Testing Strategy and Timing
Pull Request Checks Fast PR checks including SAST (Static Application Security Testing), SCA (Software Composition Analysis), and secret scanning provide immediate feedback during development. PR checks should complete in minutes. SAST analyzes source code for security vulnerabilities including injection flaws, authentication issues, and cryptographic errors. SAST should be tuned to reduce false positives. SCA identifies vulnerable dependencies and license compliance issues. SCA should check both direct and transitive dependencies. Secret scanning detects accidentally committed credentials including API keys, passwords, and certificates. Secret scanning prevents credential exposure. PR check failures should block merge for high-confidence critical findings. Blocking prevents vulnerable code from reaching main branch. Nightly Deep Scans Nightly scans including DAST (Dynamic Application Security Testing), fuzzing, and IAST (Interactive Application Security Testing) provide comprehensive testing without blocking development velocity. DAST tests running applications for vulnerabilities including injection, authentication, and authorization flaws. DAST complements SAST by testing runtime behavior. Fuzzing generates malformed inputs to discover crashes and security issues. Fuzzing is effective for finding input validation and memory safety issues. IAST combines SAST and DAST by instrumenting applications during testing. IAST provides accurate findings with low false positives. Differential Scanning Differential scanning analyzes only changed code and dependencies, reducing scan time and noise. Differential scanning enables fast PR feedback. Baseline scans establish security posture for existing code. New findings are compared against baseline. Incremental scanning focuses on changes since last scan. Incremental scanning reduces alert fatigue.Coverage and Policy Enforcement
Coverage Metrics Code coverage by security testing measures percentage of code exercised by security tests. Coverage should be tracked and improved over time. Critical sink and source coverage measures testing of security-sensitive code paths including database queries, file operations, and authentication. Critical paths warrant comprehensive testing. Coverage targets should be defined per application based on risk. High-risk applications require higher coverage. Coverage metrics should be displayed in pull requests, providing visibility into testing thoroughness. Visibility drives improvement. Failure Policies Build failure policies define which findings block builds. Policies should balance security with development velocity. High-confidence critical findings should block builds. Blocking prevents known vulnerabilities from reaching production. Medium and low severity findings should warn without blocking. Warnings maintain awareness without blocking delivery. False positive suppression enables developers to mark false positives with justification. Suppression should have expiration to force periodic review. Risk Acceptance Workflow Risk acceptance workflow enables exceptions to failure policies with documented justification and approval. Exceptions should be time-limited. Risk acceptance should require appropriate authority based on risk level. High-risk exceptions require security team approval. Accepted risks should be tracked in risk register. Tracking maintains visibility.Tool Integration and Findings Management
Unified Findings Pipeline Findings from multiple tools should be aggregated into unified pipeline. Aggregation provides single source of truth. Findings normalization converts tool-specific formats into common schema. Normalization enables cross-tool analysis. Findings correlation identifies duplicate findings from multiple tools. Correlation reduces noise. Deduplication Deduplication identifies identical findings across scans and tools. Deduplication prevents duplicate work. Fingerprinting creates unique identifiers for findings based on vulnerability type, location, and context. Fingerprinting enables accurate deduplication. Deduplication should preserve finding history. History shows when findings were introduced and fixed. Automated Ticket Creation Findings should automatically create tickets in issue tracking systems. Automation ensures findings are tracked. Ticket creation should be configurable by severity and confidence. Not all findings warrant tickets. Tickets should include finding details, remediation guidance, and links to documentation. Comprehensive tickets enable efficient remediation. Ticket lifecycle should be synchronized with finding status. Closed findings should close tickets. Learning Mode Learning mode for new rules observes findings without blocking builds. Learning mode enables rule tuning before enforcement. Learning period should be time-limited (typically 1-2 weeks). Limited duration prevents indefinite learning. Learning mode findings should be reviewed to assess false positive rate. Review informs enforcement decisions.Tool Selection and Configuration
SAST Tools SAST tools should support relevant languages and frameworks. Language support should match technology stack. SAST configuration should enable relevant rules and disable noisy rules. Configuration should be version-controlled. Custom rules should be developed for organization-specific patterns. Custom rules address unique risks. SCA Tools SCA tools should provide comprehensive vulnerability databases with timely updates. Database freshness is critical. SCA should support multiple package managers and ecosystems. Support should match technology stack. License compliance checking identifies problematic licenses. License compliance prevents legal issues. DAST Tools DAST tools should support modern web technologies including SPAs and APIs. Technology support should match applications. DAST authentication should support complex authentication flows. Authentication enables testing of protected functionality. DAST should integrate with CI/CD for automated scanning. Integration enables continuous testing. Fuzzing Tools Fuzzing should be continuous rather than one-time. Continuous fuzzing finds issues as code evolves. Fuzzing should use coverage-guided fuzzing for efficiency. Coverage guidance improves fuzzing effectiveness. Fuzzing corpus should be seeded with valid inputs. Seeding improves fuzzing efficiency.Remediation and Feedback
Remediation Guidance Findings should include specific remediation guidance with code examples. Specific guidance enables rapid remediation. Remediation guidance should link to internal secure coding standards. Standards provide context. Automated fix suggestions enable one-click remediation where possible. Automation reduces remediation time. Developer Feedback Developers should be able to provide feedback on findings including false positive reports. Feedback enables continuous improvement. Feedback should be reviewed regularly by security team. Review improves tool tuning. Tool tuning based on feedback reduces false positives over time. Tuning improves developer experience. Metrics and Reporting Finding trends show security posture over time. Trends should show decreasing findings. Mean time to remediation measures responsiveness. MTTR should decrease over time. False positive rate measures tool accuracy. FPR should decrease through tuning. Tool coverage shows percentage of applications with automated testing. Coverage should approach 100%.Conclusion
Security testing automation provides fast, reliable security feedback that makes secure outcomes the default. Security engineers design testing strategies that balance speed with thoroughness, using PR checks for immediate feedback and deep scans for comprehensive coverage. Success requires appropriate tool selection, thoughtful configuration, unified findings management, and continuous improvement based on feedback. Organizations that invest in security testing automation fundamentals scale security testing across all development.References
- OWASP Testing Guide
- OWASP SAMM (Software Assurance Maturity Model)
- BSIMM (Building Security In Maturity Model)
- NIST SSDF (Secure Software Development Framework)
- DevSecOps Automated Security Testing Practices