Topic starter
02/08/2026 12:20 pm
TL;DR: AI-based offensive testing is narrowing the practical gap between penetration testing and red teaming by simulating adaptive attack chains, broadening attack-surface coverage, and producing continuous evidence for compliance and resilience, according to Terra. The governance question is no longer which service is better in theory, but which control outcomes your programme must prove.
Editorial analysis by NHI Mgmt Group, based on content published by terra: “Red Team vs. Penetration Testing: Which Service Do You Need?”.
By the numbers:
- In 2024, average budgets increased by 8%, with only 12% of CISOs facing cuts.
Key questions
Q: What breaks when penetration testing is still treated as a separate step after code is written?
A: When testing is detached from development, vulnerabilities are usually found after code has already changed, moved forward, or been deployed.
Q: Why do boards increasingly care about continuous offensive testing?
A: Boards want evidence that risk reduction is real, not assumed.
Q: What signs show that red teaming and pentesting are being used for the wrong purpose?
A: A mismatch appears when the engagement scope does not match the decision it is meant to support.
Practitioner guidance
- Define testing outcomes by control objective Separate use cases for exploit discovery, resilience validation, SOC maturity, and executive readiness so each engagement is judged against the right evidence standard.
- Expand offensive scope to identity paths Include authentication flows, delegated access, service accounts, tokens, and third-party integrations in scope because they are common entry and escalation paths.
- Use continuous validation for fast-changing environments Replace isolated point-in-time tests with ongoing coverage where web apps, cloud services, and integrations change often enough to outpace annual assessments.
Bottom line: AI-driven offensive testing is narrowing the old boundary between penetration testing and red teaming by making attack simulation more adaptive and continuous.
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02/08/2026 1:21 pm
AI-driven offensive testing is turning assurance into a continuous control problem. Once agents can adapt to environment changes and expand their own attack paths, the value of a single annual exercise drops quickly. The relevant question becomes whether an organisation can maintain evidence of resilience as systems, identities, and integrations change. That makes the programme closer to continuous control validation than occasional testing.
A few things that frame the scale:
- Companies are dedicating an average of 32.4% of their security budgets to secrets management and code security, with US organisations leading at 40.8%, according to the State of Secrets in AppSec.
A question worth separating out:
Q: What is the difference between traditional penetration testing and AI red teaming?
A: Traditional penetration testing looks for weaknesses in networks, applications, and infrastructure. AI red teaming evaluates how a model behaves, decides, and responds under pressure. It tests misuse scenarios, prompt injection, data leakage, and harmful outputs, while also checking the supporting AI stack. In practice, the two disciplines overlap but are not interchangeable.
👉 Read our full editorial: Red teaming and penetration testing now overlap in AI-driven assurance