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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.
NHIMG editorial — based on content published by Keyfactor: Keyfactor + Delinea: Shortening the Leash on Your AI Agent
By the numbers:
- By 2030, CIOs expect that 0% of IT work will be done by humans without AI, 75% will be done by humans augmented with AI, and 25% will be done by AI alone, according to a July 2025 survey of over 700 CIOs by Gartner®.
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%).
Questions worth separating out
Q: How should security teams govern AI agents that need access to enterprise systems?
A: Treat AI agents as non-human identities and require a verifiable workload identity before any privileged action occurs.
Q: Why do AI agents complicate zero trust architecture?
A: Because zero trust assumes each access decision can be verified at the point of use, but agentic systems can make those decisions dynamically and repeatedly inside a session.
Q: What breaks when AI agents use static secrets or broad credentials?
A: Static secrets create a large blast radius because one compromised or overused credential can expose multiple systems, repositories, or data sets.
Practitioner guidance
- Bind agent runtime to cryptographic workload identity Issue a verifiable certificate to each agent instance before it can access internal services, SaaS platforms, or cloud APIs.
- Broker all external access through PAM Require agents to authenticate to a privileged access workflow before retrieving repository, infrastructure, or database credentials.
- Separate identity proof from privilege scope Do not let a valid certificate become a blank cheque for access.
What's in the full article
Keyfactor's full blog post covers the operational detail this post intentionally leaves for the source:
- How the certificate issuance and Istio integration are intended to work for containerised workloads.
- How the PAM-backed retrieval path is described for external systems such as repositories, cloud platforms, and SaaS applications.
- How policy-based credential scoping and audit logging are positioned in the example architecture.
- How the source article maps the control model to autonomous AI use cases in practice.
👉 Read Keyfactor's analysis of AI agent identity, certificates, and PAM →
AI agent identity and PAM controls: what teams need now?
Explore further
06/06/2026 12:06 pm
Agentic AI turns identity into a runtime control problem, not a provisioning problem. Once the system can decide what to do next, conventional IAM assumptions about static entitlements and review cycles become too slow to govern. The field has to treat the agent as a runtime identity with a bounded execution envelope, because access decided at design time will not constrain behaviour at execution time. The practical conclusion is that identity governance must move closer to the action itself.
A few things that frame the scale:
- 92% agree governing AI agents is critical to enterprise security, yet only 44% have implemented any policies to do so, according to AI Agents: The New Attack Surface report.
- Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation.
A question worth separating out:
Q: What is the difference between workload identity and privileged access management for agents?
A: Workload identity proves which agent instance is acting, while privileged access management governs what that instance may retrieve or do. In practice, the first is about authentication and cryptographic trust, and the second is about scoped authorisation, session control, and auditability. Both are required for agent governance.
👉 Read our full editorial: AI agent identity needs certificates, PAM, and zero trust
