Author: Apurva Davé

When your team stores API keys in a vault and rotates them on a schedule, it feels like the access problem is handled.
For years, artificial intelligence has been reactive. You prompted it, and it responded by analyzing data, generating text or predicting outcomes, but only when asked.
Most workload credentials, the API keys, tokens and passwords that connect your services, carry “always on” access that never expires.
AI agent identity breaks down when agents authenticate across OAuth, API keys and managed identities simultaneously. Learn why single-protocol solutions fail.
While companies pour resources into securing employee accounts with MFA, zero trust and regular access reviews, service accounts still get created with static credentials, granted sweeping permissions and then left unmanaged. This creates a growing population of identities that operate outside traditional IAM controls.
The Trivy incident exposed a credential architecture failure, not just a supply chain one. Here’s the case for workload identity and access.
AI agent identity security is the set of practices and controls that treat AI agents as distinct, governable identities with their own authentication, authorization and audit requirements.
Secret remediation is the process of responding to an exposed credential by revoking it, rotating it and removing every trace of it from your environment.
The OWASP Top 10 for LLM Applications is the most widely referenced framework for understanding these risks. First released in 2023, OWASP updated the list in late 2024 to reflect real-world incidents, emerging attack techniques and the rapid growth of agentic AI.
Agentic AI introduces new cybersecurity risks, primarily concerning autonomous identity, tool chain exposure, and cascading compromises, requiring security teams to urgently adopt least-privilege identity frameworks and real-time monitoring designed specifically for self-directed, persistent workloads.