Who Is Responsible When an AI Agent Does Something It Was Allowed to Do?

AI agent security conversations often begin with unauthorized access. An agent receives excessive permissions, a credential is compromised, or an attacker manipulates an agent into accessing a system it should never have been able to reach. These are important risks, and identity security controls such as least privilege, access governance, and continuous monitoring can help reduce them.

But there is another scenario that is harder to address: what happens when an AI agent is properly authenticated, has an appropriate permission, and still takes the wrong action?

Consider a customer service agent that has permission to issue refunds. The organization intentionally granted that permission because issuing refunds is part of the agent's job. A customer requests a refund, the agent evaluates the request, and it issues $5,000 when $500 would have been appropriate. There was no compromised credential or unauthorized access. The agent used a legitimate permission, but its decision did not align with the organization's intent.

Challenges like this brought forward by AI sprawl are growing at an alarming pace. This has led research firms like Gartner to emphasize the importance of establishing clear identity data layers to secure AI, including their recent report, Establish an Identity Data Layer to Reduce Technical Debt and Secure AI. This report, which mentions Linx Security as an identity hygeine vendor, highlights key recommendations and analysis for this ever-changing environment.

As AI agents move from retrieving information to independently executing business processes, organizations need to govern more than whether an agent has permission to perform an operation. They also need controls for determining when an authorized action can proceed autonomously, when additional context or authorization is required, and who remains accountable for the outcome.

This changes the agentic identity governance conversation. The challenge is no longer limited to giving an agent the right access. Organizations also need to understand how that access can be exercised, what authority is effectively being delegated to the agent, and where additional controls need to exist between authorization and execution.

AI agents introduce two different access control problems

Not every AI agent governance problem is the same. One useful way to separate them is to distinguish between access granularity and judgment within authorized access.

An access granularity problem occurs when an organization cannot scope a permission as narrowly as the agent's business purpose requires. For example, an agent may need to analyze Salesforce account data for the North American market. Ideally, it would receive read access only to North American accounts. If the application's permission model only supports read access to all account records or no access at all, however, the organization may have to grant broader access than the agent actually needs to perform its job.

This is a familiar least privilege problem, but agentic systems can make its consequences more significant. A human employee with broader-than-necessary read access still has to intentionally navigate to data and use it. An autonomous agent may search, retrieve, combine, and act on information at machine speed as part of completing a task. The underlying access problem is not new, but the scale at which that access can be exercised is.

Organizations should still address this through the fundamentals of access governance: use the narrowest available permissions, scope credentials and resources where possible, limit standing access, establish ownership, and revisit permissions as an agent's purpose changes. Where an application's authorization model cannot match the organization's desired policy, that gap itself should be understood as a source of risk rather than assuming that the minimum technically available permission is necessarily appropriate.

A judgment problem is different because the permission itself may be correct.

Return to the refund example. Suppose $5,000 refunds are legitimate in certain circumstances. The agent genuinely needs the ability to issue them. Imposing a $500 permission limit would reduce risk, but it would also prevent the agent from completing legitimate tasks. The problem occurs when the agent uses an appropriate capability incorrectly in a specific situation.

OWASP makes a useful distinction in its Excessive Agency guidance between excessive functionality, excessive permissions, and excessive autonomy. Excessive autonomy becomes relevant when an agent can perform consequential actions without sufficient independent verification or approval.

That distinction matters because these two problems require different controls. Better access granularity and least privilege can reduce unnecessary permissions. They cannot, by themselves, determine whether every legitimate use of an appropriate permission is correct.

For security teams, the question therefore changes from whether the agent is authorized to perform an operation to whether that authorized operation should proceed under the current circumstances.

Authorization does not eliminate the need for judgment

Access controls define the operations an identity is authorized to perform against particular resources. For AI agents, those boundaries remain fundamental. An agent should not have access to applications, data, tools, or operations that are unnecessary for its defined purpose.

The harder problem begins inside those boundaries.

Whether a $5,000 refund is appropriate might depend on the original transaction, the reason for the refund, the customer's history, previous interactions, company policy, or other business context. The permission establishes that the agent is authorized to issue a refund of that size. It does not necessarily establish that this particular refund should proceed autonomously.

This is why organizations need to define levels of autonomy alongside permissions.

