AI SAST is static application security testing that uses large language models and other machine learning, alongside program analysis, to find, verify, prioritize, and fix vulnerabilities in source code before it runs. It reasons about what code is meant to do, not only what patterns it contains.

That one difference, reasoning versus matching, is why the category exists. Traditional SAST has been essential for two decades, and it is still the cheapest place to catch a bug. But it was built on hand-written rules and source-to-sink queries, and those methods cannot express “this endpoint modifies data without checking who is calling it.” AI SAST can.

This guide explains what AI SAST is, how the terms AI-powered and AI-native differ, how an AI SAST pipeline actually works, what it catches that rules miss, where it fails, and how to evaluate a tool with numbers rather than demos.

What is AI SAST?

Static application security testing analyzes code at rest, in the IDE, in a pull request, or in CI, without executing the application. AI SAST keeps that definition and changes the analysis engine.

A rules-based scanner has three parts: a parser that turns code into a syntax tree, a set of detection rules or data-flow queries, and a reporter. Every finding traces back to a rule a human wrote. If nobody wrote a rule for a flaw, the scanner cannot find it. If the rule is too broad, the scanner floods you with false positives. Published studies on commercial scanners found precision between 18% and 36% and false negative rates between 56% and 68%, which is why our BLAST whitepaper opens with the observation that AppSec teams have massive blind spots and low trust in what they do see.

AI SAST adds a model that has read an enormous amount of code and can hold the context of a codebase in working memory. Given the right slices of a project, the model can answer questions rules cannot:

  • Is this route reachable without authentication, given the middleware configured in settings.py?
  • Does this handler enforce that the caller owns the record it is updating?
  • Is this hardcoded credential in production code or in a test fixture?
  • Does this validation function actually neutralize the input that reaches the SQL query three files away?

Those are the questions a security engineer asks during a code review. AI SAST is, in the simplest framing, static analysis that behaves like a code reviewer with security expertise, running on every change.

The category is young. Corgea introduced its LLM-based scanner in 2024 and published the technical approach as BLAST. Since then almost every SAST vendor has added “AI” to its positioning, which is exactly why the terminology needs sorting out.

AI SAST vs AI-powered SAST vs AI-native SAST

Three terms get used interchangeably, and they do not mean the same thing. The difference is where the model sits in the pipeline.

AI SAST is the umbrella term. It covers any static analysis product that uses machine learning or LLMs somewhere in its workflow.

AI-powered SAST (sometimes called AI-assisted or AI-augmented) keeps a conventional detection engine. Rules, patterns, and taint tracking still decide what counts as a finding. AI is applied afterward, to the output: explaining findings in plain language, grouping duplicates, predicting which alerts are likely false positives, suggesting a fix, or helping you write custom rules. This is where most established vendors are today. Semgrep Assistant, Snyk’s DeepCode AI and Agent Fix, Checkmarx’s AI Query Builder and Developer Assist, and Veracode Fix all fit this pattern: a rules or data-flow core with AI around it.

AI-native SAST puts the model inside detection. The scanner still parses code and builds structure, but the question “is this vulnerable?” is answered by a model reasoning over the relevant code and context, and then checked. This is the approach Corgea took with BLAST, and it is the one that a wave of academic research since 2024 has explored. We described the shift in The Three Waves of SAST: wave one was enterprise rule engines, wave two was developer-first rule engines, and wave three rebuilds the scanner around reasoning.

AI-powered SASTAI-native SAST
Detection engineRules, patterns, taint trackingLLM reasoning over parsed code plus program analysis
Where AI is appliedAfter the scan, on the findingsDuring the scan, and again on the findings
Can find issues without a ruleNoYes
Business logic and authorization flawsRarely, only if a rule can express themCore strength
False positive handlingAI predicts which rule hits are noiseModel reasons about context before reporting; verification pass after
Fix generationYes, usuallyYes, with the same code context used for detection
DeterminismHigh for detection, lower for the AI layerLower; needs engineering for repeatability
Compute costLow for detection, moderate for the AI layerHigher; needs scoping and caching strategies
Custom rule maintenanceStill requiredReplaced by natural-language policy and examples

The distinction matters to a buyer for one reason: a bolted-on model can only improve findings the underlying engine already produced. If the rule engine never flagged a missing authorization check, there is nothing for the AI layer to triage, explain, or fix. Ask every vendor where the model actually sits.

