Topic starter
02/08/2026 12:16 pm
TL;DR: Agent identity and access risk stays underplayed in Akto's overview because the guidance still centers models, data, APIs and monitoring. Agentic systems need task-scoped authority and revocation, or standing privilege becomes a control weakness.
Editorial analysis by NHI Mgmt Group, based on content published by Akto: “AI Security Best Practices: Protecting Models, Data & AI Systems”.
Key questions
Q: Why do agent credentials increase risk even when APIs are protected?
A: Because API protection only proves the caller reached the endpoint, not that the caller should be allowed to perform the business action.
Q: What are the signs that agent authority is failing in production?
A: Look for long-lived tokens, shared credentials, missing approval logs, and audit trails that cannot attribute an action to the agent itself.
Q: Where do AI security best practices fail when agents can act independently?
A: They fail when the programme treats the model as the main control boundary and ignores the identity of the acting system.
Practitioner guidance
- Inventory every agent and automation path Create a register of all AI agents, embedded automations, and delegated workflows, including owners, connected tools, and data access.
- Bind agent access to short-lived privileges Replace standing access with task-scoped permissions for agents wherever possible, and require explicit revocation after the workflow completes.
- Add action-level authorisation to API controls Check not only that an agent can authenticate to an API, but that it is allowed to perform the specific business action in that moment.
Bottom line: AI security best practice lists often stop at models, APIs, and monitoring, but the governance gap is the identity and authority of the agent itself.
Glossary terms: Agent Identity, Shadow AI, Resource-Level Authorisation, API Governance Debt
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This topic was modified 2 days ago by NHI Mgmt Group
02/08/2026 1:18 pm
Agent identity is the control plane, not a niche subtopic: once an AI system can decide when to call tools or services, identity governance becomes the boundary that determines blast radius. Best-practice lists that stop at model safety, monitoring, or data hygiene leave the acting entity under-governed. The practitioner takeaway is to treat agent identity as part of core IAM and PAM design, not as an AI add-on.
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: How should organisations govern shadow AI without blocking legitimate use?
A: Start with approved-use policy, tool inventory, and data classification. Then require that any AI system handling internal information has named owners, logged access, and defined credential paths. The goal is not prohibition, but visibility and control. If a tool cannot be inventoried or monitored, it should not process sensitive data.
👉 Read our full editorial: AI security best practices still leave agent identity gaps