Not every action requires the same level of control. Reading a product catalog may carry little risk. Updating a customer record may require additional policy checks. A large financial transaction, deletion of sensitive data, modification of security settings, or another consequential action may require stronger authorization or human approval.

The appropriate control can also change based on context. A refund below a certain threshold might normally execute automatically, but a combination of unusual transaction history, a new customer, or other risk signals could trigger additional review. The agent still has the same underlying permission. What changes is whether the organization is willing to allow that permission to be exercised autonomously in that situation.

This creates an important separation between permission and execution. Permission determines the boundary within which an agent can operate. Runtime policy can determine whether a particular use of that permission is allowed to proceed, requires another authorization step, or should be blocked.

OWASP's guidance recommends independent authorization for consequential actions rather than relying on the agent itself to determine whether the action should proceed. This is particularly relevant for agents because the same system making a decision should not always be the only system determining whether that decision is acceptable.

The goal is not to put a person in front of every agent action. Doing so would undermine much of the operational value organizations hope to gain from autonomous systems. The objective is to establish clear boundaries around what decisions can safely be delegated and where the potential impact warrants an additional control.

That also means autonomy should not be treated as a binary choice between fully autonomous and human-controlled. Organizations can establish different levels of autonomy based on the agent, resource, action, and context. Routine actions can execute automatically. Higher-risk actions can require additional verification. Particularly sensitive actions can remain human-authorized even if the agent performs the underlying work.

As agent adoption grows, this distinction will become increasingly important. Cloud Security Alliance research published in 2026 found that 53% of surveyed organizations had experienced agents exceeding their intended permissions, while 47% reported an AI-agent-related security incident.  The challenge is therefore not simply deploying agents with credentials that work. It is ensuring the organization's governance model remains effective after those agents begin operating independently.

Responsibility remains with the organization

If an agent is authorized to act and independently makes the wrong decision, who is responsible?

The answer cannot simply be the agent.

An AI agent may execute a decision autonomously, but responsibility for the authority delegated to it remains with the organization. Organizations define what the agent is supposed to accomplish, determine what access it receives, decide how much autonomy it has, and establish the conditions under which additional authorization is required.

That does not necessarily mean one team owns every aspect of AI agent governance. In most enterprises, responsibility will need to be distributed across the teams involved in deploying and operating the agent.

The business owner should be accountable for the business purpose being delegated. That includes defining what the agent is expected to accomplish, which outcomes are acceptable, what level of autonomy the process can tolerate, and where a decision is significant enough that human involvement should remain.

Identity and security teams are responsible for translating those requirements into governance controls. They need to understand the identities agents use, govern their lifecycle and access, establish policies around authentication and authorization, and ensure inappropriate access can be detected and removed.

Application and data owners determine what authority should be available against the resources they control. They also understand where the application's permission model may be more permissive than the business requirement, creating the type of granularity gap described earlier.

AI and engineering teams need to understand how the agent actually operates. They are responsible for implementing the workflows, integrations, tool access, and technical controls that determine how organizational policy is enforced when the agent executes.

What matters is that these responsibilities are explicit rather than assumed. An agent with a technical owner but no business owner may have someone responsible for keeping it operational without anyone accountable for whether its authority remains appropriate. Similarly, an agent approved for one purpose may gradually gain new integrations and capabilities without the original governance decision ever being revisited.

This is why AI agent ownership cannot simply mean knowing who created the agent. Organizations need to know who is accountable for the authority delegated to it throughout its lifecycle.

Agent access can create indirect privilege

AI agents also complicate the way organizations calculate effective access because the identity requesting an action and the identity executing it may not be the same.

Consider an employee who cannot directly access a sensitive set of customer records but can interact with an AI agent that can. If the employee can instruct the agent to retrieve those records on their behalf, the organization may have created an indirect path to information the employee was never intended to access.

The employee's permissions may look correct. The agent's permissions may also look correct when reviewed independently. The governance failure only becomes visible when the relationship between the two identities is considered.

The same problem can occur between agents. Agent A may have relatively limited permissions but be able to invoke Agent B, which has significantly broader access. Agent B might then invoke a tool using another service identity with still greater privileges. Individually, each identity can appear appropriately configured while the complete chain creates an effective level of authority that was never intended for the originating identity.

