Topic starter
25/06/2026 5:12 pm
TL;DR: OWASP’s Top 10 for Agentic Applications defines 10 operational risk categories for autonomous and semi-autonomous AI systems that plan, call tools, use memory, and take actions inside enterprise workflows, according to Zenity. The framework matters because existing model-centric controls do not address the identity, delegation, and execution risks that emerge once AI can act.
Editorial analysis by NHI Mgmt Group, based on content published by Zenity: “The OWASP Top 10 for Agentic Applications: A Milestone for the Future of AI Security”.
Key questions
Q: What breaks when AI agents are given broad enterprise access without tight governance?
A: Broad access turns AI agents into high-speed execution paths that can move data, spend money, modify records, or delete assets before operators can intervene.
Q: Why does agentic AI increase enterprise risk compared with text-only models?
A: Agentic AI increases risk because it can move from generating advice to taking actions.
Q: What are the signs that agent controls are not working?
A: The clearest warning signs are unexplained tool calls, unexpected privilege use, unreviewed delegated actions, and memory or context changes that alter later decisions.
Practitioner guidance
- Map agent permissions to execution boundaries Inventory which agents can invoke tools, read memory, and reach sensitive systems, then compare those permissions with the actual tasks they perform.
- Separate agent identities from human workflows Give each agent a distinct identity, role, and approval boundary so delegated actions can be traced and limited independently of the user who requested them.
- Review goal integrity controls before production use Test whether an agent can be redirected by hostile context, then block release until runtime guardrails preserve the authorised objective during execution.
Bottom line: Agentic AI security shifts the problem from safe outputs to governed actions, which changes the centre of gravity for IAM and NHI teams.
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25/06/2026 5:45 pm
Agentic security is no longer a model-risk problem, it is an identity-and-action problem. OWASP’s agentic AI Top 10 matters because it shifts the security conversation from what the model says to what the agent can do. That distinction forces IAM, PAM, and NHI programmes to govern execution paths, not just authentication events. Practitioners should treat agent behaviour as a live access control issue, not a content moderation issue.
A few things that frame the scale:
- 53% of security leaders expect AI to run major portions of their infrastructure autonomously within the next three years, according to the 2026 Infrastructure Identity Survey.
- Only 23% of IT leaders were very confident in their organisation's ability to manage security and governance for GenAI deployments, according to a 2025 Gartner survey of 360 IT leaders.
A question worth separating out:
Q: How should security teams govern agents that act like privileged identities?
A: They should assign each agent a distinct identity, define explicit delegation boundaries, and monitor the actions that identity can perform across the lifecycle. If an agent can access data, invoke tools, or trigger workflow steps, it needs controls comparable to a privileged service account, plus behavioural oversight for runtime decisions.
👉 Read our full editorial: OWASP's agentic AI Top 10 reframes enterprise AI security