Topic starter
02/08/2026 11:46 am
TL;DR: OWASP’s Q2 2026 update splits agentic AI, LLM and GenAI applications, and red teaming into separate landscapes, reflecting three different attack surfaces and control requirements, according to Straikerai. The distinction matters because agentic systems introduce runtime tool use, cross-session memory, and continuous decision-making that conventional model guardrails do not govern.
Editorial analysis by NHI Mgmt Group, based on content published by Straikerai: “Three Landscapes, One Security Shift: What OWASP's Q2 2026 Update Is Really Saying”.
Key questions
Q: What breaks when agentic AI is governed like a standard LLM application?
A: The control model breaks because LLM governance assumes the main risk is what the model says, while agentic systems create risk through what the model does.
Q: Why do agentic AI systems require continuous red teaming?
A: Agentic systems change when models are retrained, prompts are updated, retrieval sources shift, or new integrations are added.
Q: What are the signs that an agentic security programme is too close to GenAI-only governance?
A: The clearest sign is when controls focus only on prompt content, model outputs, or pre-release evaluations while ignoring tool permissions, memory scope, and delegation paths.
Practitioner guidance
- Define an agentic security boundary Separate agentic workflows from conventional GenAI use cases and document where runtime decisions, tool calls, and memory persistence begin.
- Inventory every agent, tool, and MCP server Create and maintain a complete list of agents, the tools they can invoke, and the servers or services they can reach.
- Move assurance into runtime monitoring Add controls that inspect execution behaviour, context scope, and tool use during operation rather than relying only on pre-release checks.
Bottom line: Agentic AI changes the security boundary from model output to runtime action, which makes tool use, memory scope, and delegated execution the real governance problem.
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02/08/2026 12:57 pm
Agentic AI governance now has to start at the decision layer, not the model layer. Once a system can invoke tools, move context, and act across sessions, classic prompt guardrails stop being the primary control. The real issue is whether the organisation can govern the actions an agent takes after inference. That is why agentic AI cannot be folded into a generic GenAI policy without losing the actual risk boundary. Practitioners should treat decision authority as the new control point.
A few things that frame the scale:
- AI-related credential leaks surged 81.5% year-over-year in 2025, with the surrounding AI infrastructure leaking 5x faster than core LLM providers, according to the State of Secrets Sprawl 2026.
- 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.
A question worth separating out:
Q: How do IAM and NHI teams govern agent access across the request chain?
A: They should treat the human identity, the agent session, and the downstream tool as one governed path with shared lifecycle rules. That means role changes, revocation, logging, and recertification must reach the agent-mediated access point at the same time they reach the primary identity record.
👉 Read our full editorial: OWASP’s Q2 2026 landscapes split agentic AI from LLM security