This introduces an important question for identity governance: who is actually exercising an agent's authority?

If an agent is acting on behalf of a human user, the downstream action should not automatically inherit every permission available to the agent simply because the agent possesses them. Where appropriate, the user's authorization context needs to remain part of the decision. OWASP recommends executing agent actions in the user's security context where possible and ensuring that downstream functionality does not exceed the user's permissions simply because the agent has broader access.

For organizations, this means reviewing an agent's access in isolation is not enough. Governance needs to understand relationships between the human initiating a request, the agent receiving it, any additional agents or tools invoked, the identity ultimately used against the target system, and the action performed.

This is where identity context becomes particularly important. A security team may know that an agent has permission to modify a Salesforce record, but that fact alone does not answer whether a particular employee should be able to cause that modification through the agent. The policy decision may depend on both identities and the relationship between them.

As multi-agent systems become more common, these chains can become considerably more complex. Without the ability to connect identities and actions across the workflow, organizations risk creating privilege escalation paths that are difficult to see in a traditional entitlement review.

AI agent governance has to continue after access is granted

The foundations of identity governance still apply to AI agents. Organizations need to discover which agents exist, assign clear ownership, understand their business purpose, govern credentials and permissions, manage their lifecycle, and regularly evaluate whether their access remains necessary.

The difference is that agent governance cannot stop once the correct access has been granted.

As agents operate with greater autonomy, organizations need controls that continue evaluating how authority is being exercised. That means connecting identity and access context with what is happening at runtime rather than treating provisioning as the end of the governance decision.

A practical governance model should be able to answer several questions throughout an agent's lifecycle:

Which AI agents can operate in the environment, and who owns them?

Does each agent's access align with its defined business purpose?

Where are application limitations forcing the organization to grant broader permissions than it would ideally provide?

Which authorized actions can execute autonomously, and which require additional authorization or human approval?

Can a user gain access or privileges indirectly by interacting with an agent that has broader permissions?

Can one agent invoke another agent or tool and effectively escalate its authority?

Can an action be traced through the agent to the human, process, or other agent that initiated it?

Can policy prevent an inappropriate action even when the agent is technically authorized to perform it?

When the agent's purpose, integrations, or capabilities change, does its governance change with them?

Who is accountable when an authorized agent makes the wrong decision?

These questions move AI agent governance beyond simply adding agents to an identity inventory. They require organizations to understand the full relationship between identity, access, autonomy, and execution.

They also change how organizations should think about continuous governance. A quarterly review may confirm that an agent still needs a particular permission, but it cannot determine whether every use of that permission during the next three months will be appropriate. Governance therefore needs both lifecycle controls that determine what authority the agent should have and runtime controls that determine how that authority can be exercised.

Governing access, autonomy, and accountability

AI agents do not replace the fundamentals of identity governance. They make those fundamentals more important while introducing a new layer of control.

Organizations still need visibility, ownership, lifecycle management, least privilege, and access reviews across human, non-human, and AI identities. But autonomous agents require organizations to distinguish between two different problems: whether the agent has appropriate access in the first place and whether an authorized action is appropriate in the context in which it is being performed.

The first is primarily a problem of access governance. Organizations need to minimize permissions, address granularity gaps, govern lifecycle changes, and understand the relationships that can create indirect access.

The second is a problem of delegated judgment. Organizations need to determine which decisions an agent can make independently, what contextual policies should govern those decisions, where additional authorization is necessary, and who remains accountable for the outcome.

Neither can be solved effectively without the other. Perfect runtime controls cannot compensate for an agent with unnecessarily broad access, and perfect least privilege cannot guarantee that every legitimate use of an appropriate permission will produce the right outcome.

As AI agents take on more responsibility across enterprise systems, successful governance will depend on connecting identity, permissions, context, and runtime controls rather than treating agent access as a one-time authorization decision.

If you are a Gartner subscriber, you can read the report Establish an Identity Data Layer to Reduce Technical Debt and Secure AI which Linx as an identity hygiene vendor by logging into Gartner's portal here.

At Linx, we bring human, non-human, and AI identities into a unified identity governance model while extending governance to the actions AI agents take across enterprise systems. Schedule a demo to see how Linx helps organizations govern AI agent access and enforce policy as agents operate.