The Autonomous Insider: Rethinking Insider Risk for the Agentic Era

Insider threat models have historically had one thing in common: somewhere in the chain, there is a person. That person may be malicious or negligent. Their credentials may have been compromised. Their actions may be intentional or accidental. But there is still a human principal at the center of the risk.

Agentic AI is beginning to challenge that assumption.

As organizations deploy autonomous agents with access to enterprise systems, sensitive data, security tooling, and business processes, a new risk category is emerging: the autonomous insider.

The autonomous insider is not necessarily malicious or compromised. It is an authorized non-human entity capable of making decisions and taking consequential actions independently of direct human control.

That distinction matters because many of the security models used to govern trusted access were built around identities that authenticate and act, not identities that can also exercise judgment.

From Automation to Agency

Non-human identities are not new. Organizations have relied on service accounts, applications, APIs, scripts, and automated processes for decades. Security teams already understand many of the risks associated with machine identities: excessive privilege, credential exposure, poor lifecycle management, and inadequate monitoring.

Autonomous agents introduce something different.

Traditional automation generally executes predefined instructions. A script performs a task because someone explicitly defined the logic that tells it what to do. An autonomous agent may instead be given an objective and allowed to determine how to achieve it. It can gather context, select tools, break a task into smaller steps, react to new information, change its approach, and continue operating without requiring human approval for every action.

The meaningful difference is not simply automation. It is agency.

Interestingly, the concept of agency has a much older meaning outside of technology. In law, an agency relationship exists when one party, the agent, is authorized to act on behalf of another, the principal. Much of agency law deals with questions of authority, control, scope, and accountability: What was the agent authorized to do? How much discretion did the agent have? Did the agent act within the scope of that authority? And who is ultimately responsible for the result?

Agentic AI raises surprisingly similar questions.

The concepts are not identical, but the parallel is useful because organizations are no longer only delegating tasks to software. They are increasingly delegating discretion.

An autonomous agent may be given both the technical ability to act and some latitude to decide whether, when, and how those actions should be taken. That creates a fundamentally different security problem from traditional automation.

Identity tells an organization what authenticated. Permissions define what that identity can technically do. Agency introduces a more difficult question: How much independent judgment has that identity been authorized to exercise?

That becomes increasingly important as agents move from assisting humans to independently acting on their behalf.

The Risk Does Not Require an Attacker

Much of the current conversation around AI agent security focuses, appropriately, on compromise. Prompt injection, credential theft, malicious tool use, poisoned data, and agent hijacking all represent serious risks.

But those scenarios still fit relatively comfortably within traditional security thinking because there is an adversary somewhere in the model

The more difficult scenario may be the one where there is not.

Consider an autonomous security agent tasked with aggressively containing suspected threats. It detects activity that appears malicious and decides to isolate a critical production environment. Its credentials are valid, its access is authorized, its logic is functioning as designed.

No attacker compromised the agent. The agent simply made a decision that caused significant operational damage. 

The same issue could emerge in any number of environments. A cloud optimization agent could remove infrastructure it determines is unnecessary, unaware of an undocumented dependency. A software engineering agent may revoke credentials or remove integrations it concludes are obsolete. An autonomous finance or procurement system may take actions that individually fall within its permissions but collectively create an unintended business outcome.

In each case, there may be no malicious insider, compromised account, or external attacker. The entity is trusted, the actions are authorized, but the outcome is harmful.

That is the autonomous insider problem.

Valid identity. Authorized access. Expected tools. No attacker. Still an incident.

Why This Is Not Just an Accidental Insider

It may be tempting to classify this as another form of unintentional insider risk. That comparison is useful, but incomplete. An accidental human insider generally makes an error. They send information to the wrong recipient, misconfigure a system, mishandle data, or take some other unintended action.

An autonomous agent may behave differently. It can evaluate available information, choose between multiple options, take an action, observe the result, and determine what to do next.

The resulting outcome may be unintended by the organization, while the individual actions themselves were deliberately selected by the agent as part of its reasoning process. 

That creates a security problem that existing insider-risk terminology does not fully describe:

What happens when an authorized entity intentionally takes an action that nobody intended?

The issue is not intent in the human sense, but independent decision-making inside a trusted environment.

Delegated Authority Becomes the Core Risk

Cybersecurity has spent years emphasizing identity as a primary control plane, and that remains an important part of any security program. But identity alone is not enough when identities can independently exercise authority.

