Quick answer: Why does AI agent security matter?
AI agent security matters because AI agents can act through service accounts, APIs, tokens, secrets, certificates, and automation workflows that already have access to business systems.
Many of those identities don’t belong to a person. They may not show up in a normal login report, but they can still move data, update records, trigger workflows, connect systems, and use privilege.
To secure AI agents, security teams need to know what identities exist, who owns them, what they can access, and whether that access should still be allowed.
The risk starts when an AI agent can act before anyone owns the access
An AI agent doesn’t need a badge, a desk, or a normal workday to create access risk.
Picture a support agent that checks customer records, opens tickets, updates account notes, and calls an internal API. The work looks useful. The business wants it live quickly.
Then someone asks a simple question: What identity is the agent using?
That’s usually where the questions start.
The agent may be using a service account created months ago. That account may have broader access than the agent needs. The API token may never expire. The secret may sit inside a pipeline no one reviews.
And the owner may be “the platform team,” which usually means no single person owns it.
This is the problem behind the identity that never logs in. The identity may not look like a user, but it can still act with privilege.
Webinar takeaway: You can’t govern what you can’t see
This article is based on Segura®’s webinar, The Identity That Never Logs In: Securing Identities in the Age of AI Agents, featuring Joseph Carson, Advisory CISO and Chief Evangelist at Segura®, and Christian Bartels, IAM Expert at Elimity.
Their main point was clear:
“You can’t govern what you can’t see.”
That line hits the real issue. AI agents are arriving in environments where non-human identities are already hard to track.
The scale is already bigger than most teams can manage manually. GitGuardian reported 29 million secrets detected in public GitHub repositories in 2025. IBM reported that 97% of organizations with an AI-related breach lacked proper AI access controls.
The takeaway: organizations need to build AI agent security into their AI initiatives from the start by understanding the identities, credentials, and privileges that power each agent.
Follow the access path behind the AI agent
The AI agent may be the visible part of the workflow. The real risk often sits in the identity, credential, token, or permission path behind it.
When security teams follow that path, the question changes from “Is this AI tool approved?” to “What access is this agent using, who owns it, and can we prove what happened?”
AI agents use identities that security teams already struggle to manage
Most organizations already have non-human identities everywhere: service accounts, API keys, secrets, certificates, bots, scripts, workloads, and automation pipelines. They keep systems running in the background, but they also create access that’s easy to miss.
The problem gets bigger when AI agents start using these identities to do work on behalf of people.
An agent may summarize records, update a ticket, run a workflow, query a database, or send a request to another system. Behind each action is an identity, credential, token, or permission path.
If the service account behind the agent can access customer records, financial data, and administrative functions, the agent can potentially reach all of them too.
If no one is responsible for that account, excessive permissions can remain in place for months or years without review. And if security teams don’t know the account exists, they won’t see the risk until something goes wrong.
The access behind AI agents determines their security impact
AI agent security can sound abstract until you picture what happens inside a normal business process.
A DevOps team connects an AI assistant to help investigate failed deployments. The assistant can read logs, check pipeline status, review configuration files, and suggest fixes.
That sounds useful, but the assistant is connected through a service account that can also read production secrets, restart jobs, and change deployment settings. The agent doesn’t need all of that access to do the task.
Now the risk is much bigger than a helpful assistant making a bad suggestion. The risk is an autonomous workflow with access to sensitive systems, using an identity that may have too much privilege and too little oversight.
Where AI agent access risk shows up
These examples involve different teams and tools, but the access question is the same: What identity is the agent using, what can it reach, and who can prove what happened?
That’s why “secure the AI tool” isn’t enough. Security teams need to secure the access path behind the tool.
Attackers go after tokens, sessions, secrets, and overprivileged accounts
Attackers don’t always need to break into a system the hard way. Sometimes they can use a valid identity, a stolen token, a leaked secret, or an active session and walk right in.
As became clear during the webinar:
"They don't hack anymore, they log in."
That’s especially dangerous with non-human identities because they often have three traits attackers love:
- They run quietly.
- They’re hard to tie back to a person.
- They often keep access longer than they should.
