The real value comes from connecting those signals.
For example:
AI agent authentication
↓
Privilege change
↓
Unusual API activity
↓
Endpoint anomaly
↓
Suspicious network connection
↓
Sensitive data access
Individually, each event might look manageable.
Together, they could represent an attack chain.
This is why modern security operations increasingly need SIEM + XDR + UEBA + threat intelligence + automated response working together.
Where UEBA Fits
UEBA stands for User and Entity Behavior Analytics.
And the word “entity” matters.
Because the entity doesn’t always have to be a human.
It could be:
- A user
- A service account
- An API key
- A device
- A workload
- An AI agent
UEBA can help establish behavioral patterns and identify activity that deviates from those patterns.
For an AI agent, that could mean understanding:
Which applications does it normally access?
Which APIs does it normally call?
How frequently does it operate?
Which resources does it normally touch?
What does its normal behavior look like?
Once you understand the baseline, abnormal behavior becomes easier to identify.
This Is Where Identity Security Meets XDR
Identity security shouldn’t exist in a separate security universe.
Suppose an AI agent suddenly behaves abnormally.
The next question isn’t just:
“Is the identity suspicious?”
It’s:
“What else is happening around it?”
Maybe the endpoint associated with the workflow is communicating with a suspicious destination.
Maybe another identity was compromised.
Maybe a privilege escalation happened immediately before the unusual API calls.
Maybe threat intelligence identifies the destination as malicious.
Now identity activity becomes part of a much larger investigation.
This is where XDR can provide additional context by connecting signals across different security layers.
Where Seceon OTM Fits
This is one of the reasons Seceon’s Open Threat Management (OTM) Platform is relevant to this changing security model.
OTM brings together capabilities including:
SIEM + XDR + SOAR + UEBA + Threat Intelligence + Threat Hunting
within a unified security operations platform.
Instead of treating identity, endpoint, network, cloud and application activity as completely separate investigations, OTM is designed to correlate those signals and provide broader context.
For example:
Identity anomaly
↓
UEBA detects abnormal behavior
↓
XDR correlates endpoint and network activity
↓
Threat intelligence adds context
↓
SIEM provides the event history
↓
SOAR can automate an appropriate response
The goal isn’t simply to detect that an AI agent did something unusual.
The goal is to understand:
What happened → why it matters → what else is connected → what should happen next
That’s a much more useful security question.
AI Agents Are Also Creating a New Access-Control Problem
There’s another issue that security teams shouldn’t ignore.
AI agents need permissions.
And permissions can accumulate.
An agent might start with access to one application.
Then someone adds another integration.
Then another API.
Then another workflow.
Six months later, nobody remembers exactly why the agent has access to everything it can reach.
This is the same problem security teams have dealt with for years with human identities:
Excessive privileges.
The difference is that automated identities can operate much faster.
If an overprivileged human account is compromised, the attacker may have access to sensitive resources.
If an overprivileged AI agent is compromised or manipulated, it may be capable of taking automated actions across multiple systems.
That’s why AI agent identity governance is becoming an important part of cybersecurity.
Security Teams Need an Inventory of Non-Human Identities
You can’t protect what you don’t know exists.
A practical starting point is building visibility into:
Who has access?
What applications are they connected to?
What credentials do they use?
What permissions do they have?
When were those permissions last reviewed?
What does normal behavior look like?
And for AI agents:
What actions are they actually capable of taking?
This isn’t just an IAM problem anymore.
It’s becoming part of security operations.
What Should Security Teams Monitor?
If your organization is deploying AI agents, consider monitoring:
1. Agent Identity
Know which AI agents exist and which credentials they use.
2. Permissions
Understand what resources each agent can access.
3. API Activity
Monitor unusual API calls, destinations, and request patterns.
4. Behavioral Changes
Identify activity that differs significantly from the established baseline.
5. Privilege Escalation
Watch for unexpected changes in permissions.
6. Data Access
Monitor unusual access to sensitive information.
