Topic starter
19/08/2026 4:05 pm
TL;DR: An agentic AI workflow can speed creation of Nuclei vulnerability checks, improve coverage for exposed panels and uncovered products, and reduce time spent on repetitive engineering work, according to Intruder, but only when engineers keep directing, validating, and correcting outputs. The practical lesson is that AI can accelerate detection development, yet false positives, hallucinations, and missed edge cases still make human oversight the governing control.
Editorial analysis by NHI Mgmt Group, based on content published by Intruder: “How We’re Using AI to Write Vulnerability Checks (and Where It Falls Short)”.
Key questions
Q: What breaks when agentic AI is used to create vulnerability checks without review?
A: The checks may compile but still fail operationally because the model can invent unsupported functions, weak matchers, or incomplete logic.
Q: Why does AI-assisted detection engineering still need clean and vulnerable test targets?
A: Because you cannot trust a generated check until it proves the intended difference between a safe host and a vulnerable one.
Q: How do security and engineering teams know whether AI feedback quality is actually improving?
A: They should look for consistent gains in user action signals, not just model outputs.
Practitioner guidance
- Define a human-in-the-loop template workflow Use agentic AI to draft checks, but require engineer review before any template enters production scanning.
- Test every generated check against clean and vulnerable targets Validate matchers, extractors, and endpoint logic against both known-vulnerable and known-safe hosts before rollout.
- Prefer multi-request checks for exposed services Design checks so the first request identifies the service and the second confirms whether unauthorised access or data exposure is real.
Bottom line: Agentic AI can speed detection development, but the decisive control remains engineer review and test validation.
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19/08/2026 4:39 pm
Agentic AI is becoming a detection engineering copilot, not a replacement for governance. The article shows that the best outcome is faster template production with human steering, not fully autonomous check creation. That is the right framing for security operations because detection content is safety-critical and small errors can cascade into bad response decisions. Practitioners should judge these systems by validated output quality, not by how much code they can generate unattended.
A few things that frame the scale:
- A Harris Poll survey of more than 300 technology decision-makers found that 86% expect agentic AI to deliver positive ROI, yet fewer than half had AI governance policies in place.
A question worth separating out:
Q: Should teams use agentic AI for detection content before full autonomy is possible?
A: Yes, but only as a productivity layer inside a controlled process. The best use case is accelerating draft creation, targeted research, and template iteration while engineers retain final authority. That approach captures speed benefits without turning detection quality into a model-output problem.
👉 Read our full editorial: Agentic AI can speed vulnerability check creation, but oversight remains essential