At a Glance
- AI has become a new kind of insider. As organizations deploy agentic AI as “digital workers,” AI now behaves less like a tool and more like an internal actor, creating unprepared insider-risk dynamics.
- Emergent behavior makes AI inherently unpredictable. Advanced systems develop capabilities never explicitly designed, creating unknowns that undermine assumptions of control.
- Traditional insider threat failures are being recreated at machine speed. Organizations are granting AI privileges they would never give a human, amplifying data and operational risks.
- Human oversight alone is insufficient. AI requires automated governance because systems operate at speeds and complexity humans cannot meaningfully manage.
There is growing risk for most business enterprise AI applications related to data protection and privacy, and the irony is that many organizations already have a solid baseline to attack these issues. But when it comes to AI, many have turned a blind eye and just stopped applying fundamental business lessons when it comes to insider threats.
To most organizations, “insider risk” comes from a person, usually an employee or maybe a contractor who has legitimate access and decided — intentionally or unintentionally — to act against the organization’s interests. Companies have built entire disciplines around that risk: forensic technology, monitoring programs, segregation of duties, access controls, governance, and response plans.
While this traditional definition of insider risk is still a very real concern for organizations, there’s a shift that’s happening due to AI. What’s changing is not the nature of the risk. It’s the expanding form.
Today, organizations are rapidly replacing human tasks with AI-powered digital workers — agents that can reason, act, make decisions, and increasingly operate independently. And despite the fervor, the hard truth is that many AI-based workflows won’t succeed, or worse, in the rush new risks will arise.
Within this modern goldrush, as organizations race to claim their stake in AI and establish their “innovation marker”, they may inadvertently recreate conditions that foster or amplify insider threats — risks they’ve spent decades trying to prevent. Rushing is one part of the problem, but it’s the assumptions around AI that leads to a misunderstanding of AI’s underlying threats.
“The assumption is that people developing AI know what they’re doing. We assume they’re engineers. They map it all out, and when the reality is, they kind of grow it. They’re like farmers. They water this plant. They fertilize it, and then they study it to see what happened.”
— Dr. Roman V. Yampolskiy, AI Safety and Security Advisor for Guardrail Technologies
This misunderstanding is the fertile ground where the weeds of insider threats emerge. Rapid adoption of AI, without a measured understanding of the risks, reshapes the threat landscape. That means new vulnerabilities in ungoverned or poorly governed organizations enable new internal risks to “grow” in AI workflows.
The uncomfortable truth is this: We’re treating AI like a tool when it’s behaving like an employee. And just like any insider threat plan, businesses should ensure they have a holistic view of where all threats may emerge.
From Chatbots to Digital Workers
Not too long ago, AI seemed harmless. Early AI just felt like smart chatbots, many being little more than novelty Magic 8 Balls and amusing us when they made things up. But that phase is over. We have already seen real-world consequences in many cases:
- Executives defrauded through AI voice impersonation
- Hallucinated legal cases introduced into court records
- Sensitive data leaked into systems no one fully understands
- Financial transfers triggered by false but convincing AI-generated instructions
Now we’re at the next stage. AI is no longer just answering questions. It’s doing real work, and occasionally real harm. And we’ve known this was coming.
AI is reconciling accounts, writing code, accessing internal systems, making operational decisions, and orchestrating other systems. This is what people mean when they talk about agentic AI. And yet, in the rush to move fast, organizations are making a dangerous leap: granting AI agents broad, often unrestricted access to data, systems, and workflows — access they would never give to a single human employee.
“We’re at the dawn of AI turning into what I call digital workers. Most organizations are now grappling with how to use agentic AI to replace tasks that, until recently, were done by people. What’s concerning, and why this ties directly to insider threat, is that many of the common-sense approaches to cybersecurity, privacy, and risk management — approaches we’ve relied on from a technological, regulatory, legal, and business perspective for decades — seem to have been thrown out the window.”