How AI SAST works

A well-engineered AI SAST pipeline is not “send the repo to a chatbot.” Pasting a whole codebase into a prompt does not work; context windows are finite, most files are irrelevant to any given question, and irrelevant context lowers accuracy. Nor does generic retrieval-augmented search work well: as the BLAST whitepaper shows, a simple semantic query over a large codebase can return dozens of loosely related files, including migrations and tests, which balloons context and destroys precision.

Instead, the tools that work combine several stages. The exact architecture varies by vendor, but the stages below describe Corgea’s approach and are representative of AI-native SAST in general.

1. Project parsing and context gathering

The scanner first builds a structural map of the project: abstract syntax trees for each file, symbol tables, import graphs, route definitions, framework configuration, middleware, dependency injection wiring, and templates. This is classic static analysis and it is what makes the later steps precise. Corgea’s CodeIQ engine does this to understand how components relate before any model is involved, and then decides which parts of the application to bring together as context for each question.

The goal of this stage is to give the model the right slices of the codebase, not all of it. For a login view in a Django app, that means the view, the URL route, the authentication middleware configured in settings, the form or serializer, and the user model, and nothing else.

2. Semantic and data-flow understanding

With structure in hand, the analyzer traces how data moves: from request parameters, headers, and file inputs through validation, transformation, and business logic to sinks like database queries, shell commands, file writes, and HTTP calls. Traditional taint tracking does this too, but it breaks on dynamic dispatch, reflection, framework magic, and cross-service boundaries.

AI SAST uses the structural analysis as scaffolding and the model to fill in what the scaffolding cannot see. The model can recognize that a decorator enforces authentication, that an ORM call is parameterized, that a helper named sanitize_html does what its name suggests (or does not), and that a value flowing from a message queue originated from an untrusted user.

3. LLM detection

This is the step that separates AI-native from AI-powered. For each unit of analysis, a function, a handler, an endpoint, the model is asked, with the assembled context, whether it contains a vulnerability, which one, and why. Because the model understands intent, it can flag:

  • an API endpoint that accepts GET, POST, PATCH, and DELETE without any identity check;
  • a purchase flow where the client-supplied price is trusted;
  • a password reset that leaks whether an email is registered;
  • a file upload that validates extension but not content;
  • a log statement that writes a full request body containing credentials.

None of these has a syntactic signature a rule can reliably match. All of them are among the most exploited classes of web vulnerability.

4. Verification and false-positive filtering

A candidate finding from a model is a hypothesis, not a result. The model can be wrong in both directions: it can hallucinate a vulnerability that a mitigating control elsewhere prevents, or it can miss one. Serious AI SAST tools therefore run a second pass that tries to disprove each candidate: is there an upstream check, a framework default, a compensating control, or a test-only context that makes this safe?

The BLAST whitepaper gives a concrete example. A conventional scanner flagged a Django view for CSRF because of a @csrf_exempt decorator. Corgea’s verification step read the surrounding code, saw that the route required an authenticated user, validated input, and performed server-side logic, and concluded the exemption was deliberate and mitigated. That finding was suppressed with an explanation rather than dumped on a developer.

The same false-positive filtering can be applied to findings from other scanners. Corgea’s auto-fix and triage layer accepts results from Checkmarx, Fortify, GitHub Advanced Security, Semgrep, and Snyk, which is a practical way to get AI verification on a legacy scanner’s output without replacing it.

5. Reachability and prioritization

Not every true positive matters equally. A SQL injection in an internal admin script behind VPN is a different risk than one on a public endpoint. AI SAST tools increasingly compute reachability: which findings sit on a code path that an external request can actually hit.

