Topic starter
20/08/2026 5:48 pm
TL;DR: Black Hat 2025 conversations showed agentic AI shifting security concerns from content generation to action-taking systems, with long-standing weaknesses in RBAC, API security, session handling, and third-party trust becoming more exposed as AI workflows scale, according to ActiveFence. The governance lesson is that AI security must be continuous and identity-aware, because delegated machine actions now create blast-radius problems that annual reviews cannot contain.
Editorial analysis by NHI Mgmt Group, based on content published by ActiveFence: “Alice at Hacker Summer Camp: Black Hat 2025 Key Takeaways”.
Key questions
Q: What breaks when agentic AI is governed only as a data platform problem?
A: The control model breaks because data governance does not answer who or what is acting on the data at runtime.
Q: Why do agentic AI workflows increase risk when RBAC and API permissions are broad?
A: Because the agent can turn broad standing access into immediate impact.
Q: What are the warning signs that GenAI governance is failing in production?
A: Common signs include rising token spend without clear business growth, poor traceability of prompt flows, inconsistent routing between models, and backend services that still contain long-lived API keys.
Practitioner guidance
- Implement runtime authorisation for agent actions Constrain every high-risk tool call to explicit, context-aware approval conditions so the agent cannot exceed its intended task scope.
- Inventory third- and fourth-party AI dependencies Map where suppliers, sub-processors, and embedded AI services can alter data flows, permissions, or response paths without direct notice.
- Move threat modelling into delivery pipelines Re-run AI threat models whenever prompts, tools, policies, or integrations change, not only at design sign-off.
Bottom line: Agentic AI shifts the core security problem from what models say to what connected systems can do on their behalf.
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20/08/2026 6:28 pm
Continuous threat modelling is now the organising principle for agentic AI governance. The article’s strongest point is not that AI creates new risks, but that agentic systems compress the time between decision, tool use, and impact. That makes annual or even quarterly review cycles structurally too slow for the problem. The field should treat runtime modelling as a governance discipline, not an optional testing enhancement. Practitioners should assume that the relevant control boundary is moving as quickly as the agent itself.
A few things that frame the scale:
- 92% of organisations expose NHIs to third parties, raising concerns about supply chain security, according to the Ultimate Guide to NHIs.
A question worth separating out:
Q: How should organisations respond when a supplier’s AI use creates fourth-party risk?
A: They should extend due diligence beyond the direct vendor and ask how downstream AI services affect authentication, data handling, logging, and incident response. The practical test is whether the enterprise can still trace and revoke access if a hidden dependency changes behaviour. If not, supplier assurance is incomplete.
👉 Read our full editorial: Agentic AI security now hinges on continuous threat modelling