The central security challenge with autonomous agents is not simply access, but delegated authority.

The agency-law comparison is useful here as well. Authority is not necessarily unlimited simply because an agent has the ability to act. There is a difference between having authority to perform a particular action and having broad discretion to decide when, why, and under what circumstances that action is appropriate.

The same distinction applies to autonomous systems.

An agent may need permission to disable accounts as part of its function. That does not necessarily mean it should have unrestricted authority to decide which accounts to disable under every circumstance. A cloud agent may need the ability to modify infrastructure, but that does not mean every technically permitted change falls within the intended scope of its authority.

This creates a governance problem that extends beyond traditional access control. Organizations need to understand not only what an agent is capable of doing, but the boundaries within which it is expected to exercise judgment.

Which decisions can an agent make independently? Which actions require human approval? Can it delegate work to another agent or invoke additional systems and identities? Can individually low-risk actions be chained together into a high-impact outcome? Can it alter its environment in ways that expand its own effective authority? 

And when an agent acts in a way that was technically permitted but never actually intended, who is accountable for the outcome?

These questions matter because increasing autonomy is not an accidental side effect of agentic AI. It is the product.

The value of autonomous systems comes from reducing the need for constant human intervention. As organizations become more comfortable with the technology, they will naturally remove friction. Agents will receive more context, more integrations, more permissions, and more authority. Humans may move from directly performing tasks to supervising agents, then to reviewing exceptions, and eventually to monitoring outcomes after actions have already occurred.

That makes the distinction between access and authority increasingly important.

Least privilege has traditionally focused on giving an identity only the access necessary to perform its role. In an agentic environment, that principle may need to expand. Organizations also need to consider the minimum level of discretion an agent requires to perform that role safely.

Traditional Insider-Risk Signals Will Not Be Enough

Many insider-risk programs rely on a combination of identity, behavioral, and contextual signals.

A human insider may exhibit unusual working hours, abnormal access patterns, job dissatisfaction, sudden changes in behavior, attempts to bypass controls, or suspicious data movement.

An autonomous agent does not behave like a person. It does not become disgruntled. It does not resign. It does not have financial problems. It does not suddenly start coming into the office at strange hours. 

Unless science fiction has badly underestimated how petty the robot uprising is going to be, we are probably not going to catch an autonomous insider because it complained about its manager and started updating its résumé.

The signals are different.

An agent may operate continuously by design. It may legitimately access large volumes of information. It may interact with dozens of systems in seconds. It may call tools, change paths, delegate tasks, or perform actions at a scale and speed that would look wildly anomalous for a human but completely normal for an autonomous system.

That means organizations cannot simply apply traditional user behavior analytics to agents and assume the problem is solved.

The underlying concept behind behavior analytics, however, still matters. What changes is what we need to measure.

From User Behavior Analytics to Agent Behavior Analytics

User and entity behavior analytics (UEBA) has long been used to establish baselines and identify activity that deviates from expected behavior. A similar approach will be necessary for autonomous agents, but the behaviors being modeled will need to change.

For an agent, anomalous behavior may not look like logging in at 3:00 a.m. or downloading an unusually large number of files. It could mean invoking a tool it rarely uses, accessing a new class of data, taking an unusual sequence of actions to accomplish a familiar task, interacting with a system or agent it has never contacted before, or repeatedly retrying failed actions in ways that create operational risk.

More concerning behaviors could include attempts to expand the scope of its own permissions, sudden increases in the frequency or impact of autonomous actions, deviations from the workflows normally associated with its assigned objective, or decisions with a significantly larger blast radius than its historical baseline.

The goal should not simply be to determine whether an agent’s actions are technically authorized. Security teams also need to understand whether those actions are consistent with how that agent is expected to behave.

That creates an important distinction between permission and normality.

An agent may be allowed to modify production infrastructure. That does not mean modifying hundreds of resources in ten minutes should automatically be considered normal.

It may be authorized to access customer data. That does not mean querying an entirely new data set should go unquestioned.

Just as behavioral analytics helps identify when a legitimate human identity begins behaving in unexpected ways, Agent Behavior Analytics (ABA) could provide an important control for identifying when a legitimate autonomous identity begins operating outside its expected patterns.

The challenge, of course, is that agents are designed to adapt.