A stale service account from an old project may still connect to production. An API token may still work after the contractor who created it is gone. A secret may sit inside a repository, ticket, or build log longer than anyone realizes.
AI agents can add another layer to this problem. If an agent can reach sensitive data, trigger workflows, or call internal APIs, attackers may not need to compromise the agent itself. They may only need the identity the agent uses.
That’s why security teams should look closely at the credentials, tokens, sessions, and service accounts behind AI-driven workflows.
Shadow AI creates shadow identities
Shadow IT has been around for years.
It usually starts with good intentions. A team needs to move quickly, so they adopt a tool before security has time to review it. Maybe it begins with a free trial, a browser extension, or a purchase made on a corporate card.
Shadow AI is the next version of that same problem. The difference is that AI tools often connect directly to data, applications, and workflows, which means the access risk can grow much faster.
A business team connects an AI tool to a shared drive. A developer connects an AI assistant to a repository. A support team connects an AI workflow to customer tickets. A marketing team connects an automation tool to campaign data.
Most teams aren’t thinking about access risk when they connect a new AI tool. They’re trying to solve a problem, save time, or automate a task.
The challenge is everything that gets connected in the background.
Every new AI workflow may create or reuse identities, tokens, API keys, OAuth grants, secrets, and permissions. Some are approved. Some aren’t. Some are visible to IAM and security teams. Some slip through the cracks.
That’s how shadow AI turns into shadow identities.
Identity visibility comes before AI agent control
Security teams can’t reduce AI agent risk if they don’t know what exists. It sounds basic, but it’s where many programs get stuck.
A team may know its critical applications. It may know its cloud accounts. It may know its main identity provider. But when someone asks for a full list of service accounts, API keys, secrets, certificates, automation identities, and AI-connected workflows, the answer often takes time.
During an incident, that delay can cost the team time it doesn’t have.
If an incident happens, the team needs to answer practical questions fast:
- Which identity was used?
- What did it access?
- Who owns it?
- Was the access expected?
- Can we revoke it without breaking production?
Those questions shouldn’t require four tools, three Slack threads, and someone who “might remember why that account exists.”
At this scale, spreadsheets and annual reviews won’t be enough. Teams need current identity data they can use to prioritize risk, prove control, and act quickly.
AI agent security starts with a clean map of human and non-human identities, especially the ones tied to privilege.
How to reduce AI agent access risk
The goal is to make sure AI-driven workflows operate with clear, governed, and well-understood access.
A practical AI agent security model should do five things.
1. Find the identities behind AI activity
Start by identifying the accounts, tokens, API keys, secrets, certificates, OAuth grants, and service accounts connected to AI tools and automation workflows.
Don’t stop at the AI application. Follow the access path into cloud platforms, SaaS tools, ticketing systems, repositories, databases, pipelines, and internal APIs.
2. Assign ownership
Every non-human identity needs a real owner.
“DevOps” isn’t enough. “Security” isn’t enough. “Nobody touches it because it might break something” is a risk signal.
A named owner should know what the identity does, why it exists, what it can access, and when it should be reviewed or removed.
3. Define the agent’s scope before it acts
AI agents use the context and access they’re given. If an agent can reach more systems, data, or actions than the task requires, it may use that access in ways the business didn’t intend.
Before an AI agent goes live, teams should define what it can access, what it can do, where it can operate, and which actions should be blocked by default.
The safest model is to give the agent a narrow job, a narrow access path, and clear boundaries before it starts taking action.
4. Limit privilege to the task
AI agents should only have the access needed for the job they’re doing.
A support agent may need to read a ticket and suggest a response. It probably doesn’t need broad access to customer exports, admin settings, or payment data.
A DevOps assistant may need to read deployment logs. It probably doesn’t need persistent access to production secrets.
Least privilege becomes even more important when an identity can act quickly and repeatedly.
5. Use just-in-time access where possible
Persistent privilege gives attackers more time and more room to move. Just-in-time access helps reduce that window.
Instead of letting an identity keep broad access all the time, teams can grant access for a specific task, at a specific time, with approval and monitoring where needed.
That matters for human administrators, and it matters for non-human identities too.
6. Monitor what privileged identities actually do
Access reviews show what an identity could do. Monitoring shows what it actually did.
