We receive these massive 100-page ethical hacking reports, and it takes our developers weeks to manually patch everything. Is there a way to feed these findings into an AI model to generate the necessary code fixes automatically? I’m looking to bridge the gap between discovery and defense.
3 answers
This is the next frontier in DevSecOps. You can use Large Language Models to parse the ethical hacking report and suggest specific code snippets. For example, if the report identifies a SQL injection, the AI can suggest the exact parameterized query for your specific framework. However, you must be careful—never auto-apply these fixes. The AI might break the application's logic. We’ve started using a tool that creates a "Pull Request" based on the ethical hacking findings, which a human dev then reviews. It has cut our remediation time by nearly 40%.
Does your ethical hacking report come in a machine-readable format like JSON or XML? That makes it much easier for an AI to digest the data accurately.
Focus on the "High" and "Critical" items first. AI is great at the repetitive "Low" severity fixes in an ethical hacking report, saving your best devs for the hard stuff.
Excellent point, Randy. Alice’s idea of using AI to generate Pull Requests for human review is definitely the safest and most efficient path forward.
Currently, it’s just a PDF, Keith. We are asking our ethical hacking vendor to provide a structured data export for our next engagement. If we can get that, I think we can build a much more reliable pipeline to feed those vulnerabilities directly into our Jira board with AI-generated suggestions for the fix.