AI SAST
AI SAST (AI-powered static application security testing) represents an evolution in how organizations identify vulnerabilities in their source code. By leveraging the reasoning capabilities of large language models (LLMs), AI SAST can understand code context and behavior in ways that traditional rule-matching approaches cannot.
Traditional SAST tools analyze source code without executing it, searching for patterns that match known vulnerability signatures. While effective for certain vulnerability classes, these tools operate on rigid rules that must be defined for each programming language and framework. This approach creates two persistent challenges: false positives that overwhelm development teams and blind spots where context-dependent vulnerabilities slip through.
AI SAST addresses these limitations by introducing an intelligent reasoning layer. Rather than simply matching patterns, AI models can interpret code semantically though multi-file testing, understanding how functions interact, how data flows through an application, and whether a potential vulnerability is actually exploitable given the surrounding context. This enables the detection of critical vulnerabilities—such as SQL injections and XSS—that previously required human review to identify without false positives.
Consider the difference this way: Traditional SAST is like a spell-checker that flags every word not in its dictionary. AI SAST is like a human editor who understands grammar, context, and intent—capable of distinguishing between a genuine error and an intentional stylistic choice.
This contextual understanding becomes increasingly critical as modern software development grows more complex. Microservices architectures, polyglot codebases, and the proliferation of AI-generated code all demand security tools that can reason about code rather than merely scan it. Tools that can catch what previously only humans could.
How AI SAST works
The AI SAST process begins with extracting functions from the source code. From these functions, the system identifies vulnerability candidates—code patterns that warrant deeper analysis. An AI model then evaluates each candidate alongside its surrounding context to confirm or discard the existence of a vulnerability.
This approach differs fundamentally from traditional SAST. Where conventional tools apply rigid rules that must be explicitly satisfied for something to be flagged as vulnerable, AI SAST leverages the reasoning capabilities of LLMs to understand code the way a human reviewer would. The model can follow complex data flows, recognize custom or framework-specific patterns, and make nuanced judgments about whether a particular code construct actually poses a security risk.
The key advantage is flexibility. Traditional SAST struggles in scenarios where context is vital, where human judgment would typically be required to determine if a vulnerability truly exists. AI SAST bridges this gap by approximating that human reasoning at machine speed.
AI SAST vs. traditional SAST: A comparison
Comparing AI SAST versus traditional SAST requires appreciating their distinct strengths and limitations.
Contextual understanding
Traditional SAST has inherent limitations in obtaining and utilizing the context necessary for processes where human judgment is key. This typically results in high false positive rates, as the tool cannot distinguish between a dangerous pattern and a safely handled one. AI SAST excels precisely where traditional tools struggle, providing the contextual flexibility that complex vulnerability assessment demands.
Severity of findings
Traditional SAST struggles to reliably detect critical vulnerabilities like SQL injection and XSS—flaws that typically required human review to identify accurately. AI SAST brings these high-severity findings within reach of automation.
Language support
Traditional SAST requires specific rules for each programming language, meaning coverage depends entirely on vendor investment in rule development. AI SAST is language-agnostic, allowing the same analytical approach to be applied across multiple programming languages without maintaining separate rule sets for each.
Development speed
AI SAST enables faster development of new detection capabilities. Creating rules for traditional SAST requires extensive manual effort and testing for each vulnerability type and language combination. AI models can be trained more efficiently to recognize new vulnerability patterns.
Execution considerations
Traditional SAST generally executes faster and at lower cost per scan. AI SAST, while slower in raw execution time, often delivers superior results that reduce overall remediation effort. The initial investment in AI-powered analysis frequently pays dividends through reduced time spent triaging false positives.
Precision
When properly optimized, AI SAST can achieve precision rates exceeding 90%, dramatically reducing the false positive burden on development teams. However, this requires careful tuning and validation: AI models can hallucinate, generating false positives that must be controlled through rigorous methodology.
AI SAST challenges conquered
Developing effective AI SAST involves overcoming significant technical and methodological challenges. Organizations considering AI SAST adoption should understand what this still ongoing journey entails.
Managing non-determinism
AI-based systems do not produce completely deterministic results. The same input may yield slightly different outputs across runs, complicating both testing and consistent performance measurement. Robust AI SAST solutions must account for this variability in their validation processes and work on enhancing their results.
Controlling hallucinations
LLMs can generate confident-sounding but incorrect conclusions, a phenomenon known as hallucination. In security testing, this manifests as false positives: vulnerabilities reported that do not actually exist. Significant effort must be invested in reducing hallucination rates to acceptable levels.
Minimizing false negatives
While false positives waste developer time, false negatives—real vulnerabilities that go undetected—pose actual security risks. Effective AI SAST must balance precision (avoiding false positives) with recall (catching real vulnerabilities).
