A recent conversation among information system directors focused on the idea of democratizing AI by giving employees access to AI agents—software programs that can perform tasks, interact with systems, and even make decisions. These agents are being integrated into business processes, allowing teams to create their own assistants tailored to specific needs. However, discussions with information system security officers revealed growing concerns about managing these AI agents, particularly regarding who can use them, what permissions they should have, and how to ensure their actions are traceable and accountable. A central question has emerged: should AI agents be treated like human users with standard access controls, or do they represent a new kind of digital identity that requires unique governance? According to research firm Gartner, as many as 40 percent of enterprise applications could include AI agents by 2026, compared to less than 5 percent in 2025. This rapid growth highlights the increasing role of AI in areas such as business operations, finance, legal processes, and more. AI agents can perform a wide range of tasks, from accessing multiple systems to delegating actions to other agents. While granting them the same rights as human users may be acceptable for simple, short-term tasks, it can pose significant risks if their activities are prolonged. For this reason, AI agents should be treated as workload identities—temporary, limited, and short-lived. Unlike traditional user identities, AI agents can adapt their behavior and expand their capabilities based on the tools or modules they use. This means they are not only identities that need to be authenticated but also ones that can act independently. The challenge now is not just about access, but about authority. When an AI agent accesses a file, it must be clear what it is allowed to do with it—analyze, transmit, modify, or trigger a process. Technical access does not automatically mean the agent has the authority to make all decisions related to that access. Many current systems can identify who has access, but they are less effective at determining what that access entails. This gap increases risks, especially when agents delegate tasks, potentially creating a chain of access that extends beyond what was intended. Managing non-human identities like AI agents is more complex than managing human users. Unlike a person, an AI agent does not have a clearly defined function or a direct point of contact. If an incident occurs, teams may need to reconstruct the agent's purpose, the systems it accessed, and the decisions it could have made. As a result, governance models must evolve to monitor AI agents in real time, block risky access, and revoke unnecessary permissions. These efforts are taking place against the backdrop of new European regulations like the NIS2 directive and the AI Act, which emphasize stronger security, risk management, and oversight of AI systems. The management of AI agent identities now goes beyond traditional Identity and Access Management (IAM) systems. To address these challenges, companies must ensure their directories are reliable, inventory all non-human identities, and apply the principle of least privilege—granting only the permissions necessary for a specific task. Each AI agent should have a responsible party, a defined purpose, and limited access for a limited time. Multifactor authentication (MFA) should be used to protect accounts, tokens, and sessions from phishing attacks. Companies should also plan for the possibility of an agent's credentials being compromised by using segmentation, temporary access, and continuous monitoring. These measures are not exclusive to AI agents but align with broader governance principles for all digital identities. However, with AI agents, these considerations cannot be postponed.