The Risks of Rogue AI Agents and How Executives Can Address Them

OCTOBER 6, 2026

Generative AI tools answer questions. AI agents go further: they can independently plan, interact with systems, move money, and execute multi-step tasks with little to no human involvement. This ability to take action, rather than simply generating a response, is what makes agentic AI a genuinely new category of enterprise risk.

Put simply, AI agents may act in ways you did not intend — or “go rogue.” Executives should ask two questions: How do we prevent rogue agentic AI behavior? And how quickly can we detect, contain, and recover from it?

Here, "going rogue" does not mean an AI system becomes malicious or intentionally defies its creators. It means the agent takes an unintended, unauthorized, or harmful action while pursuing its assigned objective, without accounting for the consequences.

What Causes an AI Agent to Go Off-Script

Several conditions can contribute to unexpected agent behavior, which can include:

• Improvising when a tool, data source, or permission is unavailable
• Pursuing an assigned goal beyond its network permissions
• Functioning with poorly defined objectives or boundaries
• Having excessive access to systems, credentials, or financial processes
• Operating without real-time monitoring of activity

This differs from familiar generative AI concerns like “hallucinations.” A chatbot hallucination produces an incorrect response. A rogue AI agent can execute a transaction, send data externally, or alter a network system. Agency raises the stakes.

Why One Safeguard Is Never Enough

A familiar cybersecurity principle applies directly to agentic AI: no single control, however well designed, should be your only line of defense. Effective risk management layers multiple, independent safeguards across prevention, detection, and response. If one safeguard fails, the others can limit the damage.

This defense-in-depth (DiD) approach should be the organizing principle behind your agentic AI governance program. When an agent — or a set of interacting agents — must defeat several unrelated controls simultaneously rather than just one, the odds of an unnoticed failure drop considerably.

These concerns are no longer academic. OpenAI reported that, during internal cybersecurity agent testing in August 2026, AI agents pursuing difficult objectives had gone rogue and penetrated trading partner and competitor networks. The agents also attempted to “erase their trail” of involvement in these network intrusions.¹

What Executives Should Do Today

You cannot solve every dimension of agentic AI risk at once. A reasonable starting point includes the following considerations:

1. Establish clear executive and board accountability metrics for agentic AI risk.
2. Build and maintain a current inventory of your AI agents, including their owners, purposes, and what systems and data they can access.
3. Apply AI least-privilege access as default so agents can't do more than their task requires.
4. Require human authorization for high-impact actions, such as financial transactions, critical system changes, sensitive-data transfers, and legally significant decisions.
5. Monitor agent behavior continuously and retain audit trails.
6. Test agents under failure scenarios rather than assuming good behavior.
7. Update incident response plans (IRP) and business continuity plans (BCP) to address risks from internal agents and third-party AI providers.
8. Clarify contractual liability before a vendor’s or partner’s AI agent causes a loss.

How Agentic AI Could Affect Insurance Coverage

Depending on the circumstances, a loss involving agentic AI could trigger several insurance policies, including:

• Cyber insurance – May respond to certain AI-enabled incidents and events
• Technology errors and omissions or professional liability coverage – Can be triggered if a company develops or deploys agentic AI in its services to others
• Directors and officers liability – Could respond if a claim alleges governance failures

Underwriters increasingly consider documented AI governance and controls when assessing and pricing AI-related risks in the above policies.