Topic starter
02/08/2026 12:23 pm
TL;DR: Autonomous assessment is moving offensive testing away from manual execution and toward asset context, risk prioritisation, and remediation guidance, according to Hadrian. Offensive testing now needs controlled identity, scoped access, and traceable authority so its outputs can feed remediation without overextending trust.
Editorial analysis by NHI Mgmt Group, based on content published by Hadrian: “Presentation: How agentic AI is redefining offensive cybersecurity”.
Key questions
Q: Why do agentic security tools create new access control risks?
A: Because they can select actions dynamically, chain tools, and operate across systems without a human approving every step.
Q: What do security teams get wrong about autonomous AI attackers?
A: They often assume an LLM can independently plan and execute a full intrusion.
Q: How should security teams govern AI agents used for offensive testing?
A: Treat offensive AI agents as distinct workloads with explicit ownership, scoped tools, and logged approvals.
Practitioner guidance
- Define agent test scope and authority Document exactly which environments, assets, and validation steps an agentic testing system may access, and prohibit actions outside that scope.
- Apply non-human identity controls to testing agents Treat offensive testing agents as governed non-human identities with least privilege, revocation paths, and auditability.
- Tie findings to remediation workflows Route agent-generated asset context and ranked findings into the vulnerability and exposure management process so prioritisation drives action.
Bottom line: Agentic AI is shifting offensive testing from manual execution toward autonomous assessment, which raises governance demands around scope, authority, and traceability.
Glossary terms: Agentic Testing, Autonomous Test Authority, Agent Identity, Exposure-based prioritisation
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This topic was modified 2 days ago by NHI Mgmt Group
02/08/2026 1:23 pm
Agentic offensive testing creates a new governance layer, not just a faster testing workflow. Once a system can choose what to inspect and how to sequence verification, the issue becomes authority management as much as test coverage. That means offensive security programmes now need explicit rules for machine action, not just better automation. The practitioner conclusion is simple: govern the tester as carefully as the target.
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 do you know agentic testing is actually helping remediation?
A: The clearest signal is whether findings become easier to prioritise and fix, not whether the agent produces more alerts. If asset context, severity ranking, and remediation guidance are driving faster closure of the highest-risk issues, the programme is working. If not, the output is just faster noise.
👉 Read our full editorial: Agentic ai is reshaping offensive cybersecurity testing