Corgea’s reachability analysis discovers web endpoints (path and HTTP method) in the repository, resolves each endpoint’s handler, builds a call graph from the handler to any vulnerable function, and returns a verdict with path depth and evidence. A finding with no path from an entry point is far less likely to be exploitable and can be deprioritized; a finding two hops from /api/orders goes to the top of the list.

6. Auto-fix with validation

Once a finding is verified and prioritized, the same context that found it is used to fix it. The model generates a patch that follows the codebase’s conventions, the framework’s idioms, and any organizational policy. The critical part is what happens next.

A generated fix should not be trusted on its own. In Corgea’s pipeline, each fix runs through quality checks; fixes that fail those checks are re-attempted rather than applied. The smarter auto-fixing release describes this self-healing loop, with fix coverage around 85% of findings and quality up 8% after the change. The output is a pull request or an IDE suggestion with a rationale a reviewer can check, never a silent commit. Corgea reports fix accuracy above 90% on its product page, and Latio Tech’s analyst evaluation ranked it the best SAST auto-fixing solution among seven tools tested, scoring fixes on coverage multiplied by quality.

7. Policy and organizational context

Rules-based scanners encode organizational knowledge as custom rules in a DSL, which is expensive to write and maintain. AI SAST can take that knowledge as natural language and examples.

Corgea’s PolicyIQ lets teams describe their business domain, trust boundaries, data classification, compensating controls, and compliance requirements, attach code examples, and apply the policy to specific projects. The model then uses that context to tailor detection, suppress findings that a legitimate control mitigates, and shape fixes to match internal frameworks. The policy-as-code variant lets the same context live in version control next to the code.

This is the mechanism that replaces rule debt. Instead of “write a Semgrep rule for our custom auth decorator,” the instruction becomes “functions decorated with @require_tenant are authorized; treat them as such.”

What AI SAST finds that rules-based SAST misses

Rules are excellent at what they were designed for: injection sinks, dangerous functions, weak cryptography, hardcoded secrets, insecure deserialization. AI SAST does not replace that coverage; it extends it into the classes of vulnerability that require understanding intent.

Missing or broken authorization (CWE-285, CWE-862, CWE-639). The most common critical finding in real applications is an endpoint that lets a user act on data they do not own. No syntactic pattern distinguishes Order.objects.get(id=order_id) from Order.objects.get(id=order_id, owner=request.user); only the intent of the endpoint tells you which is correct. In the Corgea vs. Snyk benchmark, missing authorization on data-modifying FastAPI routes was one of the confirmed issues that Snyk did not report. The same class was missed by Aikido in the Corgea vs. Aikido benchmark.

Authentication flaws (CWE-287). Endpoints exposed without any identity check, multi-step flows where a later step does not re-verify the earlier one, weak password policies, and reset flows that leak account existence. BLAST’s PyGoat example, an endpoint accepting four HTTP methods with the authentication decorator commented out, is exactly this.

Business logic vulnerabilities (CWE-840). Price or quantity tampering, workflow steps that can be skipped or replayed, race conditions in balance updates, coupon reuse, and limit bypasses. These are design flaws, and detecting them requires the analyzer to infer what the design intended.

Multi-file and framework-mediated flows. A rule engine sees one file’s call graph. AI SAST can follow a request through a router, a middleware stack, a controller, a service layer, and an ORM, and account for framework behavior that is configured rather than called. The Django brute-force-protection middleware example in the BLAST whitepaper is the canonical case: a rule that inspects the login view alone reports a false positive, because the protection is declared in settings.py.

Sensitive data exposure in context. Logging is not a vulnerability; logging a password is. Returning a user object is fine; returning its password hash is not. These findings depend entirely on what the data is, which a model can infer from names, types, and usage.

Context-dependent false positives. The flip side of all of the above. A hardcoded credential in a test fixture, a CSRF exemption on an authenticated API, a “SQL injection” that is actually a parameterized query, a dangerouslySetInnerHTML fed by a sanitizer. Rules cannot tell these from real issues. AI SAST can, and the effect on alert volume is large: Corgea’s product data reports roughly three times fewer false positives than conventional scanners on the same code.

