AI Agents Take Over: The Evolution of Legacy IAM

Machine identities are rapidly outnumbering humans in today’s digital landscape, with AI agents taking the lead. This shift is challenging traditional identity models designed for humans, leading to a breakdown at a machine’s pace.

AI agents, a growing class of machine identities, are not only authenticating but also acting autonomously. The surge in AI adoption is evident, with ServiceNow investing billions in security acquisitions, indicating a shift towards identity as the primary control plane for AI risk management.

Research by CyberArk in 2025 confirms the exponential growth of machine identities, surpassing human numbers significantly. Microsoft’s Copilot Studio users have created over a million AI agents in a quarter, showcasing the rapid pace of AI integration. Gartner predicts a significant increase in enterprise breaches linked to AI agent misuse by 2028.

Legacy architectures struggle to cater to the scale of machine identities for several reasons. Cloud IAM processes are sluggish, security reviews do not align with agent workflows, and production demands prioritize speed over accuracy. This results in the prevalence of static credentials, which eventually become vulnerable breach points.

Gartner emphasizes the inadequacy of retrofitting human-centric IAM approaches to fit machine use cases. This mismatch leads to ineffective management of machine identities, posing regulatory risks and security vulnerabilities. The governance gap is evident, with organizations primarily focusing on human identities as privileged users, neglecting the higher access rates of machine identities.

The visibility gap exacerbates the issue, as many machine identities operate outside security oversight. Gartner warns that this fragmented approach compromises IT infrastructure security. Legacy service accounts pose systemic risks by persisting even after the associated workloads cease to exist, leaving orphaned credentials vulnerable to exploitation.

Attackers are increasingly targeting identities over endpoints, leveraging legitimate credentials and identity loopholes for unauthorized access. The rise of agentic AI introduces a new category of machine identity, challenging traditional security frameworks. These AI agents require meticulous credential scoping to adhere to the principle of least privilege.

To address these challenges, organizations should adopt dynamic service identities, ephemeral credentials that reduce the attack surface. Platforms integrating identity, endpoint, and cloud telemetry offer real-time detection of agent misuse. Continuous monitoring, just-in-time access, and auditable delegation chains are essential for securing AI workflows effectively.

2026 is expected to witness a widening gap between AI deployment and security governance, necessitating a proactive approach to machine identity security. Organizations must transition from legacy IAM architectures to dynamic service identity models to combat the escalating threats posed by machine identities. Embracing comprehensive security measures is crucial to stay ahead of the evolving landscape of machine-to-machine attacks.