Optimizing context and cost
LLMs require sufficient context to make accurate determinations about vulnerability existence. However, more context means more tokens processed, which directly impacts operational costs. Early AI SAST experiments often prove economically unviable until this balance is optimized.
At Fluid Attacks, our development process addressed each of these challenges through controlled experimentation. We tested against targets of evaluation with high densities of known SQL injection and XSS vulnerabilities. Findings that matched known vulnerabilities became true positives; known vulnerabilities not detected became false negatives; new findings underwent manual verification by security experts to determine if they were genuine discoveries or false positives.
This rigorous methodology enabled us to achieve a 92% reduction in required analyses and a 98% reduction in costs compared to initial estimates, transforming AI SAST from an interesting experiment into a production-ready capability.
Why security testing still needs human expertise
Despite the achievements of AI SAST, artificial intelligence alone is not enough. The highest-quality security outcomes emerge when AI capabilities combine with human expertise.
AI excels at speed, scale, and endurance. It can analyze vast codebases rapidly, handle multiple projects simultaneously, and work continuously without fatigue. But AI also has limitations: it can generate false positives through hallucination, its performance varies across vulnerability categories, and its non-deterministic nature means results may differ slightly between runs—all factors that benefit from human validation.
This is why Fluid Attacks integrates AI SAST within its Continuous Hacking solution, which combines AI-powered tools with human pentester expertise. AI SAST serves its purpose, while security experts investigate complex, highly diverse issues, and ensure that no critical vulnerability escapes detection.
This hybrid approach delivers what AI could not achieve alone: comprehensive vulnerability detection with low false positive rates, at speeds that match modern development cycles.
AI SAST for automated vulnerability closure
Beyond detection, AI SAST accelerates the verification of fixes. When a client requests a reattack on our platform for a vulnerability originally detected by AI SAST, there is no need to wait for a pentester to manually verify whether the issue was resolved. AI SAST automatically runs on demand, analyzes the code again, and confirms whether the vulnerability still exists or can be closed.
Evaluating AI SAST solutions
Organizations evaluating AI SAST solutions should assess capabilities across several dimensions.
Detection quality
Examine precision rates against established benchmarks. Look for demonstrated performance against real-world vulnerability types relevant to your technology stack. Consider whether the solution handles your specific languages, frameworks, and architectural patterns effectively.
False positive rates
Evaluate the solution's precision—the percentage of reported vulnerabilities that are genuine issues. High false positive rates waste developer time and erode trust in security tools. Look for solutions demonstrating precision rates above 90%, validated through rigorous testing against known vulnerability sets.
Integration with human review
The best AI SAST solutions acknowledge that AI alone is insufficient. Evaluate how the tool integrates with human security expertise. Basically: Does the solution support workflows where AI and humans collaborate effectively?
Privacy and compliance
Source code is sensitive intellectual property. Ensure the solution meets your data protection requirements. Key considerations include data retention policies, third-party data sharing, encryption in transit and at rest, access controls, and regulatory compliance.
AI SAST privacy and data protection
Enterprises rightfully demand rigorous data protection for any tool that accesses source code. AI SAST solutions must address these concerns transparently.
Fluid Attacks' AI SAST is designed with privacy as a foundational requirement. The system does not process or store client data. No data is shared with third parties. All data in transit and at rest is encrypted. Importantly, access to client repositories and testing results is restricted through role-based controls and audited authentication.
AI SAST summary
AI SAST combines artificial intelligence with static application security testing to deliver contextual, accurate vulnerability detection that traditional rule-based scanners cannot achieve.
AI SAST detects critical vulnerabilities—such as SQL injection and XSS—that traditional SAST cannot find without generating excessive false positives.
The real power of AI SAST emerges when paired with expert intelligence. Automated intelligence handles speed and scale, while security experts validate findings and uncover complex vulnerabilities that AI alone might miss.
AI SAST is language-agnostic, enabling consistent analysis across multiple programming languages without requiring language-specific rules.
Effective AI SAST implementation requires careful optimization. Through rigorous experimentation, organizations can achieve dramatic cost reductions—up to 98%—while maintaining over 90% precision.
Privacy and compliance remain paramount. Enterprise-grade AI SAST solutions must ensure data protection, regulatory compliance, and transparent handling of source code.
Getting started with AI SAST
AI SAST represents a significant advancement in application security testing, but realizing its benefits requires choosing the right approach. Tools that combine AI capabilities with human expertise deliver superior outcomes compared to either approach alone.
The AI SAST testing technique is available for the Advanced plan of our Continuous Hacking solution, which integrates AI-powered vulnerability detection with expert pentester validation and offers an exclusively high accuracy SLA. Contact us to start experiencing real AppSec.