The pattern across all of these is the same: the vulnerability lives in the gap between what the code does and what the code was supposed to do. Rules can only see the first half.

Limitations of AI SAST and the hallucination problem

AI SAST is not magic, and vendors who claim otherwise should lose credibility. These are the real constraints.

Hallucination. A language model asked “is this vulnerable?” will sometimes say yes to safe code, with a confident explanation. This is the single most important failure mode to test for. Corgea’s 12-model benchmark was built specifically around it: of the 1,913 labeled cases, 351 (18%) are hard negatives, code that pattern-matches to a vulnerability but is safe. A tool that reports a high detection rate without a hard-negative set is reporting a number that is close to meaningless, because a scanner that flags every database query catches 100% of SQL injection. The mitigation is architectural: ground the model in parsed code rather than raw text, require it to cite the path and the missing control, run a verification pass that tries to disprove each candidate, and measure precision against ground truth.

Missed findings. Models miss things too. On that same corpus, the best single-model profile reached about 40% recall at above 90% precision, with the rest of the ranked field between roughly 32% and 40%. That is the recall of a bare model on hard cases, before the structural analysis, multi-pass scanning, and policy context a full product adds, but it is a useful reminder that “AI” is not a synonym for “complete.” Any honest AI SAST vendor should be able to tell you their measured recall and what corpus it was measured on.

Non-determinism. Run a rule engine twice on the same commit and you get the same output. Run a model twice and you may not. Production AI SAST tools need seeding, snapshotting, caching, and result reconciliation so the same pull request yields consistent findings, and so a fix you applied yesterday does not resurface as a new finding tomorrow. Ask how the vendor handles this.

Cost and scan time. Reasoning over code with a large model is more expensive than running a regex over it. Scanning a monorepo naively is prohibitively slow and costly. Real implementations scope analysis to changed code, cache structural analysis, and use cheaper models or classical analysis for the easy cases. Expect a cost conversation, and expect the vendor to have levers.

Language and framework coverage. Model reasoning generalizes across languages better than rules do, but the structural analysis underneath still has to parse each language and understand each framework’s routing, middleware, and ORM conventions. Verify that your stack is covered rather than assuming it.

Data handling. AI SAST sends code to a model. Where does that model run, who operates it, is your code retained, and is it used for training? These are contract questions, but they are also architecture questions; ask them early.

Explainability versus correctness. A model can write a persuasive paragraph about a vulnerability that does not exist. Good explanations are valuable, but they are not evidence. Treat the explanation as a claim to check against the cited code path.

None of these limitations argues against AI SAST. They argue for evaluating it properly.

How to evaluate AI SAST tools

Vendor demos use the vendor’s repository, the vendor’s configuration, and the vendor’s time limit. A buyer needs a pilot that answers a narrower question: does this tool produce enough trusted signal in our repositories and our workflow to justify operating it? The full pilot procedure, with a downloadable scorecard, is in How to evaluate SAST tools. The points below are the AI-specific additions.

Define what you are buying AI for

Detection, triage, or remediation are different problems, and most products do one exceptionally well. Name your bottleneck first. If your existing scanner already finds what matters and you drown in noise, AI triage on top of it may be enough. If your incidents come from authorization and logic flaws that no scanner flagged, you need AI-native detection, and no amount of AI triage will help.

Build ground truth with hard negatives

Use a fixed corpus: a deliberately vulnerable application, past incidents from your own history, or a labeled slice of a representative repository. Label every case as true positive, false positive, or unknown, and deliberately include safe code that looks dangerous: parameterized queries that resemble injection, test fixtures with fake secrets, authenticated routes with CSRF exemptions, sanitized HTML output. Without those, you cannot measure hallucination, and a tool that is simply aggressive will look best.

