As artificial intelligence continues to transform enterprise operations, one overlooked—but increasingly critical—gap in identity governance is emerging: ownership of non-human identities, particularly those belonging to autonomous AI agents.
These AI-driven identities are no longer theoretical. They are real, operational, and embedded within some of the most sensitive workflows in modern organizations. They provision access, automate entitlements, initiate workflows, and—in some cases—make irreversible business decisions. Yet few enterprises can say with confidence who owns these agents, what privileges they hold, or how they behave over time.
And in cybersecurity, what no one owns is precisely where the greatest risk resides.
From Scripts to Self-Governed Systems
Non-human accounts have long existed in the enterprise: service accounts, bots, API clients, and orchestration scripts. Historically, these identities have been static, deterministic, and narrowly scoped. Their governance has reflected that legacy—largely focused on credential rotation, vaulting, and occasional access reviews
But the emergence of agentic AI—autonomous digital entities capable of learning, reasoning, and decision-making—renders those governance models inadequate. AI identities are not static. They adapt. They reconfigure. They interact with APIs, applications, users, and even other agents to achieve objectives. And they do so with varying degrees of visibility, interpretability, and alignment.
“AI agents are not tools. They’re actors,” says Brandon Traffanstedt, Field CTO at CyberArk. “They operate with a blend of autonomy, reasoning, and persistence. And that makes them fundamentally different from the scripts and service accounts we’ve governed in the past.”
In short, AI agents behave more like employees than machines. But they are rarely treated with the same level of scrutiny, accountability, or oversight.
Ownership as a Security Control
In traditional identity governance, ownership is often seen as administrative metadata—a name in a column, a field in a CMDB, or a contact email for escalation. But in the context of AI identities, ownership must evolve into an operational control.
Ownership is the mechanism through which responsibility is assigned, reviewed, and enforced. It connects the identity to a human steward—someone who understands what the agent does, how it operates, what credentials it uses, and how its behavior is evaluated over time.
Without clear ownership, organizations lose their ability to:
- Audit entitlements
- Monitor behavioral drift
- Enforce lifecycle boundaries
- Respond to incidents
- Meet regulatory expectations for explainability and accountability
“Without ownership, your controls are cosmetic,” says Christina Richmond, Principal Analyst at Richmond Advisory Group. “You can vault credentials, scan for anomalies, and enforce MFA, but if no one is responsible for the agent’s behavior, you’ve built a house with no front door.”
Put simply: If no one owns the agent, no one is responsible when it fails.
Case in Point: The Agentforce Incident
Consider the example of Agentforce, a generative AI assistant released by Salesforce in 2024. Designed to accelerate case resolution in customer service environments, Agentforce was integrated into enterprise support workflows with access to tickets, user history, and interaction summaries.
In several early deployments, organizations reported erratic behavior. The agent hallucinated issue summaries, escalated trivial cases, and provided contradictory advice. In some instances, it generated language that conflicted with internal tone guidelines or violated procedural policy.
The most alarming aspect? These actions were technically successful. The AI hadn’t malfunctioned—it had operated within its defined scope. The problem was not execution, but interpretation. And critically, no individual within the affected organizations had been designated as the owner of the agent’s behavior.
This wasn’t a rogue system. It was an unowned one.
The Myth of Functional Ownership
In many organizations, the prevailing belief is that ownership can be handled “at the team level.” DevOps teams manage service accounts. Application teams govern integrations. Infrastructure teams oversee cloud identities. That may suffice for static workloads, but it falls apart with AI agents.
AI agents typically exist at the intersection of multiple domains:
- Developed by engineering
- Deployed through a SaaS provider
- Acting on HR, security, or IT data
- Impacting business workflows outside the development loop
This diffusion creates ambiguity. And in governance, ambiguity is a control failure.
“Shared ownership is often the illusion of accountability,” says Kristin Buckley, Principal Strategist at SPHERE. “In practice, it means everyone assumes someone else is watching.”
When AI identities are shared across functions, embedded in vendor platforms, or created through orchestration layers, functional ownership becomes insufficient. Someone must be explicitly named—not as a point of contact, but as a control owner.
Silent Access, Persistent Drift
AI agents pose a unique challenge because they are capable of accumulating privilege and evolving behavior silently.
In many organizations, agents are deployed with initial access intended for narrow use cases—provisioning accounts, triaging requests, summarizing security alerts. But over time, agents are updated, trained on new data, connected to additional tools, or handed broader scopes “temporarily.” Without strong ownership, these changes go unreviewed.
One identity professional likened it to a new hire who never receives a performance review, never gets offboarded, and starts writing new policies after a few months. The difference? An employee has a manager. Most AI agents do not.
And when access decisions are made by entities no one monitors, risk becomes inevitable.
Regulatory Pressure Is Coming
The issue of AI ownership is not just a security concern. It is increasingly a regulatory one.
New frameworks such as the EU AI Act, NYC Local Law 144, and emerging U.S. federal guidance on AI governance all emphasize the need for accountability, transparency, and human oversight. These are not abstract values—they map directly to ownership.
Regulators are beginning to expect that:
- AI-generated actions are traceable
- Decision-making processes are explainable
- Responsibility for automated behavior is clearly assigned
- Organizations can demonstrate who approved access, who maintained it, and who is responsible for responding to misuse
In this climate, the absence of ownership may be interpreted not as oversight—but as negligence.
What Good Looks Like
To effectively manage AI identities, organizations must integrate ownership into their existing IAM and IGA frameworks. This includes:
- Assigning named, individual owners to every AI identity, including those embedded in SaaS or third-party tools
- Monitoring agent behavior for drift, privilege accumulation, or anomalous decision patterns
- Enforcing lifecycle policies, including sunset dates, periodic reviews, and access deprovisioning triggers
- Validating ownership at key control points—such as onboarding, policy change, or credential renewal
In mature programs, ownership isn’t treated as a static attribute. It’s governed as a control surface—just like entitlements, credentials, and MFA.
Ownership Is Operational, Not Optional
Organizations must begin treating ownership as operational hygiene—especially in environments where AI agents act with autonomy.
Too often, security teams only discover ownership gaps after an incident. A credential is misused. A workflow fails. A support agent is replaced by an LLM with no review process in place. These are not hypothetical scenarios. They are already happening. And they will continue to accelerate as AI capabilities are embedded deeper into operational infrastructure.
“It’s easy to say that AI will transform identity. But the inverse is also true,” says Traffanstedt. “The way you govern identity—especially unowned identity—will determine whether AI becomes a force multiplier or a threat vector.”
In this moment, the question isn’t whether AI agents will become part of your environment. They already are. The question is whether your governance structures will adapt fast enough to secure them.
