The AI accountability gap

This episode of Techsplainers explores the AI accountability gap and why it has become a defining governance challenge for runtime security. As AI agents move beyond simple automation and begin executing business actions across enterprise systems, organizations must answer harder questions about authority, approval and responsibility. The episode explains how agentic AI changes the identity model by introducing continuous action, delegated authority, short-lived identities and decision-making across trust boundaries. It examines why traditional identity and access management approaches fall short when applied to autonomous agents, leading to over-privileged access, invisible delegation, weak runtime enforcement and poor incident reconstruction. Listeners learn why the real shift is from treating identity as a login problem to treating identity as an authority problem. The discussion also covers the need to connect identity, policy, secrets and runtime enforcement so organizations can prove who approved an action, under what conditions and for how long. Along the way, the episode looks at audit pressure, regulatory expectations and the practical security risks of deploying agents without clear accountability controls. Find more information at https://www.ibm.com/think/insights/accountability-gap-autonomous-ai Find all the Techsplainers episodes here https://www.ibm.biz/techsplainers-podcast Narrated by Anna Gutowska