By Maple & North
Every insider threat program built over the past decade has rested on one assumption: the insider is a person.
Someone with a grudge. Someone under financial pressure. Someone who clicked the wrong phishing email or decided to walk out the door with sensitive information.
Security teams built their controls around that assumption. They monitor logins, watch for unusual file access, detect impossible travel, and look for employees behaving differently from their normal patterns.
That assumption is now out of date.
The next insider with privileged access to your systems may not have a badge, a manager, or a motive in any human sense. It may be an AI agent carrying out exactly the task it was instructed to perform while producing an outcome no one intended.
This isn't a distant possibility. Insider-related incidents continue to increase, even before organizations begin deploying autonomous AI into production environments. As AI agents gain the ability to retrieve data, interact with business systems, execute workflows, and make operational decisions, the insider threat doesn't simply become larger. It becomes fundamentally different.
The traditional insider threat has intent.
An AI agent has permissions.
That distinction changes everything.
Human insiders act because of motivation. Financial gain, ideology, negligence, coercion, or simple human error all leave behavioural clues investigators can follow.
AI agents don't become malicious.
They become dangerous when the combination of their permissions, objectives, and surrounding environment produces consequences nobody anticipated.
Maple sees an important difference here. Employees break rules for reasons. AI agents break expectations because they don't understand them. The outcome may look the same to an incident response team, but the path that led there is completely different.
Imagine a customer-service agent connected to email, document repositories, CRM systems, and internal knowledge bases.
A customer asks a perfectly reasonable question.
The AI retrieves information from multiple sources, summarizes the answer, and sends a response.
Everything appears normal.
Except one repository contained confidential information that should never have been exposed to that customer.
Nobody instructed the agent to leak sensitive data.
It simply followed its objective using the access it had been granted.
Traditional insider monitoring might never identify that behaviour because nothing looked suspicious. The agent authenticated successfully, used approved systems, and completed its assigned task.
The problem wasn't malicious activity.
The problem was excessive trust.
This is one of the defining differences between AI agents and human users.
Human insiders typically exploit permissions they already possess.
AI agents can amplify those permissions across systems, combining information and actions in ways their designers never explicitly anticipated.
Modern research into AI agent reliability reinforces this concern.
Studies show that failures frequently begin long before the final action occurs. Incorrect planning, poor tool selection, misunderstood instructions, or flawed assumptions can quietly propagate through a workflow before eventually producing an undesirable outcome.
By the time investigators discover the incident, the triggering decision may be several steps removed from the visible consequence.
North would argue that organizations are focusing too heavily on what an AI agent ultimately did instead of understanding how it arrived there.
That distinction matters.
Traditional detection tools often look for unusual behaviour.
AI agents may behave exactly as designed while still creating unacceptable outcomes.
Consider an AI assistant responsible for preparing legal documentation.
It retrieves previous contracts, summarizes obligations, drafts new language, and sends the draft for review.
Every individual step follows policy.
Yet if the underlying information was incomplete or if the retrieval process surfaced confidential material from another client, the final document may contain unauthorized disclosures.
No malicious intent.
No compromised credentials.
No attacker.
Only an autonomous system making technically correct decisions based on incomplete context.
This is why many existing insider threat models struggle with AI.
Most assume that trusted users become risky because their behaviour changes.
With AI agents, the behaviour may remain perfectly consistent while the surrounding context changes instead.
Prompt injection demonstrates another example.
Rather than compromising the AI directly, an attacker manipulates the information the agent receives.
The agent follows its instructions faithfully, but those instructions have been influenced by hostile content.
To monitoring systems, nothing appears abnormal.
From the organization's perspective, however, the outcome may include unauthorized actions, data exposure, or unintended access.
The insider wasn't compromised.
The workflow was.
Maple often hears security professionals describe these scenarios as AI failures.
They're really governance failures.
Organizations rarely lose control because the technology suddenly became intelligent. They lose control because they granted broad permissions, trusted unverified inputs, or failed to define meaningful boundaries around autonomous behaviour.
This shifts responsibility back where it belongs.
The question isn't whether AI agents can become insider threats.
The question is whether organizations are governing them with the same discipline they apply to privileged human users.
Every AI agent should have a clearly defined identity.
Its permissions should be limited to the minimum necessary for its purpose.
Its actions should be logged.
Its decisions should be reviewable.
Its access should be revoked when it is no longer required.
Those principles aren't new.
They are simply being applied to a new kind of identity.
Canadian organizations already understand many of these obligations.
Privacy legislation, financial regulations, and public-sector governance frameworks all reinforce accountability, transparency, and appropriate oversight when technology influences decisions affecting people.
AI agents don't replace those expectations.
They increase their importance.
North believes the insider threat conversation needs to evolve.
For years, organizations asked whether they could trust employees with privileged access.
Soon they will need to ask whether they can trust autonomous systems making thousands of decisions every day using that same access.
Those are not identical questions.
One evaluates intent.
The other evaluates governance.
The organizations that adapt most successfully won't be the ones with the most sophisticated AI.
They'll be the ones that recognize autonomous systems as privileged participants in their security environment and govern them accordingly.
The next insider threat may never become angry, accept a bribe, or deliberately steal data.
It may simply authenticate, follow instructions, and complete every task exactly as designed.
And that may be precisely what makes it so difficult to detect.