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This episode addresses the critical security shift required as AI systems evolve from passive tools into autonomous agents capable of executing actions on behalf of users. The discussion centers on the DPACT framework, which proposes replacing static token-based access with a dynamic model based on delegation, policy, auditability, context, and time to prevent unauthorized agent behavior.
Podcast: Securing AI Agents: Identity, Authorization, and the DPACT Framework

In this episode, Sahil Agarwal talks about the critical challenges of identity, authorisation, and security in the age of AI agents. Sahil introduces the DPACT framework (Delegation, Policy, Auditability, Context, and Time) as a blueprint for building responsible, guardrailed agentic systems, moving away from simple token-based access toward bounded, delegated authority. By Sahil Agarwal
Olimpiu Pop: Hello, everybody. I'm Olimpiu Pop, an InfoQ editor. And today we are discussing, well, probably not surprising, about AI and agents and the way how they act in our name or they should act in our name. And we are having that conversation with Sahil Agarwal. I will not add too many accolades on his shoulder because he can do it on his own. Sahil Agarwal: Thank you so much, Olimpiu, for having me here. My name is Sahil Agarwal and I am an area engineering leader at the leading cloud collaboration and content platform company where I'm leading the identity and authorization stack of the company. And basically what we're trying to build is also very closely related to how do you separate human identities from agents? And I think, through this conversation, we can unwrap a lot about what agent identity means, what are the pitfalls of it, and how should we think about it if you are somebody who is building agents for either your own fund or for your enterprise? Olimpiu Pop: Based on our initial conversation, we touched a bit on how the things are working. I think probably towards the beginning of…
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