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AI agents behave differently from traditional users and applications. They operate continuously, act on their own logic, and often interact with multiple systems in a single workflow. This changes how access and identity risks show up.
AI agents are designed to run independently. Once deployed, they can access APIs, services, and data without human approval for each action. Without strict access controls, this autonomy can lead to unintended or excessive access.
To avoid breaking workflows, AI agents are often granted broad permissions. These privileges are rarely reviewed, increasing the impact of misconfigurations or misuse.
Many AI agents authenticate using long-lived API keys or shared secrets. These credentials are hard to rotate, difficult to trace, and commonly reused across systems.
Security teams often lack a complete inventory of AI agents, their access rights, and their activity. When incidents occur, it becomes difficult to determine which agent was responsible and why.
Most IAM platforms focus on users and basic service accounts. They do not account for agentic behavior such as decision-making, action chaining, and continuous system interaction.
As AI agents evolve, they often receive additional permissions to support new tasks. Without lifecycle governance, access accumulates quietly, increasing risk with every change.
AI agents authenticate using secure methods such as tokens, certificates, or API-based authentication. This avoids shared credentials and allows each agent to be verified before accessing systems.
Define fine-grained authorization policies that control which APIs, applications, or data an AI agent can access. Permissions are tied to the agent’s role and purpose, not convenience.
Manage AI agents, bots, and service accounts as identities within a single system. This provides a consistent way to assign access, track usage, and maintain ownership.
Provision AI agent identities, rotate credentials, and revoke access when agents change or are retired. This prevents outdated agents from retaining unnecessary permissions.
Maintain detailed logs of authentication and access activity for AI agents. These records support security reviews, investigations, and compliance requirements.
AI agents are registered and managed as first-class identities rather than unmanaged API keys or background services. This creates clear ownership, traceability, and control for every agent operating in the environment.
Access decisions are tied to agent identity, role, and context. AI agents can be restricted to specific APIs, applications, or data sources, reducing unnecessary access and limiting exposure.
The same access policies, review processes, and audit controls apply to users, service accounts, and AI agents. This removes gaps between human and automated access management.
Security teams gain visibility into which AI agents exist, what they can access, and how their permissions change over time. Access can be updated or revoked as agents evolve or are retired.
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