What are the security risks of using Generative AI (ChatGPT, Gemini) in Cyber Security audits?

All answers to this question.

The risk of data leakage is the primary hurdle for Generative AI (ChatGPT, Gemini) in the security sector. Standard consumer versions of these tools use your input to train future models, which is a non-starter for Cyber Security professionals. You must use Enterprise versions with strict "no-training" clauses or deploy locally hosted models. We use Gemini Pro on a private cloud to scan our legacy Python scripts for SQL injection patterns. It’s incredibly fast at identifying "low-hanging fruit" vulnerabilities, which allows our senior analysts to focus on complex logic flaws that AI still can't quite grasp.

   Answered 2025-06-15 by Deborah Lewis

How do you verify the code fixes suggested by Generative AI (ChatGPT, Gemini) to ensure they don't introduce new, subtle bugs?

   Answered 2025-09-22 by Larry Sanchez

• Verification is key, Larry. We never push AI-generated code directly to production. Every suggestion goes through our standard CI/CD pipeline, including automated unit tests and a final peer review. Think of the AI as a junior developer who is very fast but occasionally makes confident mistakes—it requires constant adult supervision to be truly effective in a security context.

     Commented 2025-10-10 by George Bennett

I’ve found Generative AI (ChatGPT, Gemini) very useful for summarizing complex security logs into executive-level reports during an incident response.

   Answered 2025-12-05 by Melissa Foster

• Absolutely, Melissa. Bridging the gap between technical details and C-suite understanding is where these agents provide immediate, tangible value during a crisis.

     Commented 2025-12-12 by Deborah Lewis