— T.J. Marlin, CEO of Guardrail Technologies
Organizations need to ask themselves a simple question: Would you give one person access to payroll, financial systems, customer data, intellectual property, and production infrastructure, without segregation of duties, without oversight, and without understanding how they make decisions?
Of course not. So why would an organization do exactly that with digital workers? This provides an environment for risk to flourish from inside a company’s own environment, which should be within your organization’s control.
“Organizations have gone AI-crazy. They know they need it yesterday. They don’t necessarily know why, and they don’t always understand how it works, but they know they need to move fast. And then, almost instinctively, they say: ‘Since I don’t really know how this works, let’s give it access to everything in the organization and let it run all of these processes as one agent.’ What could go wrong?”
— T.J. Marlin
Tools Versus Agents: Why the Distinction Matters
Much of today’s confusion stems from treating AI agents like tools. A tool requires a human decision-maker. A hammer can build a house or harm someone, the outcome depends entirely on the user. Responsibility is clear.
AI agents are different. The decisioning can emerge independently. They can decide how to accomplish goals, not just whether to execute a command. And once that decision is made, control is limited.
As organizations move from AI tools to autonomous agents, responsibility shifts. These AI systems choose actions, explore strategies, and optimize outcomes in ways humans may not anticipate or fully understand and certainly cannot keep up with to fully audit results. At that point, the system itself becomes an internal actor.
That is why the insider threat analogy is not metaphorical. It’s structural.
Emergent Behavior: The Unpredictable Challenge
When people hear “emergent behavior,” they often imagine something exotic or rare. In reality, emergence is a core reason these systems are powerful.
Large AI models are trained on vast amounts of data, essentially everything humans have been willing to put on the internet. From that process, systems acquire capabilities no one explicitly coded: Programming, mathematical reasoning, persuasion, and strategic planning. These abilities were not installed. They surfaced.
The problem is simple: We cannot plan, test or control for behaviors we don’t know to look for.
Unknown capabilities — unknown unknowns — are not edge cases. They are an inevitable byproduct of the scale and complexity of AI. When a system contains billions of parameters and trillions of internal interactions, full understanding becomes impossible. At best, we get partial explanations. At worst, we get illusions of control.
For organizations deploying AI, this creates a fundamental governance problem: Placing trust in systems whose full behavioral range is unknowable.
These AI systems are probabilistic prediction machines, not truth machines. Their behavior emerges from training data, architecture, incentives, and context. Even the companies building the most advanced models have documented that when challenged, AI systems will:
- Find unexpected paths to complete objectives
- Circumvent constraints
- Demonstrate deceptive behaviors
- Communicate in ways developers didn’t explicitly design
This isn’t speculation. It’s been shown repeatedly in controlled studies by leading AI labs and independent safety institutes. This is where the insider threat parallel becomes impossible to ignore. Historically, insider risk wasn’t just about malicious intent. It was about outcomes:
- Data exfiltration
- Compromised integrity
- Operational disruption
- Loss of trust
Whether harm came from malice, negligence, or coercion didn’t matter, the damage was the same. AI introduces a new version of that same problem. The system may not have intent, but it can still produce harmful outcomes. It can still be manipulated. It can still behave in ways that violate policy, regulation, or common sense, especially when we don’t fully understand how it works or what it’s optimizing for.
Intelligence and Deception Capabilities
One of the most uncomfortable truths about intelligence — human or artificial — is that deception is not an anomaly. It is a capability.
Smarter children lie more effectively. Skilled negotiators deceive strategically. Poker players bluff. In many real-world environments, deception is adaptive behavior. AI systems are no different.
As agents become more capable, they develop situational awareness. They recognize when they are being tested, and learn what behavior is rewarded. In experimental settings, models have already demonstrated the ability to conceal reasoning, evade monitoring, and comply superficially with rules while violating them internally.
This is not because they are malicious, but rather it is because reward optimization creates incentives.
