As enterprises, the scale of their use of artificial intelligence, a hidden management crisis develops – one that does not prepare much for confrontation: the creation of unrestrained AI agents.
These agents are not speculative. They are already embedded in corporate ecosystems-access to access, exercising permissions, initiating work flows, and even making decisions about critical business. They operate behind the scenes in ticket systems, orchestration tools, SAAS platforms and safety operations. And yet many organizations do not have a clear answer to the most basic questions about management: who owns this agent? What systems can affect? What decisions does he make? What access did he accumulate?
This is a dead point. In identity safety, no one is the greatest risk.
From static scenarios to adaptation agents
Historically, identities other than human-as services bills, scripts and bots-static and predictable. Narrow roles and strictly surprised access were assigned to them, which makes them relatively easy to manage thanks to older control elements, such as the rotation of certificates and the vault.
But Agentic AI introduces a different identity class. These are adaptive, permanent digital actors who learn, reason and act autonomously in various systems. They behave more like employees than machines – they can interpret data, initiate actions and evolve with time.
Despite this change, many organizations are still trying rule this identity ai with outdated models. This approach is insufficient. AI agents do not follow static textbooks. They adapt, recombine the possibilities and extend the boundaries of their project. This liquidity requires a new paradigm of identity management – one rooted in responsibility, monitoring behavior and life cycle supervision.
Property is a control that makes other controls work
In most identity programs, ownership is treated as administrative metadata – formality. But when it comes to AI agents, property is not optional. This basic control allows responsibility and security.
Without clearly, critical functions break down. Permissions are not checked. The behavior is not monitored. Life cycle limits are ignored. And in the case of an incident, no one is responsible. Security controls, which seem solid on paper, become irrelevant in practice if no one is responsible for identity.
Property must be surrender. This means assigning a named human flight attendant to every AI identity – some who understand the purpose, access, behavior and influence of the agent. Property is a bridge between automation and responsibility.
Real risk of ambiguity
Risk are not abstract. We have already seen real examples in which AI agents implemented in customer service environments showed unexpected behavior-generation of hallucinated reactions, escalating trivial problems or displaying a language incompatible with the brand's guidelines. In such cases, the systems operated as intended; The problem was interpretative, not technical.
The most dangerous aspect in these scenarios is the lack of clear responsibility. When no person is responsible for the decisions of the AI agent, organizations remain exposed – not only to operational risk, but reputational and regulatory consequences.
This is not a dishonest problem with AI. This is an unsuccessful identity problem.
Illusion of common responsibility
Many enterprises are assuming that the property of artificial intelligence can be served at the team level – Devops will manage service accounts, engineering will supervise integration, and the infrastructure will be the owner of the implementation.
AI agents are not limited to one team. They are created by developers, implemented via the SAAS platforms, operate in the scope of HR and security data and affect work flows in business units. This interfunctional presence creates diffusion-diffusion management leads to failure.
Common property too often translates into a lack of property. AI agents require clear responsibility. Someone must be replaced and responsible – not as technical contact, but as an owner of operational control.
Quiet privilege, gathered risk
AI agents are a unique challenge, because their risk trace expands quietly over time. Narrow fission is often activated – perhaps reserve support or supporting support tickets – but their access tends to grow. Additional integrations, new training data, wider goals … And no one stops assessing again whether the extension is justified or monitored.
This quiet drift is dangerous. AI agents not only have privileges – they rule them. And when the systems are undertaken by systems that no one does, the probability of non -social or improper use increases dramatically.
This is an equivalent employment of the contractor, providing extensive access to the building and never conducting a performance review. Over time, this contractor may begin to change the company's rules or existing systems in which they have never accessed. The difference is that human employees have managers. Most AI agents do not.
Regulatory expectations are evolving
What began as a security gap quickly becomes a matter of compliance. Regulatory framework-from the EU AI Act for local legal provisions regulating automated decision making-they want to demand identification, the possibility of explaining and human supervision for AI systems.
These expectations are depleted directly on their own. Enterprises must be able to show who approved the implementation of an agent who manages his behavior and who is responsible in the event of damage or improper use. Without the named owner, the enterprise can not only face the operational exhibition – it can be considered neglect.
Model of responsible management
Effective ruling AI agents means integrating them with existing identity management frames and access to the same rigorous used for privileged users. This includes:
- Assigning a named person to each AI identity
- Monitoring of behavior in terms of signs of drift, escalation of privileges or anomal activities
- Enforcement of life cycle rules with expiration dates, periodic inspections and depression triggers
- Validation of ownership in control gates, such as implementation, change of rules or modification of access
This is not only the best practice – this is a required practice. The property must be treated as a live control area, not a check box.
Have it before he has you
AI agents are already here. They are embedded in your work flows, analyze data, make decisions and act with growing autonomy. The question no longer sounds whether you are using AI agents. You are. The question is whether your management model has caught up with them.
The path forward begins with property. Without it, any other control becomes cosmetic. Thanks to this, organizations gain the foundation they need to safely, safely, and adapt to their risk tolerance.
If we do not have an AI identity acting on our behalf, we have successfully submitted control. In cyber security, control is everything.
