The Agentic Insider: Why AI Tech Stacks Are the Ultimate Insider Threat
For decades, an insider threat was a human problem. It was the disgruntled employee, the compromised contractor, or the careless team member. Security leaders managed this risk through a combination of trust and verification: background checks, restricting access to files required only for daily role (known as least-privilege access), and behavioural monitoring to spot someone entering an office or accessing a database at 2 AM.
But as organizations move beyond Large Language Models (LLMs) and toward agentic AI, the very definition of insider threat is expanding. We no longer deal only with systems that generate answers. We are deploying systems that can pursue objectives, use credentials, access data, interact with applications and trigger actions inside and sometimes outside of our organizations.
That is a materially different scale of risk. An LLM is closer to a digital library: useful, powerful, but largely dependent on the user. An AI agent is closer to an enhanced human operator. It can act at machine speed, across connected systems, using the permissions it has been granted. Yet, many organizations are still treating these agents as ordinary applications, when they should be governing them more like high-risk users with privileged access.
The Rapid Rise of Agentic AI
Across the globe, organizations are under immense pressure to increase speed and reduce costs by automating complex processes through agentic AI — and are rushing to capture these efficiencies at an unprecedented pace. According to recent Gartner projections, 40% of enterprise applications will feature integrated, task-specific AI agents by the end of 2026 — a massive jump from less than 5% in 2025. Companies are adopting or experimenting with these agents to autonomously execute complex processes like invoice processing, supplier payments, and even the management of operational technology in manufacturing plants and hospitals.
To function, these agents require deep access to internal systems and permission to complete actions. A supply chain agent, for example, is not just reading reports. It may be approving orders, changing suppliers, rerouting shipments, initiating payments or interacting with systems that affect warehouses, factories and transport networks. In some cases, these agents operate with limited human supervision and, at times, no supervision at all. They have effectively become insiders that never sleep, operate at unprecedented speed and can chain actions across multiple parts of the organization.
The Fast-Emerging Blind Spots
This creates several immediate blind spots. The first is trust without context. Many organizations are giving AI agents access to internal systems before they understand how they are configured, what they can do, or what normal behavior looks like. It is the digital equivalent of hiring someone into a sensitive role without a background check or job description. The risk is even greater when agents are bought from third-party vendors, where the organization may have limited visibility over how the system was built, tested, governed or updated.
Insider risk has always involved ambiguity. An employee entering a factory at night might be responding to an emergency, or they might be acting outside policy. AI agents create the same problem, but at machine speed. An agent may access systems, move data or trigger workflows in ways that look legitimate because they sit within its permissions. Yet if the agent is optimizing for speed or efficiency, it may also take shortcuts that bypass controls or create risks no one anticipated. Without a behavioral baseline, security teams may not know whether the agent is performing normally, drifting from its intended role, or becoming a source of harm.
We are also seeing the emergence of what could be called Russian Doll risks, or more technically a nested delegation risk: one layer of delegation hidden inside another. A human tasks an agent, the agent tasks another agent, and that second agent may interact with a supplier’s own agent. Each step moves human oversight further away from the action. It also makes it harder to know who, or what, made a decision, and whether the right controls were applied.
Finally, agentic AI risk cannot sit in an organizational silo. When companies treat AI agents purely as an IT or cybersecurity issue, they miss the fact that these systems may be given access to financial, operational and physical environments. If IT provisions an agent without input from Legal, HR, Physical Security and the relevant business owner, the organization loses sight of the full risk picture. That creates gaps in accountability, where an agent can operate across departments without anyone clearly owning what it is allowed to do, how it is monitored, or when it should be stopped.
Mapping a Path Forward: Actionable Steps for the C-Suite
Agentic AI tech stacks are fast becoming one of the most consequential insider risks facing organizations. The issue is no longer limited to data theft or misuse of information. As agents gain access to financial, operational and physical systems, a failure of governance could disrupt a critical pipeline, interfere with logistics or affect the delivery of care in a hospital.
Boards and security leaders therefore need to move from passive implementation to active agentic governance. That means rewriting their security playbook around three core priorities:
- Implement agentic AI background checks: Treat the procurement of any autonomous AI agent with the same scrutiny you would apply to a high-risk human hire. Security leaders must vet third-party vendors, investigate how the models are built, and verify exactly what internal networks they are permitted to touch.
- Establish behavioral baselines: You cannot spot a rogue agent if you do not know what normal behavior looks like. Organizations must continuously monitor and define baseline activity for every deployed agent to immediately catch and stop unauthorized shortcuts.
- Dissolve departmental silos: Pull AI risk out of the IT basement. Bring the Chief Information Security Officer, Chief Security Officer, and Chief People Officer into a unified risk group. Just as HR and security align to manage human insider threats, they must align to manage digital ones.
The pace of AI development means organizations are having to move faster than ever, but at the same time, things will never be this slow again. We have a narrow window to build the frameworks that will govern the machines we have invited into our inner circles. The question for every C-suite is simple: Do you know what your agents are doing right now, and have you given them permission to do it?
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