AI SAST

SAST built for how agents code

Security Testing Agent's AI SAST scans agent-generated code the moment it's written, catching vulnerabilities at the speed of the autonomous SDLC.

Why Teams Switch

SAST has never worked. AI coding agents make it worse.

AI Coding

Not built for autonomous coding

Scanning at commit or PR breaks down when agents ship code around the clock

Secure AI coding

Scan agent-generated code the moment it's written, wherever your agents work

Accuracy

Even more noise

You already have too many FPs. Agents shipping code nonstop means even more.

Stop chasing false positives

Reduce false positives by 10x with our Code Property Graph and AI reasoning

Overhead

AI just makes the toil worse

Triaging findings was always manual. More code from AI just means more work.

Better security with agents

AI built into the platform sharpens results; agents and skills handle the toil

Secure AI Coding

Scan code as AI writes it

Automatically scan AI-generated code in Cursor, Windsurf, and Claude Code for vulnerabilities and other security risks.

No context switchingGet alerted on vulnerabilities as the code is generated, without leaving the AI coding tool.
Deterministic scanningRuns every single time code is generated, not just when the agent, or your developer, thinks to.
Full application contextEvaluates AI-generated code against your existing code, not as an isolated snippet.
Reduce the noise

Cut false positives

Traditional SAST tools flood developers with false positives. Harness Security Testing Agent doesn't.

Code property graph. Uses a CPG to evaluate data flow and control flow, not just syntax, delivering 10x fewer false positives.

AI confidence scoring. Scores every finding by true positive likelihood, so teams can focus on real risks instead of chasing noise.

Deep vulnerability detection. Identifies business logic flaws like IDOR and multi-file attack paths that pattern-matching tools miss entirely.

Eliminate the Toil

An agentic security team

Harness doesn't just secure AI-generated code. It brings agents and AI skills to your security team to orchestrate, prioritize, and remediate faster.

Security Worker Agents. Fully autonomous agents handle orchestration, triage, prioritization, and remediation end-to-end.

AI skills for your team. Claude and MCP skills tackle time-consuming security tasks to increase efficiency and scale out your team.

Agent marketplace. Browse and deploy agents and skills purpose-built for security teams - all in one place.

Connecting Dev and Sec

AI SAST secures code for your whole team

Fix it before it becomes a ticket.

Find and fix vulnerabilities inside Cursor, Windsurf, and Claude Code without leaving the AI coding tool.

Spend less time chasing false positives. Harness delivers 10x fewer than legacy scanners.

Get AI-generated fix suggestions alongside every finding and fix security issues 95% faster.

FAQs

Common questions answered

Static Application Security Testing (SAST) is a security testing methodology that analyzes application source code to identify vulnerabilities without executing the program. SAST examines code from the inside out, giving developers visibility into potential security flaws during the earliest stages of the software development lifecycle (SDLC).

SAST tools scan code as it's written, identifying common vulnerabilities such as SQL injection, cross-site scripting (XSS), buffer overflows, and insecure cryptographic implementations. By catching these issues before compilation or deployment, SAST enables developers to fix security problems when they're least expensive to remediate, saving both time and money while reducing the risk of breaches in production. This proactive approach makes SAST an essential component of modern DevSecOps practices.

AI SAST (AI-powered Static Application Security Testing) analyzes source code for vulnerabilities before it ever runs, and applies AI to do it faster and more accurately than traditional approaches. Unlike legacy SAST tools that flood developers with false positives, AI SAST uses AI agents to automatically validate findings and eliminate noise, delivering up to 10x fewer false positives so developers can focus on what actually matters. It also applies AI-powered remediation to suggest precise, context-aware fixes, accelerating resolution without requiring deep security expertise. And with coverage extending to AI-generated code, it catches risks introduced by tools like GitHub Copilot or ChatGPT that traditional scanners often miss.

SAST plays a crucial role in meeting regulatory and industry compliance requirements by providing documented evidence of secure software development practices.

Key compliance standards:

  • PCI DSS Requirement 6.5: Organizations handling payment card data must address common coding vulnerabilities. SAST provides automated verification that code is tested for OWASP Top 10 vulnerabilities including injection flaws, broken authentication, and sensitive data exposure.
  • GDPR and data protection: SAST helps identify vulnerabilities that could lead to data breaches, demonstrating due diligence in protecting personal information. Tools can flag insecure data handling, weak encryption, and improper access controls.
  • HIPAA: Healthcare organizations must implement technical safeguards for protected health information (PHI). SAST validates secure coding practices and identifies potential vulnerabilities that could compromise patient data.
  • SOC 2: SAST provides evidence of security controls in the development lifecycle, supporting availability, confidentiality, and security trust principles.
  • NIST Secure Software Development Framework (SSDF): SAST enables organizations to implement the "Review and/or analyze human-readable code" practice (PW.7) within the NIST framework.
  • ISO 27001: SAST supports secure development lifecycle requirements and provides audit trails for security testing activities.

Compliance benefits:

  • Automated documentation: SAST generates reports showing vulnerability testing history, remediation timelines, and coverage metrics.
  • Audit trails: Detailed logs demonstrate continuous security testing throughout development.
  • Policy enforcement: Configure quality gates to prevent non-compliant code from reaching production.
  • Standards mapping: Leading tools map findings to CWE, OWASP, and compliance-specific requirements.
  • Executive reporting: Dashboard views showing security posture and compliance status.

