As enterprises scale their use of artificial intelligence, a hidden governance crisis is unfolding—one that few security programs are prepared to confront: the rise of unowned AI agents.
These agents are not speculative. They’re already embedded across enterprise ecosystems—provisioning access, executing entitlements, initiating workflows, and even making business-critical decisions. They operate behind the scenes in ticketing systems, orchestration tools, SaaS platforms, and security operations. And yet, many organizations have no clear answer to the most basic governance questions: Who owns this agent? What systems can it touch? What decisions is it making? What access has it accumulated?
This is the blind spot. In identity security, what no one owns becomes the biggest risk.
From Static Scripts to Adaptive Agents
Historically, non-human identities—like service accounts, scripts, and bots—were static and predictable. They were assigned narrow roles and tightly scoped access, making them relatively easy to manage with legacy controls like credential rotation and vaulting.
But agentic AI introduces a different class of identity. These are adaptive, persistent digital actors that learn, reason, and act autonomously across systems. They behave more like employees than machines—able to interpret data, initiate actions, and evolve over time.
Despite this shift, many organizations are still attempting to govern these AI identities with outdated models. That approach is insufficient. AI agents don’t follow static playbooks. They adapt, recombine capabilities, and stretch the boundaries of their design. This fluidity requires a new paradigm of identity governance—one rooted in accountability, behavior monitoring, and lifecycle oversight.
Ownership Is the Control That Makes Other Controls Work
In most identity programs, ownership is treated as administrative metadata—a formality. But when it comes to AI agents, ownership is not optional. It is the foundational control that enables accountability and security.
Without clearly defined ownership, critical functions break down. Entitlements aren’t reviewed. Behavior isn’t monitored. Lifecycle boundaries are ignored. And in the event of an incident, no one is responsible. Security controls that appear robust on paper become meaningless in practice if no one is accountable for the identity’s actions.
Ownership must be operationalized. That means assigning a named human steward for every AI identity—someone who understands the agent’s purpose, access, behavior, and impact. Ownership is the bridge between automation and accountability.
The Real-World Risk of Ambiguity
The risks are not abstract. We’ve already seen real-world examples where AI agents deployed into customer support environments have exhibited unexpected behaviors—generating hallucinated responses, escalating trivial issues, or outputting language inconsistent with brand guidelines. In these cases, the systems worked as intended; the problem was interpretive, not technical.
The most dangerous aspect in these scenarios is the absence of clear accountability. When no individual is responsible for an AI agent’s decisions, organizations are left exposed—not just to operational risk, but to reputational and regulatory consequences.
This is not a rogue AI problem. It’s an unclaimed identity problem.
The Illusion of Shared Responsibility
Many enterprises operate under the assumption that AI ownership can be handled at the team level—DevOps will manage the service accounts, engineering will oversee the integrations, and infrastructure will own the deployment.
AI agents don’t stay confined to a single team. They are created by developers, deployed through SaaS platforms, act on HR and security data, and impact workflows across business units. This cross-functional presence creates diffusion—and in governance, diffusion leads to failure.
Shared ownership too often translates into no ownership. AI agents require explicit accountability. Someone must be named and responsible—not as a technical contact, but as the operational control owner.
Silent Privilege, Accumulated Risk
AI agents pose a unique challenge because their risk footprint expands quietly over time. They’re often launched with narrow scopes—perhaps handling account provisioning or summarizing support tickets—but their access tends to grow. Additional integrations, new training data, broader objectives… and no one stops to reevaluate whether that expansion is justified or monitored.
This silent drift is dangerous. AI agents don’t just hold privileges—they wield them. And when access decisions are being made by systems that no one reviews, the likelihood of misalignment or misuse increases dramatically.
This is equivalent to hiring a contractor, giving them broad building access, and never conducting a performance review. Over time, that contractor might start changing company policies or touching systems they were never meant to access. The difference is: human employees have managers. Most AI agents don’t.
Regulatory Expectations Are Evolving
What began as a security gap is quickly becoming a compliance issue. Regulatory frameworks—from the EU AI Act to local laws governing automated decision-making—are beginning to demand traceability, explainability, and human oversight for AI systems.
These expectations map directly to ownership. Enterprises must be able to demonstrate who approved an agent’s deployment, who manages its behavior, and who is responsible in the event of harm or misuse. Without a named owner, the enterprise may not just face operational exposure—it may be found negligent.
A Model for Responsible Governance
Governing AI agents effectively means integrating them into existing identity and access management frameworks with the same rigor applied to privileged users. That includes:
- Assigning a named individual to every AI identity
- Monitoring behavior for signs of drift, privilege escalation, or anomalous actions
- Enforcing lifecycle policies with expiration dates, periodic reviews, and deprovisioning triggers
- Validating ownership at control gates, such as onboarding, policy change, or access modification
This isn’t just best practice—it’s required practice. Ownership must be treated as a live control surface, not a checkbox.
Own It Before It Owns You
AI agents are already here. They’re embedded in your workflows, analyzing data, making decisions, and acting with increasing autonomy. The question is no longer whether you’re using AI agents. You are. The question is whether your governance model has caught up to them.
The path forward begins with ownership. Without it, every other control becomes cosmetic. With it, organizations gain the foundation they need to scale AI safely, securely, and in alignment with their risk tolerance.
If we don’t own the AI identities acting on our behalf, then we’ve effectively surrendered control. In cybersecurity, control is everything.
He specializes in identity hygiene, leveraging AI-driven technologies to automate and secure identities at scale. His professional journey has included leadership roles at prominent financial institutions, such as Barclays, Lehman Brothers, and Neuberger Berman, where he honed his skills in complex, highly regulated environments.
