As organizations adopt cloud services, automation, and Agentic AI, machine identities—such as APIs, service accounts, workloads, bots, and AI agents—now vastly outnumber human users and have become a primary cybersecurity target. Unlike traditional automation, AI agents can autonomously access systems, request permissions, and perform actions across environments, creating challenges around visibility, ownership, privilege management, and governance. Common risks include identity sprawl, excessive permissions, long-lived credentials, hidden secrets, and limited monitoring. The paper argues that organizations must modernize identity governance with automated lifecycle management, continuous discovery, least-privilege access, credential rotation, and AI-driven threat detection. HCLTech’s framework emphasizes centralized visibility, ownership accountability, policy-based controls, risk analytics, and automated governance to help enterprises securely scale AI and cloud innovation while reducing security, compliance, and operational risks.

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