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What is automated software analysis?
Automated software analysis refers to the use of tools and processes that automatically inspect software code, binaries, configurations, and behavior to detect vulnerabilities, misconfigurations, licensing issues, and malicious components without manual intervention. It is a core practice in modern software development and security pipelines.
This category includes static analysis (SAST), dynamic analysis (DAST), software composition analysis (SCA), binary scanning, and behavioral analysis.
Why automate software analysis?
Today’s software systems are large, complex, and composed of thousands of third-party and open-source components. Manual review cannot keep pace with modern development cycles. Automated analysis provides:
- Continuous visibility across the SDLC
- Early identification of bugs and security risks
- Faster delivery of secure software
- Regulatory compliance with standards like NIST SSDF, EO 14028, and FedRAMP
How does it work?
Automated tools perform various types of analysis across different stages of the SDLC:
- Static Analysis (SAST): Scans source code or bytecode without executing it
- Dynamic Analysis (DAST): Tests running applications for behavioral issues or vulnerabilities
- Software Composition Analysis (SCA): Identifies open-source components and their licenses or CVEs
- Binary Analysis: Inspects compiled software artifacts for malicious traits or obfuscated logic
- Runtime Monitoring: Observes software behavior during execution (e.g., memory use, API calls, egress)
These tools can be integrated into CI/CD pipelines and development environments to provide continuous feedback and enforcement.
Benefits
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Improves Developer Productivity: Surfaces issues early, enabling rapid remediation before release
-
Reduces Security Risk: Identifies vulnerabilities, secrets, and misconfigurations automatically
-
Supports Compliance: Produces auditable reports to meet regulatory and contractual obligations
- Accelerates Secure Delivery:] Enables secure-by-default DevOps practices
Automated software analysis vs
|
Topic |
Focus Area |
Key Differences |
|
Manual Code Review |
Human-led analysis |
Automated tools scale across large codebases and pipelines |
|
Penetration Testing |
Simulated real-world attacks |
Automated analysis is broader and more continuous |
|
Runtime Protection (RASP) |
Defends live applications |
Automated analysis identifies issues before deployment |
Best practices for automating software analysis
- Embed SAST, SCA, and DAST into CI/CD pipelines with fail gates for critical issues
- Analyze both source and binary code to catch hidden threats
- Set up behavioral analysis for downloaded third-party packages
- Continuously scan for outdated or vulnerable dependencies
Use cases
-
DevSecOps Integration: Enforcing security gates during code commits and merges
-
Third-Party Code Assessment: Vetting open-source or vendor code before integration
-
SBOM Verification: Ensuring declared components match scanned artifacts
- Threat Detection in CI/CD: Identifying suspicious behavior or payloads during build or deployment
Additional considerations
- Automation must be paired with tuning to reduce false positives
- Not all tools cover binary or behavioral risks — use multiple layers
- Security findings should be correlated with the risk context for effective prioritization
- Consider mapping analysis outputs to threat models and risk registries