By integrating SAST into your SDLC, you create verifiable evidence that your organization follows secure coding practices, essential for passing audits and maintaining compliance certifications.

Advantages:

  • Early vulnerability detection: SAST identifies security flaws during development, when fixes are 10-100x cheaper than in production. Developers receive immediate feedback and can remediate issues before code review.
  • Comprehensive code coverage: Unlike manual reviews or testing, SAST analyzes 100% of your codebase, including rarely executed paths and edge cases that might be missed by dynamic testing.
  • Pinpoint accuracy: SAST identifies the exact file, line number, and code snippet where vulnerabilities exist, making remediation straightforward for developers.
  • No runtime required: SAST doesn't need a compiled or running application, enabling security testing earlier in the SDLC than other methodologies.
  • Cost-effective scaling: Automated SAST scans can run continuously across hundreds of projects without additional security staff.

Limitations:

  • False positives: SAST tools can flag issues that aren't actually exploitable in context, requiring manual triage and potentially eroding developer trust if not managed properly.
  • Language limitations: Each SAST tool supports specific programming languages; polyglot environments may require multiple tools.
  • Cannot detect runtime issues: SAST misses vulnerabilities related to deployment configuration, server settings, authentication mechanisms, or third-party service integrations.
  • Limited context awareness: SAST may not understand business logic or complex data flows across microservices, potentially missing sophisticated vulnerabilities or generating false positives.

For optimal security coverage, combine SAST with DAST, Software Composition Analysis (SCA), and regular penetration testing.

SAST tools can identify a wide range of security vulnerabilities and coding flaws aligned with industry standards like the OWASP Top 10 and CWE/SANS Top 25. Here are the primary vulnerability types:

  • Injection flaws: SQL injection (SQLi), command injection, LDAP injection, XML injection
  • Cross-site scripting (XSS): Reflected XSS, stored XSS, DOM-based XSS
  • Authentication and session management issues: Hardcoded credentials, weak password storage, insecure session handling, missing authentication checks
  • Cryptographic failures: Weak encryption algorithms, insecure random number generation, improper certificate validation
  • Additional vulnerabilities: Buffer overflows, path traversal, insecure deserialization, race conditions, memory leaks, insecure API usage

SAST tools detect these issues by analyzing code structure, data flow, and control flow patterns. The most advanced tools can also identify business logic flaws, code quality issues that impact security, and violations of secure coding standards. However, SAST cannot detect runtime-specific vulnerabilities like server misconfigurations or infrastructure issues, which is why it's most effective when combined with DAST and other security testing methods.

Static code analysis works by examining source code without executing the application. The process begins when a SAST tool parses your code and breaks it down into tokens: individual elements like keywords, operators, and identifiers. These tokens are then used to construct an Abstract Syntax Tree (AST), which represents the hierarchical structure of your code.

Once the AST is built, SAST tools may perform several types of analysis: control flow analysis maps all possible execution paths through your code, data flow analysis tracks how data moves from inputs to outputs, and pattern matching compares code structures against known vulnerability signatures. The tool checks for issues like unsanitized user input flowing into database queries, hardcoded credentials, insecure API usage, and violations of secure coding standards.

SAST tools vary significantly in programming language support, with most covering popular languages while some specialize in specific ecosystems.

Commonly supported languages (most tools):

  • JavaScript/TypeScript
  • Python
  • Java
  • C#/.NET
  • PHP
  • Ruby
  • Go

Frequently supported (major tools):

  • C/C++
  • Swift/Objective-C
  • Kotlin
  • Scala
  • HTML/CSS
  • SQL

Less common (specialized tools):

  • Rust
  • Perl
  • COBOL
  • Apex (Salesforce)
  • Dart
  • R

Multi-language environment challenges: Teams working with polyglot codebases face a critical decision: use a single SAST tool with broad language coverage (potentially sacrificing depth) or deploy multiple specialized tools (adding complexity but improving accuracy).

Integrating SAST into CI/CD pipelines enables automated security scanning with every code change, catching vulnerabilities before they reach production.

Choose your integration point:

  • Pre-commit hooks: Scan locally before code is pushed (fastest feedback)
  • Pull request/merge request: Scan during code review (balances speed and coverage)
  • Build stage: Scan during continuous integration (comprehensive checks)
  • Scheduled scans: Run full codebase scans nightly or weekly

Implementation best practices:

  • Start with non-blocking scans to establish baselines without disrupting workflows
  • Configure quality gates to fail builds only on critical/high-severity issues
  • Scan only changed code in PRs for faster feedback (full scans periodically)
  • Integrate results into developer tools (IDE plugins, Jira, Slack notifications)
  • Establish clear remediation SLAs based on severity levels

Successful SAST integration requires balancing security thoroughness with developer velocity. Start with critical projects, tune false positives, and gradually expand coverage across your organization.

SAST and DAST are complementary testing methodologies that examine applications from different perspectives. SAST analyzes code at rest during development, while DAST tests running applications in production-like environments.

AspectSASTDAST
Testing approachWhite-box (inside-out)Black-box (outside-in)
When it runsDuring coding / buildAgainst built application
Code accessRequires source codeNo source code needed
DetectsCode errors, logic flawsRuntime vulnerabilities, configuration issues
SpeedFast, can scan continuouslySlower, tests at runtime
False positivesHigher rateLower rate

For comprehensive security coverage, leading organizations use both SAST and DAST together as part of a layered application security strategy.

Get started with Harness AI SAST

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