Score precision, recall, and F1, not finding counts

More findings is not better. Count confirmed true positives, false positives, and false negatives against your labels, and compute precision, recall, and F1 for every tool on the same commit. Corgea publishes benchmarks in exactly this form. On the latiotech/insecure-kubernetes-deployments repository with 47 reviewed issues, Corgea reported 51 findings with 42 true positives, 9 false positives, and 5 misses, for precision of 82%, recall of 89%, and F1 of 0.86. Snyk reached 79% precision and 55% recall on the same code; Aikido reached 87% precision and 28% recall. Read those as a hypothesis to test on your code, not a procurement result, but read them as the right shape of evidence.

Test the AI-specific behaviors

  • Repeatability: scan the same commit three times. Do the findings match?
  • Explanations: could a junior developer understand and verify the issue from the explanation alone? Does it cite a code path?
  • Verification: feed it a few known false positives from your current scanner. Does it suppress them with a reason?
  • Fix quality: apply generated fixes for a sample of findings. Are they correct, idiomatic, and free of regressions? Track acceptance rate and revert rate.
  • Policy: describe one internal control in natural language. Does the tool use it on the next scan?
  • Scan modes: compare the PR check, the full scan, and the IDE experience separately. Do not compare one vendor’s deep audit against another’s default diff scan.

Check the operational questions

Scan time on your largest repository, cost model and levers, language and framework coverage for your actual stack, where the model runs and whether code is retained, integrations with your source control and CI, and the vendor’s release cadence. Static analysis is a long-term investment; in a field moving this fast, the pace of improvement is part of the product.

Compare at least two tools with the same criteria

The AI SAST market is not one product. Run two or three candidates through the same corpus with the same scoring, and let the numbers, not the demos, decide.

AI SAST vs traditional SAST vs DAST

AI SAST is still SAST: it analyzes code at rest. It does not replace dynamic testing, which exercises a running application from the outside. The three approaches fit together; the table shows where each one is strong.

Traditional SASTAI SASTDAST
What it analyzesSource code, bytecode, or binaries at restSource code at rest, with semantic contextA running application via HTTP requests
How it detectsRules, patterns, taint trackingLLM reasoning plus program analysis, then verificationSending payloads and observing responses
When it runsIDE, pull request, CIIDE, pull request, CIStaging or production, after deploy
Injection, XSS, dangerous functionsStrongStrongStrong when reachable
Business logic and authorization flawsWeakStrongPartial; needs authenticated, scripted tests
Multi-file and framework-mediated flowsPartialStrongNot visible; sees only behavior
Runtime and configuration issuesNot visibleNot visibleStrong
False positive rateHigh without tuningLow; context-aware filteringLow; observes real behavior
Explains the root cause in codeLine and rule IDPlain-language rationale with code pathRequest and response only
Fix generationRarelyYes, with validationNo
Custom knowledgeRule DSLNatural-language policyTest scripts
Cost per scanLowModerate to highModerate; needs an environment
Best forBaseline coverage of known patternsCatching what rules miss, cutting noise, fixing fastConfirming exploitability in the deployed app

The practical program is: AI SAST on every pull request for early, contextual coverage; DAST against staging for runtime confirmation; and software composition analysis for dependencies. For the head-to-head, see SAST vs DAST and SAST vs SCA vs DAST.

AI SAST tools

The market splits along the AI-powered versus AI-native line described above. In brief:

  • Corgea is an AI-native SAST platform. LLM-driven detection sits inside the scanner, backed by structural analysis, a verification pass, endpoint reachability, PolicyIQ for organizational context, and auto-fix with quality gates, across 20+ languages and frameworks. It also ingests findings from other scanners to triage and fix them. See the AI SAST product page.
  • Checkmarx pairs its enterprise SAST engine with AI for query building and agentic remediation in the IDE.
  • Veracode centers its AI on Veracode Fix, which generates patches for findings from its static engine.
  • Snyk Code uses DeepCode AI for detection and Agent Fix for retested fix suggestions, on a data-flow foundation.
  • Semgrep adds Semgrep Assistant for noise filtering, explanations, and remediation guidance on top of its rule engine.
  • Qwiet AI builds on a code property graph with LLM-generated AutoFix suggestions.

