Cybersecurity
Illustrative Security Blueprint
Custom AI Security Audit
Methodology Blueprint
This scenario models our security-first AI audit methodology applied to custom codebases. Target metrics reflect baseline operational benchmarks.
The Challenge
Legacy cybersecurity scanners catch known malware signatures but completely miss complex logic flaws, hardcoded credentials, and architectural gaps within custom software stacks. A manual audit takes weeks and slows down deployment cycles.
The Solution Architecture
We designed an automated, agentic security auditing framework that orchestrates top frontier models (like Claude 3.5 Sonnet and GPT-4o) to perform deep static and dynamic code analysis. The system simulates creative adversary attack vectors, cross-references internal configurations against global threat databases, and generates prioritized, human-readable remediation reports.
Target Audit Deliverables & Security Controls
100%
Logic flaw & OWASP LLM Top 10 coverage
90%
Target reduction in audit turnaround time
<4 hrs
Target automated run time (was multi-week)
Technologies Used
Claude 3.5 Sonnet
GPT-4o
Python
Semgrep
OWASP ZAP