If an AI-connected identity starts making unusual API calls, touching new systems, accessing sensitive data, or acting outside its expected pattern, security teams need to know quickly.
Visibility has to cover behavior alongside permissions.
Start with the AI-connected identities that could do the most damage
The fastest way to make progress is to start with the identities that could cause the most damage.
Begin with non-human identities that can access production systems, sensitive data, secrets, customer records, payment systems, cloud administration, source code, security tools, or identity platforms.
Then ask:
- Can activity be monitored and tied back to a business purpose?
- Can access be granted just in time instead of standing access?
- Can the credential, token, or secret be rotated or removed?
- Is the privilege broader than the task requires?
- Is the access still needed?
- What does it access?
- Who owns this identity?
This doesn’t need to start as a huge transformation program.
Start with the crown jewels. Find the identities that touch them. Then reduce the access that doesn’t need to be there.
What good AI agent security helps teams prove
A stronger AI agent security model gives teams answers they can use.
It should help security and identity teams see which AI workflows exist, which identities they use, what those identities can access, who owns them, and whether activity matches the expected task.
It should also help teams remove stale access, rotate credentials, reduce standing privilege, monitor privileged activity, and keep evidence for audits or investigations. It’s the day-to-day work that actually reduces risk.
No one wants to slow down every AI project with heavy processes. But no CISO wants an AI workflow running through an overprivileged service account that no one reviewed either.
AI can move fast, but it shouldn’t run on access no one owns.
Watch the full webinar
This article is based on Segura®’s webinar, The Identity That Never Logs In: Securing Identities in the Age of AI Agents, featuring Joseph Carson, Advisory CISO and Chief Evangelist at Segura®, and Christian Bartels, IAM Expert at Elimity.
Watch the full recording to hear the conversation behind these takeaways, including why identity visibility comes first, how attackers target sessions and tokens, and what security teams can do to govern AI agents and non-human identities in real environments.
See how Segura® PAM helps control AI agent and non-human identity risk
The challenges covered in this article are exactly where privileged access control becomes critical: visibility into privileged identities, just-in-time access, credential governance, secrets management, and protection against the misuse of tokens and service accounts.
Segura® PAM helps security teams bring human and non-human identities under stronger control with continuous visibility, least privilege policies, credential protection, session control, and clear records of privileged activity.
As AI agents become part of everyday work, security teams need a way to keep up with the identities behind them. That means knowing what exists, what it can access, who owns it, and what it actually did.
AI agent security starts with visibility. From there, teams can reduce privilege, remove stale access, monitor risky activity, and bring the identities that never log in back under control.
[ Learn More About Segura® PAM ]
FAQ: AI agent security and non-human identities
What is AI agent security?
AI agent security is the practice of controlling what AI agents can access, what actions they can take, which identities they use, and how their activity is monitored.
For security teams, the main concern is privileged access. If an AI agent can use a service account, API key, token, secret, or automation workflow, that access needs the same level of visibility and control as other high-risk identities.
What are non-human identities?
Non-human identities are digital identities used by systems instead of people.
They include service accounts, API keys, secrets, certificates, bots, scripts, workloads, automation pipelines, cloud resources, and AI agents.
Why are AI agents a privileged access risk?
AI agents become a privileged access risk when they can take action through identities with broad, persistent, or poorly monitored access.
If an agent uses an overprivileged service account, stolen token, or unmanaged secret, it may be able to access more systems or data than the task requires.
How can teams secure AI agents?
Teams can secure AI agents by finding the identities behind AI activity, assigning ownership, limiting access to the task, using just-in-time access where possible, rotating or removing stale credentials, and monitoring privileged activity.
The first step is visibility. Teams need to know what exists before they can control it.
How does least privilege apply to AI agents?
Least privilege means an AI agent should only have the access it needs for a specific task.
For example, an agent that summarizes support tickets shouldn’t have admin access to the full customer database. An agent that reviews deployment logs shouldn’t have standing access to production secrets.
How does Segura® help with AI agent security?
Segura® helps teams control the privileged access behind AI agents and non-human identities.
The platform supports discovery, credential vaulting, secrets management, session control, least privilege, just-in-time access, and audit-ready records of privileged activity.
