LLM Secrets Detection
Semantic credential detection beyond regex
Overview
Use AI to understand what constitutes sensitive data semantically - partial keys, obfuscated credentials, internal URLs, and PII that traditional regex-based scanners miss.
Why It Matters
Regex catches obvious patterns. AI understands context - catching credentials split across lines, obfuscated keys, and sensitive data in unexpected formats.
The Risk
Secrets leak in creative ways. Developers split keys across variables, encode them, or hide them in comments. Once in git history, they're compromised forever. Regex-based tools miss the clever leaks that attackers find easily.
Implementation Components
A complete implementation of this capability includes:
- Pre-commit hook integration
- LLM semantic analysis of suspicious patterns
- Context-aware detection (variable names, comments, patterns)
- Split-key and obfuscation detection
- PII and infrastructure detail detection
- Tunable confidence thresholds
AI Integration
This capability leverages AI/LLM technology to enhance its functionality.
Trigger
Pre-commit or PR creation
Input
Code changes + file context + suspicious patterns
Output
Semantic assessment + credential likelihood + remediation
Implementation Pattern
- 1Scan code changes for potential secrets
- 2Send suspicious patterns to LLM with context
- 3Semantic analysis of credential likelihood
- 4Block commits with high-confidence findings
Tool Examples
These are examples, not endorsements. Choose what fits your context.
Dependencies
Requires (must have first)
Same Phase
Other capabilities in this pipeline phase