AI agents are becoming enterprise identities—and a new major attack vector

Ensuring we secure AI agents with the same rigor and accountability as we do humans is critical to effectively embrace this new era of AI adoption.

Agentic AI is quickly transforming organizations, moving enterprise AI beyond systems that simply generate content to systems that autonomously take action. They retrieve data, initiate workflows, write and deploy code, and take action with minimal human intervention. Across the enterprise, these agents already file expense reports, triage tickets, draft contracts and query critical databases. Many organizations are deploying their own native or third-party vendor AI agents faster than they are securing their access.

This shift from AI as a tool to AI as an actor fundamentally reshapes the security model. For an AI agent to act, it needs access to an organization’s data and systems. And if an agent has access, it has identity. That reality changes everything. We are no longer just managing tools; we are governing autonomous actors inside the enterprise. Ensuring we secure AI agents with the same rigor and accountability as we do humans is critical to effectively embracing this new era of AI adoption.

The risk of unsecured agentic AI

The risks are no longer theoretical. In one case, security researchers were able to manipulate Doctronic’s AI system, which powers Utah’s prescription refill bot. AI red-teaming firm Mindgard tricked the system into tripling an OxyContin dose. This wasn’t a failure of intelligence, but a failure of control.

The reality is that every AI agent expands your identity attack surface. If AI agents are granted broad, static access, the consequences can be severe. These include:

• Overprovisioning: In early deployments, teams often grant agents expansive permissions just to make it work. This may accelerate experimentation, but it directly violates least privilege principles and creates a wide-open path for threat escalation during a breach.
• Credential Sprawl: Hard-coded secrets, shared service accounts and unmanaged tokens become soft targets for attackers.
• Hindered Decision-Making: Without traceable identity attribution, it becomes difficult to answer basic questions: Who initiated this transaction? Why was this record modified? Was it a human or an AI acting on their behalf? In a crisis, that ambiguity delays response and amplifies impact.
• Privilege Escalation: If an AI agent can chain actions across systems, a small initial compromise can quickly expand.
• Speed: AI agents operate far faster than human attackers or human defenders. Misconfigured access can lead to automated misuse at scale. The combination of speed and autonomy turns minor misconfigurations into enterprise-wide incidents.

What securing AI agents actually requires

Securing agentic AI isn’t about slowing innovation; it’s about enabling it to scale safely. The organizations that get this right won’t move slower; they’ll move with confidence. Here are five principles every enterprise should adopt for this approach:

1. Assign each agent a unique, verifiable identity.

   If you cannot uniquely identify an agent in your logs, you cannot secure it. Each AI agent should have its own distinct identity, never a shared service account. That identity must be authenticated using strong, modern mechanisms such as short-lived credentials, certificate-based authentication or workload identity federation.

   The goal is clear attribution. Every action must be traceable to a specific agent instance and tied to a defined purpose. Without this, accountability breaks down before security even begins.

2. Enforce least privilege dynamically

   Agentic AI access should be scoped to the minimal set of permissions required for a defined task. Better yet, those permissions should be granted just-in-time and revoked automatically after use.

   Static, long-lived permissions are particularly dangerous for AI agents because their capabilities often expand over time. What started as “read-only reporting” may evolve into workflow execution.

   Adopt policy-based access controls that evaluate context (e.g., task, environment or data sensitivity) before granting access. Access should be ephemeral, contextual and continuously evaluated.

3. Govern the agent life cycle

   Human identities go through onboarding, role changes and termination. AI agents require similar life cycle governance.

   Always determine who approves agent creation, what systems it’s authorized to access, who owns it from a business perspective and when it should be decommissioned. Without life cycle controls, dormant agents become silent liabilities.

4. Implement continuous monitoring and behavioral analytics

   Because AI agents act autonomously, static rules aren’t enough. Security teams must monitor for behavioral anomalies such as accessing systems outside normal patterns, attempting high volumes of transactions and calling APIs that are inconsistent with their defined purpose.

   Automated detection, policy enforcement and remediation are essential. At scale, only automation can govern automation.

5. Establish clear human accountability

   An AI agent may execute an action, but a human must remain accountable for it. Every agent should have a named business owner, technical owner and defined escalation paths.

   Accountability prevents the dangerous diffusion of responsibility that often accompanies automation. AI should not become a scapegoat for poor governance. Ownership must be explicit and visible at every layer.

The regulatory horizon: It's closer than you think

While governments rapidly develop AI regulations mostly focused on bias and transparency, access control and auditability will inevitably become central themes. Regulators will undoubtedly want to know how appropriate access is managed, how misuse is prevented and whether organizations can produce audit trails of automated decisions.

Many executives assume agentic AI governance is a next-year issue, but it isn’t. AI co-pilots are already embedded in productivity suites, autonomous workflows are being integrated into finance and customer service, and DevOps teams are experimenting with AI-driven code changes. In most enterprises, AI agents already have access to sensitive systems, often outside centralized IAM oversight.

Security leaders should conduct an immediate inventory to determine where AI agents are operating, what credentials they use, the systems they can access and whether they’re governed by other identities. You cannot secure what you cannot see, and most organizations are still operating without that visibility.

The organizations that I believe will lead in the AI era are those that move boldly with guardrails. Agentic AI will reshape how we work, serve customers and compete. But if we fail to secure its access, we risk undermining the very trust that makes innovation possible. The mandate for leaders is clear: Treat AI agents as first-class identities, govern them with precision, and embed accountability into every action.

This article was originally published on Forbes.com as part of the Forbes Technology Council. Read the full article here.