This publication from the Cloud Security Alliance (CSA) introduces a purpose-built Agentic AI IAM framework that accounts for autonomy, ephemerality, and delegation patterns of AI agents in complex Multi-Agent Systems (MAS).

Why identity for agents matters for startups For a startup, the fastest path to shipping an AI agent is often to give the agent access to whatever it needs and move on. That approach might work for a demo, but it creates risk as soon as the agent can access customer data, modify records, call internal systems, or act across tenants.

IBM helps enterprises secure and govern autonomous AI agents at scale by unifying agent identity, delegation, real-time enforcement, and audit-proof accountability across hybrid/multi-cloud environments.

By adopting ephemeral authentication, fine-grained access control, and Zero Trust principles, we can build a robust identity management approach that secures AI agents while enabling their full potential.

This document defines Agentic Identity and Access Management (Agentic IAM): how to repre-sent, authenticate, authorize, and govern AI agents as verifiable, auditable identities, with lifecycle, context- and intent-aware, risk-based controls comparable to those applied to human and service identities.

Explore best practices for governing AI agents, from data residency laws to corporate compliance, to ensure secure and responsible AI deployment.

There is a potential existential risk for companies that do not adopt agentic capabilities fast enough; however, in order to do that safely, Agent Identity and Access Management (Agent IAM) needs to be solved, making it the defining security topic for 2026.

Discover how AI in identity and access management, powered by agentic AI, is transforming IAM by automating identity controls and enhancing security.

It outlines the resources already available for securing today's agents and presents a strategic agenda to address the foundational authentication, authorization, and identity problems pivotal for tomorrow's widespread autonomous systems.

AI agents operate independently, often without direct human intervention, making their actions difficult to monitor and control through conventional means. Their self-directed nature demands a system that can verify their legitimacy and authorized scope of action without constant human oversight.