What Is SAST for Software Engineers? A Complete Guide

Software security is no longer an afterthought—it's a necessity. With the rise of cyber threats, software engineers are expected to deliver secure applications without slowing down development. Organizations like DeepSeek, the TSA, Gravy Analytics, and MoveIt have all been impacted by vulnerabilities in their code. This is where Static Application Security Testing (SAST) comes in.

SAST is a crucial component of Application Security (AppSec) that helps engineers detect and fix security vulnerabilities early in the Software Development Life Cycle (SDLC). But what exactly is SAST, and how does it fit into modern software development? Let’s dive in.

What Is SAST?

Definition of SAST

Static Application Security Testing (SAST) is a white-box testing technique that analyzes source code, bytecode, or binaries to detect security vulnerabilities without executing the program. Think of it as a security linter that helps identify issues such as SQL injection, cross-site scripting (XSS), hardcoded secrets, and insecure dependencies before the code reaches production.

SAST tools scan codebases by:

1. Parsing the code: Breaking it into tokens and structures.

2. Building an Abstract Syntax Tree (AST): Representing the code’s structure.

3. Applying security rules: Traditional SAST tools rely on predefined patterns to detect security vulnerabilities, often leading to high false positives. Corgea, however, takes a modern approach by leveraging AI to analyze code contextually, reducing noise and accurately identifying exploitable security risks.

4. Generating reports: Highlighting potential security risks and suggesting fixes.

Example: Understanding AST in SAST

An Abstract Syntax Tree (AST) is a structured representation of source code that allows tools like SAST scanners to analyze code for security vulnerabilities.

Before Analysis (Vulnerable Code)

import osdef delete_file(filename):    
  os.system(f"rm {filename}")

This code is vulnerable to command injection because it directly interpolates `filename` into the shell command without sanitization.

AST Representation

When SAST scans the code, it first converts it into an AST, breaking it into components:

├── Import (os)
└── FunctionDef (delete_file)
    ├── Arguments (filename)
    └── Body
        ├── Call
            ├── Function (os.system)
            ├── Argument (f"rm {filename}")

From this AST, SAST detects that `os.system` is being called with unvalidated user input, which is a security risk.

After Analysis (Fixed Code)

import osimport shlex
def delete_file(filename):
    safe_filename = shlex.quote(filename)
    os.system(f"rm {safe_filename}")```

Here, shlex.quote() sanitizes the input, preventing command injection.

How SAST Uses AST for Detection

• Scans the AST: to identify risky function calls (`os.system`).

• Applies security rules: to check if user input is properly sanitized.

• Flags vulnerabilities: and suggests fixes.

This structured approach allows SAST tools to catch security issues before execution, making them essential for secure coding. Unlike Dynamic Application Security Testing (DAST), which tests applications in a runtime environment, SAST operates without executing code, making it ideal for early detection.

Why Software Engineers Need SAST

1. Detect Security Issues Early (Shift Left Approach)
   By integrating SAST into the development pipeline, engineers can catch security flaws early, reducing the cost and effort required to fix them later.

2. Reduce Technical Debt
   Fixing vulnerabilities post-production is expensive and risky. SAST helps reduce technical debt by ensuring secure coding practices from the start.

3. Compliance with Security Standards
   Many industries require compliance with security standards like OWASP Top 10, NIST, and ISO 27001. SAST helps ensure that applications meet these regulatory requirements.

4. Enhance Developer Productivity
   By integrating SAST into IDEs and CI/CD pipelines, developers get real-time security feedback, allowing them to fix issues on the fly without disrupting their workflow. For example, a development team at a fintech company reduced their security issue resolution time by 40% after integrating SAST into their CI/CD pipeline. Instead of waiting for periodic security audits, developers received instant alerts and suggestions directly in their IDEs, enabling them to address vulnerabilities as they coded.

Key Features of a Good SAST Tool

When choosing a SAST tool, software engineers should look for:

• Accuracy: Low false positive and false negative rates.

