AI agents are accelerating the way we get work done. Schedule meetings, access data, trigger workflows, write code, and take actions in real-time to boost productivity beyond human speed across your enterprise.
And there comes a moment when all security teams are finally faced with the following question:
“Wait… who approved this?”
Unlike users and applications, AI agents are often rapidly deployed, widely shared, and given broad access privileges, making ownership, authorization, and accountability difficult to track. What was once an easy question is now surprisingly difficult to answer.
AI agents disrupt traditional access models
AI agents are more than just a type of user. They are fundamentally different from humans and traditional service accounts, and these differences disrupt existing access and authorization models.
Human Access is built on clear intent. Privileges are tied to roles, reviewed regularly, and limited by time and context. Although service accounts are not human, they are typically purpose-built, narrow in scope, and tied to specific applications or functionality.
AI agents are different. They operate with delegated authority and can act on behalf of multiple users or teams without requiring ongoing human involvement. Once approved, they are autonomous and persistent, often operating across systems and moving between different systems and data sources to complete tasks end-to-end.
In this model, delegated access not only automates a user’s actions, but also extends those actions. While human users are constrained by explicitly granted permissions, AI agents are often given broader and more powerful access rights to operate effectively. As a result, the agent can perform actions that the user himself is not authorized to do. If permission exists, the agent can perform the action. An agent can perform an action even if the user did not intend to perform the action or was unaware that the action was possible. As a result, agents can cause exposure, sometimes accidentally, sometimes implicitly, but always legally from a technical point of view.
This is how access drift occurs. Agents silently accumulate authority as their scope expands. As integrations are added, roles change, and teams come and go, agent access remains. These can be powerful intermediaries with broad and long-term powers, often without clear ownership.
No wonder existing IAM assumptions break down. IAM assumes clear identities, defined owners, static roles, and periodic reviews that address human behavior. AI agents do not follow such patterns. They do not fit neatly into categories of users or service accounts, operate continuously, and their effective access is defined by how they are used, not by how they were originally authorized. Without rethinking these assumptions, IAM will fail to recognize the real risks posed by AI agents.
Three types of AI agents in the enterprise
In enterprise environments, not all AI agents take on the same risks. Risks vary depending on agent ownership, scope of agent use, and access rights, resulting in different categories with significantly different security, accountability, and blast radius implications.
Personal agent (user-owned)
Personal agents are AI assistants that individual employees use to assist with their daily tasks. They draft content, summarize information, schedule meetings, or assist with coding, always in the context of one user.
These agents typically operate within the privileges of the user who owns the agent. These accesses are inherited rather than extended. If the user loses access, the agent also loses access. Due to clear ownership and limited range, the explosion radius is relatively small. Personal agents are the easiest to understand, manage, and remediate because the risks are tied directly to individual users.
Third-party agent (vendor-owned)
Third-party agents are built into SaaS and AI platforms that vendors offer as part of their products. Examples include AI capabilities built into CRM systems, collaboration tools, and security platforms.
These agents are managed through vendor management, contracts, and shared responsibility models. There may be limited visibility into how customers work internally, but responsibilities are clearly defined. In other words, the vendor owns the agent.
The main concern here is AI supply chain risk, or trusting that vendors are adequately protecting their agents. However, from a corporate perspective, ownership, approval pathways, and responsibilities are usually well understood.
Organization agent (shared and often unowned)
Organization agents are deployed internally and shared across teams, workflows, and use cases. They automate processes, integrate systems, and work on behalf of multiple users. To be effective, these agents are often granted broad and persistent permissions that extend beyond single-user access.
This is where the risks are concentrated. Organizational agents often don’t have a clear owner, single approver, or defined lifecycle. If something goes wrong, it’s unclear who is responsible or even fully understands what the agent can do.
As a result, organizational agents present the highest risks and the greatest explosive scope, not because they are malicious, but because they operate at scale and without clear accountability.
Agent authentication bypass issue
As explained in the article, Agents that Create Authorization Bypass Paths, AI agents not only perform tasks, but also act as access intermediaries. Instead of users interacting directly with the system, agents use their own credentials, tokens, and integrations to act on your behalf. This changes where authorization decisions are actually made.
When an agent acts on behalf of an individual user, it can provide the user with access and functionality beyond the user’s authorized privileges. Users who cannot directly access certain data or perform certain actions can still trigger agents that can. Agents act as proxies, enabling actions that users would never be able to perform on their own.
These actions are technically allowed and the agent has valid access. However, it is contextually unsafe. With traditional access control, the credentials are legitimate and no alert is triggered. This is the core of agent authentication bypass. Access is granted correctly, but it is used in a way that the security model is not designed to handle.
Rethinking risk: What needs to change?
Securing AI agents requires a fundamental shift in how risk is defined and managed. Agents can no longer be treated as an extension of the user or as an automated process in the background. These should be treated as sensitive and potentially high-risk entities with their own identities, privileges, and risk profiles.
This starts with clear ownership and accountability. Every agent must have a clear owner who is responsible for its purpose, scope of access, and ongoing review. Without ownership, authorization is meaningless and risk remains unmanaged.
Importantly, organizations also need to map how users interact with agents. It’s not enough to understand what agents have access to. Security teams need visibility into which users can invoke agents, under what conditions, and with what effective privileges. Without this user-agent connectivity map, the agent silently becomes an authentication bypass path that allows users to indirectly perform actions that they are not allowed to perform directly.
Finally, organizations need to map agent access, integration, and data paths throughout the system. Only by correlating User → Agent → System → Action can teams accurately assess blast radius, detect misuse, and reliably investigate suspicious activity when something goes wrong.
Cost of AI agents for unmanaged organizations
Uncontrolled AI agents in organizations turn productivity gains into overall risks. These agents are shared across teams, granted broad and persistent access, and operate without clear ownership or responsibility. Over time, they are used for new tasks, creating new execution paths and making their actions harder to track and contain. If something goes wrong, there is no clear owner to respond to, fix, or even understand the entire blast radius. Without visibility, ownership, and access control, an organization’s AI agents become one of the most dangerous and least managed elements in a company’s security environment.
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