Topic starter
25/06/2026 10:43 pm
TL;DR: Agentic AI systems are being wired into enterprise workflows, but most still lack the encryption, authentication, auditability, and revocation controls needed to trust them as workload identities, according to Keyfactor. The governance assumption that AI can act safely without durable identity and controlled access collapses once agents can connect to tools, data, and other systems at runtime.
Editorial analysis by NHI Mgmt Group, based on content published by Keyfactor: “The Next Security Frontier: Securing the Future of Agentic AI with Digital Trust”.
Key questions
Q: What should organisations do when agentic AI starts using enterprise tools?
A: Organisations should define what the system may access, what actions require approval, and who is accountable if behaviour changes during execution.
Q: When do secrets become a higher risk in agentic AI environments?
A: Risk rises when autonomous systems can copy, reuse, or trigger credentials across multiple tools and workflows.
Q: What breaks when agent access cannot be revoked quickly?
A: When agent access cannot be revoked quickly, business errors can compound at machine speed across publishing, pricing, data updates, or customer interactions.
Practitioner guidance
- Map every agent to a named identity Inventory agentic systems as first-class workload identities, including their owners, intended actions, and connected tools.
- Replace shared secrets with certificate-based trust Move agent connections away from SSH keys and shared secrets so the organisation can establish origin, authenticity, and revocation discipline for each runtime identity.
- Constrain each agent to narrow tool scopes Define the minimum set of applications, data stores, and actions each agent may use, then separate privileges by task rather than by platform.
Bottom line: Agentic AI becomes an identity problem once it can initiate actions across enterprise systems, because trust, authorisation, and revocation must follow the runtime path.
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25/06/2026 11:23 pm
Agentic AI is now an identity governance problem, not just an AI governance problem. Once an agent can initiate tool calls and connect to enterprise systems, it stops being a passive model output channel and becomes an identity-bearing runtime actor. That shifts the centre of gravity from prompt safety to access control, auditability, and offboarding. Practitioners should frame this as workload identity expansion, not as a narrow AI feature issue.
A few things that frame the scale:
- 67% of organisations still rely heavily on static credentials despite the risks they pose to agentic AI deployments, according to the 2026 Infrastructure Identity Survey.
- Only 13% of organisations feel extremely prepared for the reality of agentic AI despite the majority racing toward autonomous adoption, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: What is the difference between agentic AI controls and traditional workload identity controls?
A: Traditional workload identity controls usually assume a stable service with predictable behaviour. Agentic AI adds runtime decision-making, tool choice, and dynamic execution paths, so the control model has to account for changing intent, broader tool use, and more immediate containment when behaviour drifts.
👉 Read our full editorial: Securing agentic AI with digital trust and machine identity