• Speed: Fast scans that don’t slow down development.

• Integration: Support for IDEs (e.g., VS Code, IntelliJ) and CI/CD tools (e.g., GitHub Actions, Jenkins).

• Customization: Ability to tailor security rules to the project’s needs.

• Actionable Insights: Clear, developer-friendly reports with remediation guidance.

Common Challenges with SAST and How to Overcome Them

1. High False Positives
   Traditional SAST tools often generate numerous false positives by relying on rule-based pattern matching, leading to alert fatigue. For example, a traditional SAST tool might flag the following code as vulnerable due to the use of eval():

   user_input = "print('Hello, world!')"
   eval(user_input)

   While eval() can be dangerous, in this case, it is a controlled and expected input. A traditional SAST tool might flag it indiscriminately, leading to a false positive. Corgea, however, uses AI and contextual analysis to determine whether the use of eval() is actually a security risk, filtering out false positives and surfacing only real, exploitable threats, significantly improving signal-to-noise ratio.

2. Lack of Context for Findings
   Choose a SAST tool that provides exploitability analysis and prioritizes issues based on impact. For example, a traditional SAST tool might flag the following code as vulnerable due to hardcoded credentials in a test suite:

   def test_authentication():
     username = "admin"
     password = "password123"
     assert authenticate(username, password)

   
   While hardcoded credentials are generally a security risk, this code exists in a controlled test environment and does not affect production security. A traditional SAST tool might flag it unnecessarily, whereas an advanced tool with exploitability analysis would recognize the context and avoid raising a false alarm.

3. Slow Scanning Times
   Run incremental scans on modified files instead of scanning the entire codebase repeatedly.

4. Developer Resistance
   Make security a developer-friendly process by integrating SAST directly into their workflow rather than as a separate step.

5. Language and Framework Coverage
   Different SAST tools have varying levels of support for programming languages and frameworks.

For example:

• Bandit: Focuses exclusively on Python code analysis

• ESLint: Specializes in JavaScript/TypeScript security scanning

• RuboCop: Primarily for Ruby code analysis, focusing on both security and style

• Corgea: uses AI to detect vulnerabilities across 10+ languages and frameworks including Go, Ruby, Java, Typescript C#, JavaScript, Python, and more

• Semgrep: uses traditional pattern detection to find vulnerabilities in 10+ languages and frameworks including Java, C#, PHP, and Ruby

When selecting a SAST tool, ensure it supports your tech stack. For example, if you're building a microservices architecture with Python, Go, and Node.js, you'll need a tool that can effectively analyze all three languages rather than using separate tools for each language.

Consider also the depth of framework-specific analysis. Some tools may recognize basic language syntax but miss framework-specific vulnerabilities (e.g., Django security misconfigurations or Express.js routing issues).

Best Practices for Implementing SAST in Your Workflow

1. Integrate SAST into CI/CD Pipelines: Automate security scans as part of your Continuous Integration/Continuous Deployment (CI/CD) to catch vulnerabilities early.

2. Use IDE Plugins for Instant Feedback: SAST tools like Corgea offer IDE integrations that provide real-time security insights while coding.

3. Define a Secure Coding Standard: Create and enforce coding guidelines aligned with industry best practices like OWASP Secure Coding Practices. With Corgea, you can define custom scanning, false positive detection, and fixing policies using natural language to match your organization's security requirements.

4. Combine SAST with Other Security Testing Methods: SAST is powerful but should be complemented by DAST, Interactive Application Security Testing (IAST), and Software Composition Analysis (SCA) to cover all attack surfaces.

5. Train Developers in Secure Coding: Security awareness and training can help developers write secure code from the start, reducing reliance on SAST as a sole security measure.

Conclusion: Make Security a Priority with SAST

SAST is an essential tool for software engineers looking to build secure applications without compromising development speed. By integrating SAST into your workflow and leveraging AI-driven solutions like Corgea, you can detect vulnerabilities early, improve code quality, and reduce security risks.