Topic starter
02/08/2026 11:47 am
TL;DR: 34% of Latio respondents named AI pentesting the AI feature they were most excited about, according to Novee. AI pentesting is shifting into procurement, so buyers need proof of attacker behaviour rather than familiar scanning automation.
Editorial analysis by NHI Mgmt Group, based on content published by Novee: “RSAC 2026 Offers a Glimpse into the Future of Offensive Security”.
Key questions
Q: Why do traditional security tools miss many AI security risks?
A: Traditional tools are tuned for static systems, known boundaries, and conventional traffic patterns.
Q: What breaks when AI agents are given broad standing access?
A: Broad standing access breaks governance because the agent can move from one task to another without a fresh authorization check.
Q: What should teams prioritise when choosing an AI application pentesting platform?
A: Prioritise exploit validation, safety controls, and attack-path chaining over marketing language or dashboard polish.
Practitioner guidance
- Define the AI attack surface before buying tooling List the exact AI workloads in scope, including chatbots, copilots, retrieval layers, and autonomous agents, then map the trust boundaries each one crosses.
- Demand multi-step attack-chain coverage Require demonstrations that show prompt injection, agent manipulation, and downstream tool execution in a single workflow, not isolated one-off findings.
- Evaluate whether findings are behaviour-based Check that the platform validates exploitability by interacting with the system, rather than flagging only static misconfigurations or generic model warnings.
Bottom line: AI pentesting is moving from novelty to procurement category, but the buying mistake is confusing automation with behavioural testing.
Glossary terms: AI-Driven Pentesting, Prompt Injection (Agentic), Agent Manipulation, AI Trust Layer
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This topic was modified 2 days ago by NHI Mgmt Group
02/08/2026 12:57 pm
Continuous AI pentesting is becoming a governance test, not just a testing category. The issue is no longer whether organisations can scan AI applications, but whether they can validate how those systems behave when trust is manipulated. That changes the control conversation from vulnerability enumeration to behavioural assurance, which is a very different operational problem for security and IAM teams.
A question worth separating out:
Q: How do organisations separate AI governance from AI security testing?
A: AI governance defines what should be allowed, while AI security testing verifies whether the deployed system actually stays within those boundaries. Governance without runtime validation is only policy on paper, especially once agents can retrieve data, call tools, and trigger workflows on their own.
👉 Read our full editorial: Continuous AI pentesting is redefining offensive security testing