Topic starter
06/06/2026 11:21 am
TL;DR: AI agents now access enterprise systems, retrieve data, write code, and execute workflows autonomously, which makes identity, privilege, and auditability the core controls, according to Keyfactor. The critical issue is not whether agents can act, but whether their access can be cryptographically verified, narrowly scoped, and governed before execution starts.
Editorial analysis by NHI Mgmt Group, based on content published by Keyfactor: “Keyfactor + Delinea: Shortening the Leash on Your AI Agent”.
Key questions
Q: What breaks when an AI agent can acquire and use credentials inside one runtime session?
A: The review-based access model breaks first.
Q: Why do autonomous AI agents increase the risk of over-credentialing?
A: Autonomous agents need access to decide and act, but broad credentials let them move beyond the intended task boundary without a second approval step.
Q: How can teams tell whether AI access is actually under control?
A: Look for evidence that access is limited by purpose, not just by account.
Practitioner guidance
- Define agent identity before task execution Issue cryptographically verifiable workload certificates to agent runtimes so the platform can authenticate the actor before any credential request or external action occurs.
- Route external access through PAM Require agents to request third-party credentials through central PAM or secret retrieval services instead of embedding static secrets in the runtime or code.
- Limit every credential to a single task boundary Scope agent-issued credentials to the narrowest system, role, and duration needed for the current execution path, then invalidate them at task completion.
Bottom line: Agentic AI turns identity from a support function into the core control plane because autonomous systems can act, not just advise.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
06/06/2026 12:06 pm
Certificate-based identity is the minimum viable trust primitive for agentic workloads. The article is right to start with identity rather than prompt safety because an autonomous system becomes governable only when the workload itself can be cryptographically verified. Certificates do not solve every access problem, but they establish a stable identity anchor for ephemeral runtime actors. For practitioners, that means workload identity must precede policy decisions about what the agent may do.
A few things that frame the scale:
- The agentic AI market is already worth $5 billion and is projected to reach $50 billion by 2030.
A question worth separating out:
Q: What is the difference between certificate-based identity and PAM for AI agents?
A: Certificate-based identity proves which workload the agent is, while PAM decides what that workload may retrieve or use. Identity alone establishes trust in the actor; PAM constrains the secrets and privileges that actor can consume. Both are needed because authenticated identity without privilege control still leaves too much room for misuse.
👉 Read our full editorial: AI agent identity needs certificates, PAM, and zero trust