DevSecOps: Shifting Left with Static and Dynamic Code Analysis

DevSecOps integrates security practices into the DevOps process, emphasizing the importance of security throughout the software development lifecycle (SDLC). Two critical components of this integration are static code analysis and dynamic code analysis, which serve to identify vulnerabilities and improve code quality at different stages of development. Static and dynamic code analysis are essential components of a robust DevSecOps strategy.

By integrating these practices into the software development lifecycle, organizations can significantly reduce vulnerabilities and improve software quality.  

Static Code Analysis (SAST)

Static code analysis (SCA) involves examining source code without executing it. This analysis is performed during the development phase or in a pre-deployment environment, allowing developers to identify potential vulnerabilities and coding standard violations early in the SDLC. It analyzes the code's syntax, structure, and control flow to detect vulnerabilities such as:

• SQL injection

• Cross-site scripting (XSS)

• Buffer overflows

• Race conditions

• Dead code

SAST tools can be integrated into the development environment to provide real-time feedback to developers, enabling them to fix issues early in the development process.

• Methodology: SCA tools analyze the code's static state, looking for patterns that violate defined coding rules. This includes both pattern-based analysis, which checks for specific code patterns, and flow-based analysis, which examines the paths through the code to identify issues like memory corruptions and race conditions.

• Benefits:

• Early Detection: SCA helps identify vulnerabilities before the code is executed, reducing the risk of security issues being introduced into production.

• Comprehensive Coverage: It can analyze 100% of the codebase, providing a thorough examination of all potential vulnerabilities.

• Automation: Many SCA tools can be integrated into the development environment, allowing for real-time feedback as developers write code.

• Limitations:

• False Positives: SCA can produce false positives, which may distract developers from genuine issues.

• Runtime Issues: It cannot detect bugs that only manifest during execution, such as memory leaks or performance bottlenecks.

Popular tools for static code analysis include ESLint for JavaScript, Pylint for Python, and SonarQube for multiple languages, which provide insights into code quality and adherence to best practices

Dynamic Code Analysis (DAST)

Dynamic code analysis (DCA) evaluates the behavior of software during execution. This approach involves running the application in a real or simulated environment to observe its performance and interactions. DAST involves testing the application while it's running to identify vulnerabilities that may not be apparent in static analysis. It simulates real-world attacks to uncover weaknesses such as:

• SQL injection

• Cross-site scripting (XSS)

• Broken authentication and session management

• Insecure direct object references

• Security misconfigurations 

DAST tools are typically used later in the development lifecycle, during testing and pre-production phases.

• Methodology: DCA requires code execution to analyze runtime behavior, which allows it to detect issues that arise under specific conditions, such as memory usage and runtime errors. This includes dynamic application security testing (DAST), which focuses on identifying security vulnerabilities during runtime.

• Benefits:

• Real-time Feedback: DCA can identify internal failures as they occur, allowing for immediate correlation with specific actions taken during testing.

• Runtime Specific Issues: It is particularly effective at uncovering issues that are only visible during execution, such as performance bottlenecks and certain types of security vulnerabilities.

• Limitations:

• Coverage: DCA may not cover all code paths, especially if the tests do not exercise all possible execution paths.

• Resource Intensive: Running dynamic analysis can be more resource-intensive compared to static analysis, as it requires a fully operational environment.

Tools used for dynamic code analysis include profiling tools like Java VisualVM and debugging tools like GDB, which help monitor application performance and identify runtime issues.

Combining SAST and DAST for Maximum Effectiveness

While SAST and DAST offer different perspectives, they are most effective when used together. By combining both techniques, organizations can achieve a higher level of code security.

• Complementary Coverage: SAST and DAST identify different types of vulnerabilities. 

• Early Detection: SAST helps find issues early in the development process. 

• Real-World Testing: DAST simulates real-world attacks. 

• Continuous Improvement: Integrate both tools into the development pipeline for ongoing security assessment.

By investing in static and dynamic code analysis, organizations can significantly reduce the risk of security breaches, improve software quality, and build trust with customers.