For ranked, criteria-based comparisons, use the best SAST tools guide for the full static analysis market and the best AI code security tools guide for products built around AI detection and remediation. Both are maintained alongside this page.

Where AI SAST fits in an AppSec program

AI SAST changes two things about how a program operates.

First, the unit of value moves from “a matched rule” to “a defensible code review with context.” A finding arrives with the path, the missing control, the exploit scenario, and a proposed fix. That changes who can act on it: developers can resolve most findings without a security engineer translating them, which is the only way remediation scales past the size of the AppSec team.

Second, custom knowledge stops being rule debt. The context that used to live in a security engineer’s head, or in a brittle set of custom rules, becomes policy that the scanner reads. That makes results more accurate over time without a maintenance tax.

What does not change: AI SAST is still one control. It runs before code executes, so it cannot see runtime configuration, deployed infrastructure, or third-party services. Pair it with DAST, dependency scanning, secrets detection, and infrastructure-as-code scanning, and keep human review in the loop for the fixes it proposes. The teams getting the most from it treat the tool as a reviewer whose work they check, not an oracle whose output they accept.

Frequently asked questions

What is AI SAST?

AI SAST is static application security testing that uses large language models and other machine learning alongside program analysis to find, verify, prioritize, and fix vulnerabilities in source code. Unlike rules-only scanners, it reasons about what code is meant to do, so it can catch business logic, authentication, and authorization flaws and filter out false positives.

What is the difference between AI SAST and traditional SAST?

Traditional SAST matches code against hand-written rules, patterns, and source-to-sink data-flow queries. AI SAST adds a model that understands intent and context across files and frameworks. In practice that means broader coverage of logic and access-control issues, fewer false positives, plain-language explanations, and generated fixes, at the cost of higher compute and the need to verify model output.

Is AI SAST accurate, and does it hallucinate?

A raw language model can hallucinate vulnerabilities or miss real ones, so accuracy depends on how the tool is engineered. Well-built AI SAST tools ground the model in parsed code, verify each candidate finding, filter false positives, and measure precision and recall against labeled ground truth. Judge any tool by a benchmark with hard negatives, or by a pilot on your own repositories, not by vendor detection-rate claims.

What are the best AI SAST tools?

Corgea is an AI-native SAST platform that pairs LLM-driven detection with verification, reachability analysis, and auto-fix. Checkmarx, Veracode, Snyk, Semgrep, and Qwiet AI are established scanners that have added AI for triage, query building, or remediation. Compare them by measured precision and recall on your code, fix quality, and workflow fit rather than by feature lists. The best SAST tools guide has the detailed comparison.

How does AI SAST auto-fix work?

AI SAST auto-fix generates a code change for a verified finding using the surrounding code, framework conventions, and any organizational policy as context. Strong implementations then run quality checks on the patch, retry when a fix fails those checks, and deliver the result as a reviewable pull request or IDE suggestion so a developer approves it before it merges.

What is the difference between AI-native SAST and AI-powered SAST?

AI-powered SAST keeps a traditional rules or data-flow engine for detection and applies AI afterward to explain, group, prioritize, or fix the results. AI-native SAST puts the model inside the detection step itself, reasoning about code intent and context to find issues rules cannot express. The distinction matters because bolted-on AI can only improve findings the underlying engine already produced.

Does AI SAST replace DAST?

No. AI SAST analyzes code before it runs and cannot see runtime configuration or deployed behavior. DAST tests the running application from the outside. Use AI SAST for early, contextual coverage on every change and DAST to confirm exploitability in a deployed environment.

Can AI SAST work with the scanner we already have?

Often, yes. Some AI SAST platforms, including Corgea, ingest findings from scanners such as Checkmarx, Fortify, GitHub Advanced Security, Semgrep, and Snyk, then apply AI verification, prioritization, and auto-fix to them. That is a low-risk way to pilot AI triage and remediation before changing your detection engine.