Advanced SAST Techniques and Optimization

Advanced SAST Techniques and Optimization

Custom rule development allows organizations to enforce specific security requirements beyond generic vulnerability detection. Organization-specific patterns, deprecated internal APIs, and compliance requirements can all be encoded as SAST rules. Custom rules also help detect business logic vulnerabilities that generic tools miss.

Incremental analysis optimization dramatically improves SAST performance for large codebases. By analyzing only changed files and their dependencies, scans complete in minutes rather than hours. Git integration enables precise change detection, while dependency analysis ensures that modifications affecting security are caught even in unchanged files.

Machine learning enhancement of SAST tools represents an emerging frontier. ML models trained on organization-specific code patterns can reduce false positives by understanding local coding conventions. Anomaly detection identifies unusual code patterns that might indicate security issues even without matching known vulnerability patterns. Natural language processing of comments and documentation can reveal security assumptions that code analysis alone might miss.