AI agents have emerged as a rapidly growing class of machine identities, operating with minimal governance and the ability to not only authenticate but also take action. The significant investments made by companies like ServiceNow in security acquisitions signal a shift towards making identity the control plane for managing AI risk in the enterprise.
Research by CyberArk in 2025 confirms the exponential growth of machine identities, surpassing human identities by a wide margin. Microsoft’s Copilot Studio users have witnessed a 130% increase in the creation of AI agents in a single quarter, as predicted by Gartner that a quarter of enterprise breaches by 2028 will be attributed to AI agent misuse.
The Limitations of Legacy Architectures in the Age of Machines
Traditional identity and access management (IAM) approaches were not designed to cater to the unique requirements of machines, leading to the creation of shadow agents and over-permissioned service accounts. This is primarily due to the sluggish nature of cloud IAM, the mismatch between security reviews and agent workflows, and the pressure to prioritize speed over precision.
Gartner’s research underlines the challenges of retrofitting human IAM approaches for machines, resulting in fragmented and ineffective management of machine identities. This not only violates regulatory mandates but also exposes organizations to heightened risks.
The governance gap is evident, with a vast majority of organizations still focusing on defining only human identities as “privileged users,” despite machine identities outnumbering humans significantly. As a result, machine identities often possess higher levels of sensitive access compared to human identities.
Furthermore, the lack of visibility exacerbates the problem, with a significant portion of an organization’s machine identities operating beyond the purview of security teams. Without a cohesive machine IAM strategy, organizations are at risk of compromising the security and integrity of their IT infrastructure.
Legacy service accounts pose a systemic risk as they persist even after the workloads they support have been decommissioned, leaving behind orphaned credentials with no clear ownership or lifecycle. Several enterprise breaches in recent years have exploited these long-lived credentials, emphasizing the need for better identity management practices.
Attackers have shifted their focus towards exploiting identities rather than endpoints, recognizing the vulnerabilities present in identity management systems. This shift underscores the importance of robust identity security measures to safeguard against unauthorized access and data breaches.
The Challenges Posed by Agentic AI to Identity Management
The emergence of AI agents necessitates a reevaluation of identity management practices, as these agents require their own credentials to interact with other systems. Gartner highlights agentic AI as a critical use case, emphasizing the importance of meticulously scoping credentials to adhere to the principle of least privilege.
Platforms that offer integrated identity, endpoint, and cloud telemetry solutions are becoming essential in detecting and mitigating potential abuses by AI agents in real-time. Fragmented tools are ill-equipped to handle the speed and scale of machine-to-machine interactions, underscoring the need for unified identity management platforms.
Gartner advocates for the adoption of dynamic service identities, defined as ephemeral, tightly scoped, policy-driven credentials that minimize the attack surface. By transitioning from static service accounts to cloud-native alternatives, organizations can enhance compliance and provide AI builders with the necessary identities to develop applications securely.
Proactive Measures for Addressing Dynamic Service Identity Shifts
Security leaders are advised to prioritize dynamic service identities over legacy service account models, as they reduce management overhead and the attack surface. By implementing just-in-time access and zero standing privileges, organizations can enhance their security posture and mitigate the risks associated with machine identity attacks.
Continuous monitoring of machine credentials is essential to detect anomalous activities and unauthorized access, aligning with the principles of zero trust. By enforcing strict agent lifecycle management and adopting unified identity platforms, organizations can effectively manage the evolving threat landscape posed by machine identities.
Anticipated Trends in Machine Identity Management for 2026
The gap between what AI builders deploy and what security teams can govern is expected to widen in 2026, posing significant challenges for organizations. As machine identities continue to proliferate at an accelerated pace, organizations must adapt their identity security models to effectively address the evolving threat landscape.
By transitioning towards agentic identity management and prioritizing dynamic service identities, organizations can strengthen their security defenses against machine-based attacks. Embracing a proactive approach to identity management and leveraging unified platforms will be critical in safeguarding against the escalating risks associated with machine identities.