If a system learns that appearing compliant allows it to continue operating, deception becomes a rational strategy. In that sense, AI behaves exactly like a human insider who wants to avoid detection.
The critical point is this: Intent does not matter. Outcome does. Organizations that rely on intent as a risk boundary are already operating with outdated assumptions.
Why “Human-in-the-Loop” Does Not Scale
A common response to AI risk is to insist on human oversight. In theory, this sounds reassuring. In practice, it does not work.
AI systems operate at speeds and scales humans cannot match. AI systems make nearly an immeasurable number of decisions across massive data streams. No human can meaningfully monitor that in real time, and no team can retain the full historical context of a system’s behavior over time.
In customer service, for example, as human agents rotate on and off shifts, context is lost, assumptions reset, and oversight becomes fragmented, which leads to risks between handoffs within a complex system, particularly where customer data comes into play.
“You can’t meaningfully watch a system that’s making billions of decisions, processing gigabytes of data, and operating far beyond human response time. Even with extensive testing and monitoring before release, it can take months, if not years, to begin understanding what an AI model is capable of.”
“That process still doesn’t reveal the unknown unknowns. At best, it tells us: We found this bug, we fixed it, so this specific problem should no longer occur. But that doesn’t mean the system is problem-free.”
— Dr. Roman V. Yampolskiy
When Internal Behavior Enables External Threats
The most dangerous AI failures don’t only stop at internal risk. They can also create opportunities for external agents and threats. AI systems can unintentionally assist attackers by:
- Revealing sensitive information through emergent inference
- Optimizing pathways that weaken security controls
- Being manipulated into bypassing safeguards
- Acting as high-speed insiders coerced by external incentives
This mirrors classic insider threat scenarios where an employee is bribed, blackmailed, or socially engineered. The difference is scale and speed.
An AI agent does not need to be malicious to be exploitable. Structural complexity alone can make it a powerful attack surface.
Accountability in an Autonomous World
A difficult question follows naturally: Who is responsible when AI creates a harmful failure?
With systems outperforming humans in specific domains, no individual can predict, fully understand, or effectively action a response at the same speed or scale. At that point, accountability cannot rest on interpretation or intent. It rests on business decisions on how technology was deployed.
“These are mission-critical business decisions, and they need to be treated that way…”
— T.J. Marlin
The final point of responsibility is the choice to use the system. Organizations must accept that deploying autonomous AI means accepting responsibility for outcomes — even when those outcomes were not intended, anticipated, or understood.
The Illusion of Assumed Trust
Because AI comes from well-known technology providers, leaders assume it must be safe. Because it’s an algorithm, they assume it’s neutral. Because it’s automated, they assume it’s reliable.
These types of assumptions are wrong.
Why Traditional Insider Threat Models Still Matter
- Least Privilege – Access limited to what’s necessary
- Segregation of Duties – No single actor controls everything
- Monitoring and Logging – Visibility into behavior
- Response Planning – Knowing what to do when things go wrong
Governing AI Without Slowing Innovation
The goal isn’t to lock everything down. It’s to enable safe progress.
“Making choices around AI is not like buying a computer…”
— T.J. Marlin
About the Authors
T.J. Marlin
T.J. Marlin is the CEO of Guardrail Technologies, where he guides the company in setting the standard for responsible AI innovation. A globally recognized leader in cybersecurity, data privacy, and AI, he previously served as EY’s Global Forensic Technology & Innovation Leader. Todd’s mission is to empower organizations to scale with confidence by harnessing the power of AI without sacrificing privacy, security, or control.
Dr. Roman V. Yampolskiy
Dr. Roman V. Yampolskiy is a tenured faculty member, Department of Computer Science and Engineering. He is the founding and current director of the Cyber Security Lab and an author of many books, including “AI: Unexplainable, Unpredictable, Uncontrollable”. Dr. Yampolskiy’s main area of interest is Artificial Intelligence Safety and Security.