If an AI agent makes a critical decision in your system, who is responsible?

As AI agents become more autonomous, traditional Identity and Access Management (IAM) frameworks face new challenges. These agents are evolving beyond simple service accounts into distinct identities, yet they lack HR-driven lifecycles and defined roles. This marks the rise of what some call the "agentic enterprise.

Treating these agents as true identities means addressing three key challenges:

⚙️ Lifecycle Management Without HR Systems  AI agents are created by development teams or CI/CD pipelines on platforms like Vertex AI, rather than through systems like Workday. Who signs off on their identity? And who retires them once a project wraps up? Without a central governance model, these agents risk accumulating unchecked—a phenomenon sometimes referred to as "Shadow AI.

💡 Defining "Least Privilege" for Autonomous Actions   Rather than fitting into traditional roles, agents are task-driven. This calls for moving away from static permissions towards dynamic, intent-based policies that grant only the access necessary for specific actions. Such precision is essential to minimise risk and avoid privilege creep.

🔒 Audit and Compliance Attribution   When an agent interacts with data, responsibility can be murky—is it the data scientist who designed it? The business owner who deployed it? Effective audit trails should link agent actions back to the humans or processes behind them, along with the intended outcomes.

Addressing these challenges is fundamental to adopting AI-driven automation securely. Okta’s blueprint and the forthcoming "Okta for AI Agents" platform offer a framework to manage this new identity category, including discovery, governance, and a "universal logout" capability to swiftly reduce risk.

How are your teams managing governance around "Shadow AI" in practice?

#IdentitySecurity #AI #IAM #Okta #CyberSecurity #ZeroTrust