A guide to agentic AI security

This episode of Techsplainers explores why AI agents require a different security mindset than other nonhuman identities. Agentic AI behaves less like traditional software and more like a digital insider capable of making decisions, calling tools and taking action across systems. The episode walks through four practical security principles organizations can apply today. It explains why human oversight remains essential, how containment strategies such as sandboxing and least privilege can reduce risk, why the full machine learning lifecycle must be secured against threats like data poisoning, and how the action layer introduces new concerns around APIs, function calls and prompt injection. Along the way, the discussion connects benefits, risks and real-world governance considerations, helping listeners understand why trust in AI agents depends not just on model quality, but on access controls, monitoring and carefully designed operational guardrails. Find more information at https://www.ibm.com/think/insights/agentic-ai-security Find more episodes https://www.ibm.biz/techsplainers-podcast Narrated by Alice Gomstyn