Agentic AI refers to artificial intelligence systems that operate autonomously, executing tasks, making decisions, and interacting with digital systems without direct human intervention. Unlike traditional automation, which follows predefined workflows, Agentic AI systems exhibit self-directed behavior, often orchestrated across modular architectures that include planning, reasoning, and adaptive learning capabilities. These systems are increasingly deployed across enterprise environments for use cases ranging from process automation to threat detection and customer service.
In cybersecurity and identity management, Agentic AI introduces unique challenges, primarily due to its reliance on Non-Human Identities (NHIs)—digital credentials assigned to AI agents, APIs, services, and workloads that enable access to data and infrastructure. As Agentic AI gains autonomy, it becomes both a critical operational asset and a potential security liability.
The rise of Agentic AI significantly expands the attack surface in enterprise environments. Each AI agent typically uses multiple NHIs to perform its functions—interacting with APIs, databases, and cloud services—which, if misconfigured or compromised, can lead to lateral movement, data exfiltration, and systemic breaches. Moreover, the autonomous nature of these agents means they often operate at high speed and scale, making manual oversight impractical and increasing the potential impact of credential misuse.
From a governance perspective, security teams must account for the ephemeral and dynamic nature of AI workloads. Static secrets, hardcoded credentials, and long-lived tokens are no longer viable. Instead, enterprises must adopt ephemeral credentialing, continuous authentication, and AI-specific threat detection to secure Agentic AI ecosystems.
In practice, Agentic AI is deployed in a wide range of enterprise functions. For example, in financial services, autonomous AI agents monitor transactions for fraud in real time, accessing sensitive data via NHIs. In healthcare, AI-driven diagnostics systems retrieve patient data from electronic health records through credentialed API calls. In DevOps, self-healing infrastructure agents detect and remediate system failures using machine identities with infrastructure-level permissions.
These applications, while operationally valuable, require precise control over NHI permissions, lifecycle management, and access boundaries to prevent misuse or unintended propagation of elevated privileges.
Agentic AI systems depend heavily on NHIs to function. Each AI agent must authenticate to resources, trigger workflows, and access datasets—all of which require machine credentials. As AI agents multiply, so do NHIs, often outpacing human identities by an order of magnitude. Poorly managed NHIs—such as orphaned credentials left behind by decommissioned agents—create serious security risks, as evidenced by recent breaches involving stale or overprivileged machine identities.
Securing Agentic AI therefore requires integrating NHI lifecycle automation, policy-based access controls, and continuous attestation mechanisms into AI orchestration layers.
Industry data suggests that over 68% of cloud security breaches now involve NHI misuse, and the adoption of Agentic AI is accelerating this trend. Organizations managing large-scale AI deployments report NHI-to-human identity ratios exceeding 17:1. Standards bodies such as NIST are beginning to address autonomous systems in their identity frameworks, and early implementations of post-quantum cryptography and decentralized identity (DID) models are emerging to address future-proofing concerns.
Additionally, leading enterprises are integrating Zero Trust architectures purpose-built for AI workflows, enforcing microsegmentation, just-in-time access, and behavioral authentication for AI agents.
Agentic AI represents both an opportunity and a challenge. It offers operational efficiency and intelligence at scale but also introduces complex, real-time security concerns that traditional IAM and PAM systems are ill-equipped to handle. For organizations embracing digital transformation, securing Agentic AI requires rethinking identity governance with a focus on NHIs—treating them not as static credentials, but as dynamic, high-risk entities embedded within autonomous workflows.
Ultimately, the future of secure AI deployment depends on robust, adaptive NHI security frameworks that align with the speed and autonomy of Agentic AI systems.