Traditional baselines assume some degree of behavioral consistency. Autonomous systems may intentionally change their approach based on context, which means detecting meaningful anomalies without flagging every novel decision will be difficult.

That does not make behavior analytics less valuable. It makes context more important.

Organizations will need to establish expected boundaries around an agent’s objectives, tools, data access, action sequences, level of autonomy, and potential impact, rather than relying on a simple baseline of repetitive activity.

Organizations Need an Inventory of Agency

Most organizations are beginning to recognize the need for an inventory of AI systems and non-human identities. That is necessary, but it will not be sufficient.

Organizations also need an inventory of agency.

For every autonomous or semi-autonomous system, security teams should understand not only what it can access, but what it can independently decide and execute.

That means distinguishing agents that only generate recommendations from those that can take action. It means identifying which systems can make reversible changes and which can make irreversible ones, which agents can modify identities or security controls, which can move sensitive data or money, which can invoke other tools or agents, and where human approval has been removed from the workflow.

It also means knowing how to stop them.

That sounds obvious, but a reliable mechanism to suspend or constrain an agent becomes more complicated when that agent operates across multiple systems, uses delegated identities, triggers other automation, or interacts with other agents.

This is effectively a new dimension of privilege management.

Least privilege can no longer be limited to what an identity can access. It also has to account for how much autonomy that identity has been given to use that access.

Logging Will Need to Capture Decisions, Not Just Events

Traditional logging is largely built around recording actions: an identity authenticated, a file was accessed, a configuration changed, an API was called. 

Those records remain essential, but when an autonomous agent takes a consequential action, knowing what happened may not be enough.

Security teams may also need to understand what objective the agent was attempting to accomplish, what information influenced its decision, which tools it considered or invoked, what intermediate actions occurred, whether another agent or system influenced the decision, and what caused it to change its approach.

In other words, security teams will increasingly need decision provenance, not only event logs.

This has implications for incident response, auditability, governance, compliance, and forensic investigation.

An incident involving an autonomous system cannot always be reconstructed by reviewing authentication and API logs alone. Investigators may need to reconstruct the decision chain that produced the outcome.

That becomes particularly important when every individual action appears legitimate.

If an agent had valid credentials, used approved tools, remained inside its technical permissions, and still caused an incident, the most important forensic question may not be “How did it get access?” It may be “Why did it decide to do that?”

The Insider Threat Model Needs to Expand

Traditional insider threats are not disappearing. Employees will still act maliciously. People will still make mistakes. Credentials will still be stolen. Autonomous agents themselves will also become targets for attackers.

But another risk condition now exists alongside those familiar scenarios:

An entity can be authorized, uncompromised, and operating entirely inside the environment where it was intentionally deployed, and it can still independently cause harm.

That is the autonomous insider.

The goal is not to anthropomorphize AI or suggest that autonomous systems possess malicious intent. This is not a prediction that the robots are plotting against us.

The problem is much more practical.

Organizations are beginning to place independent decision-making capabilities inside trusted environments, connect them to sensitive systems, and give them the authority to act. Our security models need to account for what happens next.

For years, one of the most important questions in cybersecurity has been: Who has access?

In the agentic era, organizations will need to ask another:

Who — or what — has the authority to decide?

And increasingly, security teams will also need to know whether those decisions still look like the ones that entity was expected to make.

Explore Resources on this Strategic Security Issue

The resources below provide additional context for why this is becoming a strategic security issue, particularly as organizations move from experimenting with agents to granting them meaningful access and decision-making authority.

Gartner^® Insider Risk Management Cookbook: Perfecting the Soup
A framework for modern insider risk management that focuses on behavior, identity, and AI-driven detection.
Download the Report

Agent Behavior Analytics: Securing the Autonomous Enterprise
Understand how to detect, monitor, and investigate AI agent activity across modern security operations.
Read the White Paper

Praxen, Open-Source Agent Behavior Verification Tool
Praxen compares an AI agent’s declared policy against real evidence — its code, live deployment state, and behavioral logs — and reports exactly where observed behavior drifts from intent, before it becomes a risk.
Get Praxen

Observra, Open-Source Telemetry SDK for AI Agents
Observra captures what agents do — model calls, tools use, cost, errors — converts it into one common event format, and routes it to the observability and security tools you already use.
Get Observra