7. Network Activity
Correlate agent behavior with network connections and endpoint activity.
8. Response Actions
Have clear policies for what should happen when an agent behaves abnormally.
The MSSP Challenge Is Even Bigger
For an MSSP, this problem scales quickly.
One customer may have a handful of AI agents.
Another may have hundreds of automated identities.
Another may be running thousands of service accounts and API integrations.
Now imagine monitoring all of that across dozens of customers.
The MSSP needs to understand:
Which identity belongs to which customer?
Is this behavior normal for that customer?
Is the same attack pattern appearing across multiple environments?
Which incident should be investigated first?
This is where centralized, multi-tenant security operations become increasingly important.
A platform such as Seceon OTM can help MSSPs bring identity, endpoint, network, cloud and application security signals into a unified operational workflow.
The objective isn’t simply to monitor more identities.
It’s to make those identities understandable at security-operations scale.
The AI Agent Security Checklist
Before deploying an AI agent into a production environment, security teams should be able to answer:
- What identity does the agent use?
- What permissions does it have?
- Which applications can it access?
- Which APIs can it call?
- What data can it retrieve?
- Can it create or modify resources?
- Can it escalate its privileges?
- How is its behavior monitored?
- What happens if its behavior becomes abnormal?
- How quickly can its credentials or access be revoked?
If those questions don’t have clear answers, the agent may already represent an unmanaged attack surface.
The Bigger Shift in Identity Security
The traditional identity model was built around:
People → Accounts → Applications
The modern environment looks more like:
People + AI Agents + Service Accounts + APIs + Workloads → Applications + Data + Infrastructure
That’s a much bigger identity ecosystem.
And security operations need visibility across all of it.
AI agents aren’t going away.
Neither are automation, APIs, cloud workloads, or machine identities.
The organizations that adapt early will be the ones that treat these identities as first-class security entities, rather than invisible infrastructure.
FAQ: AI Agent Identity Security
What is non-human identity security?
Non-human identity security focuses on protecting machine identities such as service accounts, API keys, workloads, bots, and AI agents that authenticate and access organizational resources.
Why do AI agents need identity security?
AI agents often require credentials and permissions to access applications, APIs, data, and infrastructure. If those identities are compromised, overprivileged, or misused, the agent could potentially perform unauthorized actions.
What is AI agent identity governance?
AI agent identity governance involves managing an agent’s identity, credentials, permissions, access lifecycle, and authorized actions throughout its operational lifetime.
Can UEBA monitor AI agents?
UEBA can analyze behavior from users and other entities. In environments where AI agents and automated identities generate telemetry, behavioral analytics can help identify activity that deviates from established patterns.
How can SIEM and XDR help with AI agent security?
SIEM can centralize identity, cloud, application, endpoint, and network events. XDR can help correlate related signals across security layers, giving analysts broader context when investigating suspicious activity.
How does Seceon OTM address this problem?
Seceon OTM combines SIEM, XDR, SOAR, UEBA, threat intelligence, and threat hunting in a unified security operations platform. This allows security teams to correlate identity behavior with endpoint, network, cloud, and other security telemetry.
Is AI agent security important for MSSPs?
Yes. MSSPs managing multiple customer environments need visibility into human and non-human identities across different organizations. Centralized and multi-tenant security operations can help make that monitoring more scalable.
The New Identity Perimeter
The identity perimeter is no longer just about employees.
It includes everything that can authenticate and take action.
Users.
Service accounts.
API keys.
Workloads.
AI agents.
And the security question is evolving with it.
It’s no longer enough to ask:
“Who logged in?”
We need to ask:
“What identity is acting, what is it allowed to do, and does its behavior make sense?”
That’s where identity security, UEBA, SIEM, XDR and automated response start coming together.
And as AI agents become more common, that combination may become less of an advanced capability and more of a baseline requirement.
AI agents are becoming part of the workforce.
Their identities need to become part of the security model too.