In AI-native environments, identity isn’t just about people, it’s about AI agents, models, and automated systems accessing sensitive data, APIs, and cloud resources. The traditional human-centric IAM models don’t cover the dynamic, agent-driven interactions now happening inside AI pipelines.

This talk challenges security leaders, IAM architects, and DevSecOps practitioners to rethink identity and data security as AI shifts from tool to autonomous actor.

I will cover:

How AI agents, fine-tuned models, and autonomous workflows create new identity risks

Machine-to-machine identity management in AI-native environments

Preventing privilege creep, identity sprawl, and shadow access in AI-generated pipelines

Applying Zero Trust and Identity Governance to AI agents and data flows

How to embed Data Access Governance and JIT identity controls in your AI SDLC

Who Should Attend:

IAM & Identity Governance Professionals — to understand AI-driven identity risks

Data Security & Compliance Leaders — to learn how AI-native pipelines impact data exposure

DevSecOps Engineers & Architects — to embed identity and access controls in AI workflows

AI Practitioners & ML Engineers — to grasp the security implications of model access and agent permissions

Risk & Governance Teams — to align AI identity governance with regulatory compliance and Zero Trust strategies