Why AI Is Becoming the Enterprise’s Most Dangerous Insider
Key Highlights
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AI systems now operate inside enterprises with access and autonomy similar to human insiders, expanding familiar risk patterns at machine speed.
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Insider risk increasingly stems from behavioral drift — by people and AI — rather than explicit malicious intent.
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Security leaders must prioritize behavioral intelligence to detect abnormal activity before AI-driven incidents occur.
For years, enterprise security has revolved around a persistent truth: incidents occur when legitimate access is used in illegitimate ways. Sometimes that misuse stems from malicious intent; more often it comes from human error, over-permissioned environments, or the quiet drift of normal behavior into risky territory.
Artificial intelligence (AI) isn’t rewriting that truth. It’s intensifying it in familiar ways, only faster. Shadow AI and agentic AI (both sanctioned and unsanctioned) execute tasks, move data and make decisions at unprecedented speed and scale. The threat is not a new category but a magnified version of one security leaders already know well: insider risks.
For enterprises, the task in 2026 is to rethink insider risk management by extending proven human-insider principles to the digital employees and systems that now behave like insiders.
The archetypes of AI insider risk
The archetypes of AI insider risk sit on the same spectrum as human insiders: often invisible, legitimate and unmonitored.
Shadow AI becomes an invisible insider as more well-meaning employees leverage unsanctioned GenAI tools to enhance their productivity (best case scenario) or malicious employees leverage unsanctioned AI tools to intentionally exfiltrate data.
Sanctioned agentic AI behaves like an over-privileged employee, operating inside the perimeter with broad autonomy and access. Without visibility and guardrails, it can overshare, auto-publish IP or expose sensitive information at extremely high volume.
Unsanctioned agentic AI mirrors a rogue colleague. Plugins or prompt-injected agents chain actions under legitimate identities, expanding exposure through routine use. Unsanctioned agents run the risk of exposing the organization to unknown or unintended risks because expected corporate controls and monitoring are not in place.
These patterns are the natural outcome of AI manifesting as digital employees, treated as organizational insiders and expanding insider risk through digital behavior rather than human intent.
AI’s expanding insider risk footprint
AI is no longer an experiment on the edge of the enterprise. It sits inside daily workflows: assistants drafting analysis, note-taking tools recording meetings, autonomous agents performing multi-step tasks and developers relying on generative models for code, testing and automation. Much of this use is unsanctioned or poorly governed, not out of malice but because the tools are frictionless and immediately useful.
This ubiquity unlocks enormous productivity but also creates a fertile ground for mistakes. We’ve seen meeting transcripts quietly stored in personal cloud accounts; domain-wide connectors granted to autonomous agents with little oversight; AI agents with access to sensitive corporate documents and autonomy to create documents accidentally published with publicly accessible URLs; and tools manipulated through prompt injection to perform actions that appear indistinguishable from routine automation.
Recent reporting from Anthropic showed how threat actors attempted to steer its Claude model into executing steps of an automated espionage workflow, manipulating small tasks into a larger intrusion sequence. The pattern is clear: AI is accelerating both the scale and subtlety of offensive operations, but with the permissions, access and trust of an insider.
The same risks, at machine speed
Once AI becomes embedded in enterprise workflows, its risk profile begins to resemble the insider risk patterns security teams already understand. The parallels are direct. Malicious misuse occurs when an agent is hijacked or intentionally manipulated to act under legitimate credentials, giving attackers the ability to move data or execute harmful tasks with the same authority as a trusted user.
Negligent misuse arises when well-intentioned employees rely on AI tools in unsafe ways, such as pasting sensitive content into external services, or over-trusting AI outputs in high-consequence decisions. And compromised misuse emerges when shadow AI tools, rogue extensions, or prompt-injected agents are exploited as unmonitored intermediaries, performing actions the user never intended and that traditional controls often fail to see.
What unites these scenarios is behavioral drift: the moment when access, human or machine, begins to be used in ways that deviate from established norms. The shift may be subtle at first (a new tool adopted, a sudden change in data movement, an unusual spike in automated activity) but it is almost always visible before the incident occurs, if an organization knows what to look for and how to see it. This is where the power of behavioral intelligence becomes strategic.
Behavior reveals risk early
For human insiders, behavior has always been the earliest and most reliable signal of risk. Before an incident occurs, people often change how they work: accessing systems at unfamiliar hours, turning to new tools, or moving data in ways that don’t match their usual patterns. These shifts, subtle as they seem, give security teams the chance to notice that something is off long before harm is done.
AI is introducing its own version of this. Systems that generate content or automate tasks leave footprints too: in the prompts they receive, the actions they take and the way they connect across applications. When those footprints start to look different, it often reflects a change in how the AI is being used. Sometimes the cause is innocuous, like a user experimenting with a new workflow. Other times it stems from a misconfiguration or from someone deliberately trying to steer the system into behavior its designers didn’t intend.
The opportunity for security leaders is to start thinking about these machine patterns the way they think about human ones: as a source of insight into intent, context and emerging risk. Doing that requires more than simply watching what an AI system produces. It means understanding how it moves through the environment, how it interacts with sensitive information, and where its decisions diverge from expected norms. It also means considering how to guide these systems toward safer behavior without slowing the innovation they enable.
This shift reflects the same evolution that has reshaped insider risk management programs over the past decade: a move away from static controls and toward a deeper understanding of how work actually happens inside the enterprise. The difference now is that organizations must apply that understanding not only to people, but to the AI systems that increasingly operate alongside them.
The leadership imperative for 2026
The coming year will challenge security leaders to rethink how trust is earned, monitored and maintained across both human and machine activity. AI’s role inside the enterprise is no longer theoretical or peripheral; it is shaping decisions, touching data and influencing outcomes in ways that demand a more nuanced understanding of intent and context.
Security models built solely on access, controls, or static policy won’t keep pace with that shift. The next generation of resilience will come from leaders who treat behavior as the signal that matters most. Those who do will be positioned not just to prevent incidents, but to guide their organizations confidently through the next wave of AI-driven transformation.
About the Author
Marshall Heilman
CEO
Marshall Heilman is the Chief Executive Officer for DTEX Systems, a global provider of insider risk management. Marshall has more than 20 years of experience in cybersecurity (in startup and large public organizations), holding executive leadership and highly technical roles at Mandiant and Google.