Why Every AI Deployment Should Be Governed Like a Privileged Employee
For years, conversations about insider threats have followed a familiar pattern. Security teams worried about malicious employees stealing intellectual property, well-intentioned staff making costly mistakes, and compromised accounts being exploited by attackers. Regardless of the scenario, one assumption sat quietly beneath every insider threat programme: the insider was a person.
The rise of artificial intelligence is beginning to challenge that assumption.
Across organisations, AI assistants, copilots, knowledge systems and autonomous agents are being connected to repositories of corporate knowledge, collaboration platforms, business applications and data stores. The objective is entirely reasonable. Businesses want employees to spend less time looking for information and more time using it. They want faster decisions, increased productivity and fewer administrative burdens. In most cases, that’s exactly what AI is delivering.
Yet something more significant is happening beneath the surface. AI is gradually moving beyond the role of a productivity tool and becoming an active participant in how work gets done. Employees increasingly rely on AI to find information, interpret knowledge, recommend actions and, in some cases, execute tasks on their behalf.
That shift raises an important question for security leaders.
If a system can access more information than most employees, make sense of it in seconds and influence decisions across the organisation, should we continue to think of it as simply another piece of software? Or is it time to acknowledge that AI has started to behave rather more like an insider?
This isn’t an argument against AI. Quite the opposite. The organisations that derive the greatest value from AI over the coming decade will almost certainly outperform those that hesitate. The opportunity is too significant to ignore.
The challenge is ensuring that our enthusiasm for what AI can do doesn’t distract us from understanding what AI can access.
For decades, insider threat programmes have been designed around understanding human behaviour. Security teams learned how to identify employees who intentionally misused access, staff who created risks through carelessness, and accounts that had fallen into the hands of attackers. The framework was built around concepts such as identity, intent, behaviour and privilege.
Those approaches worked because humans have limitations.
An employee may technically have access to thousands of documents, but that doesn’t necessarily mean they’re going to read them. They have finite time, attention and patience. In many organisations, information remains undiscovered not because it is protected particularly well, but because finding it requires effort.
AI changes that dynamic considerably.
Unlike people, AI isn’t constrained by the practical limits of time and effort. It can search vast collections of information in seconds, identify patterns across multiple systems, summarise years of documentation and connect information from entirely different parts of the business. Tasks that might occupy an employee for days can often be completed in moments.
This doesn’t make AI malicious, nor does it mean organisations should fear it. What it does mean is that many of the assumptions underpinning traditional insider risk models deserve another look.
Much of the discussion around AI security focuses on the technology itself. We debate models, hallucinations, infrastructure, prompt engineering and governance frameworks. These are all worthwhile conversations, but they can sometimes obscure a more fundamental issue.
The biggest risks associated with AI often have surprisingly little to do with AI itself.
More often than not, AI shines a light on weaknesses that already existed.
Most organisations have accumulated years of permissions, repositories and data stores. Access rights are granted to support projects, teams are given shared workspaces, temporary exceptions become permanent arrangements and information spreads across collaboration platforms faster than anyone intended. None of this is unusual. In fact, it is difficult to find a large organisation where it hasn’t happened.
Anyone who has spent time in enterprise security knows how these situations develop. A project is launched, access is granted to half a dozen people “for now”, and several years later nobody is entirely certain who owns the repository, although everyone agrees it would be best not to touch anything until after the next major release.
Before AI, many of these issues remained largely hidden. Information might have been accessible, but it wasn’t necessarily discoverable. People didn’t know where it was, didn’t think to look for it, or simply couldn’t be bothered.
Few security frameworks formally recognise “nobody knows where it is” as a control, yet many organisations have benefited from it more than they might care to admit.
AI is steadily removing that layer of accidental protection.
A file buried in an obscure repository may have been effectively invisible to most employees. To an AI system, however, it may be just another source of information. The technology doesn’t distinguish between information that is difficult to find and information that is easy to find. If access exists, discovery becomes considerably easier.
This is why AI governance is ultimately a data governance conversation.
The question isn’t simply what the model can do. The more important question is what we’ve allowed it to see.
The value of enterprise AI depends on access. The more organisational knowledge a system can draw upon, the more useful it becomes. But that same access creates visibility that no individual employee could realistically possess.
In many organisations, AI systems are being connected to document repositories, intranets, collaboration platforms, service management systems and knowledge bases. Viewed individually, each connection makes sense. Collectively, however, they can provide an AI system with visibility across the organisation that exceeds that of almost any human employee.
That’s where the insider threat conversation starts to become interesting.
Historically, insider risk was largely centred around people who had access to information. The emergence of AI introduces a situation where employees can suddenly enhance their own capabilities through technology.
Consider a trusted employee trying to do their job more efficiently. They ask an AI assistant to help prepare a report, answer a customer query, gather research or identify relevant information. Their intentions are entirely legitimate. In fact, they’re probably doing exactly what the business wants them to do.
What changes is the scale.
The employee no longer needs to understand where information resides. They don’t need detailed knowledge of repositories, folder structures or business systems. They simply need to know what question to ask. The AI takes care of the searching, synthesising and summarising.
The result is what might best be described as an AI-augmented insider.
Not a malicious insider.
Not a negligent insider.
Not a compromised insider.
An ordinary employee whose ability to discover and use information has been dramatically enhanced by AI.
The combination of human intent and machine capability creates a new dynamic. Neither element is necessarily dangerous on its own, but together they can produce outcomes that traditional security models were never designed to address.
This becomes even more apparent when AI starts acting as an intermediary between users and information.
Historically, employees interacted directly with systems. They searched file shares, visited intranets and browsed knowledge bases. Retrieving information required a degree of familiarity with where knowledge lived within the organisation.
Today’s AI systems change that relationship.
Increasingly, users interact with information through conversation. They ask questions, and AI searches on their behalf. Rather than navigating multiple repositories, employees engage with a single interface that can reach across all of them simultaneously.
In effect, AI becomes an information broker.
At first glance, this appears entirely beneficial. Employees find information faster. Duplication decreases. Knowledge becomes more accessible.
However, it also creates a subtle shift in risk.
When discovery becomes effortless, information that was once technically accessible but practically obscure can suddenly become much easier to surface. The reported incident involving an internal AI system at Meta highlighted concerns around how AI-mediated access to organisational knowledge can create unintended consequences. Regardless of the details of any particular incident, the broader lesson is clear: information doesn’t always need to be stolen to create risk. Sometimes it simply needs to be surfaced in a context that was never anticipated.
For security leaders, that distinction matters.
Traditional security controls have often focused on preventing unauthorised access. AI introduces scenarios where access may technically be authorised, but the ease and speed of discovery create entirely new governance challenges.
And if today’s AI assistants are changing how information is accessed, tomorrow’s AI agents will change how work is performed.
Much of the current conversation around AI focuses on assistants that answer questions and generate content. The next wave of AI systems is likely to be far more autonomous. Agentic AI introduces the possibility of systems that initiate workflows, update records, interact with applications and perform actions on behalf of users.
That’s a very different conversation.
At that point, organisations are no longer deciding what information AI can access. They’re deciding what authority AI can exercise.
Security teams have spent decades defining approval processes, segregation of duties and governance models for people. Those same questions are beginning to emerge around AI agents. What decisions can they make? What systems can they interact with? What actions should require human oversight?
Like most emerging technologies, the answers are still evolving. What is clear, however, is that the future of insider risk may be shaped as much by delegated authority as by access to information.
None of this should be interpreted as a reason to slow AI adoption.
In fact, organisations that respond to these challenges by attempting to block AI altogether are likely to discover that employees will simply find alternative ways to gain the same benefits. The business value is too compelling, and the productivity gains are too significant.
The goal should not be restriction.
The goal should be governance.
That starts with understanding where AI is being used and what information it can access. It requires revisiting long-standing assumptions around permissions, data ownership and classification. It demands stronger visibility into how information is surfaced, not simply how it is stored.
It also requires a shift in mindset.
Many organisations continue to think about AI as a tool, much like a document editor, collaboration platform or business application. Increasingly, that comparison feels inadequate. AI isn’t simply storing information or facilitating communication. It is interpreting knowledge, influencing decisions and becoming embedded within organisational workflows.
Viewed through that lens, it starts to resemble a trusted insider far more than a traditional application.
The organisations that succeed over the next decade will recognise this before their competitors do. They will understand that AI and governance are not opposing forces but complementary ones. The more capable AI becomes, the more important governance becomes alongside it.
After all, the most privileged insider in your organisation may not be a senior executive, a system administrator or a long-serving employee.
It may be the system you deployed to help them all work a little faster.
And unlike most employees, it will make use of every permission you’ve given it. Which is precisely why those permissions deserve closer attention than ever before.