AI-SAST

Static scanning reimagined. From code to runtime.

Missed business logic flaws

Deep architectural understanding removes false positives. Not static rules, not manual triage. Customers report 85 to 90%+ reduction within weeks.

AI-SAST

AI Fix

Application security leaders don't need more findings. They need clarity on what matters, why it matters, and how to fix it safely at scale.

Dormant RCE, verified by data-flow reachability

94%

<50%

AppSec teams drown in alerts that don't impact the business. Developers lose trust in the findings and stop acting on them.

Why scanners miss it: payload and execution are separated by time and location. A forgotten endpoint poisons system state; a periodic job detonates it later. AI-SAST traces the flow across the Software Graph, confirms the missing access control, and escalates: attackers can take over the server.

Code-to-Runtime

Apiiro massively reduced the false positives. In our environment, we've seen something like a 90%+ reduction in findings. The bad kind, the ones that weren't useful. Work becomes faster, easier, and more productive.

Guardian Agent

No remediation context

Detect real, exploitable vulnerabilities, including business logic flaws legacy scanners miss entirely. Reachability from code to runtime decides priority.

Focus on real risk

Apiiro AI-SAST

AST + AI Agent

How AI-SAST works

Critical alert, dismissed with evidence

Multi-step flows like authorization bypasses evade pattern matching entirely. The vulnerabilities attackers actually exploit go undetected.

Guardian Agent

Apiiro AI-SAST

Eliminate noise

Static scanning reimagined. From code to runtime. The risk validation engine for agentic software development.

What customers see in weeks

Even true positives arrive with no explanation of where the issue originates, how it propagates, or how to fix it without regressions.

Read the technical deep dive blog →

Reduce backlog and MTTR

Leading legacy SAST vendors

One finding. Two verdicts.

Benchmarked on real enterprise code

Deep Code Analysis

Validated findings and contextual root-cause fixes accelerate remediation, cut operational cost, and unblock software delivery.

What AI-SAST delivers

Legacy SAST detects patterns. AI-SAST validates risk. It reasons over your Software Graph like an expert application security engineer: confirms true positives, dismisses false positives with evidence, and fixes at the root. Built on your AppSec Data Fabric.

Deep Code Analysis dramatically reduced false positives in our environment within weeks. By mapping SAST findings to API entry points, we can better prioritise the risks that matter most.

Explore the Data Fabric that powers it →

AI-SAST shifts static analysis from a detection problem to a risk validation and fix problem. Three outcomes follow.

Leading legacy SAST vendors

Real examples from a single enterprise repository, anonymized. Both verdicts are possible only with contextual AI reasoning over the Software Graph. Pattern matching gets both wrong.

Endless noise

AI coding agents increased development velocity 5x and application risk 10x. Legacy scanners now generate millions of findings in large enterprises with no way to tell which represent real, exploitable risk. Four systemic failures follow.

Why scanners flag it: input hits a shell command. The Software Graph proves every path is blocked: one by validation buried in a constructor, the other by a configuration flag that renders the code unreachable. AI-SAST dismisses it with evidence, and your developers never see it.

No runtime exposure

90%

Legacy SAST was built for the pre-AI age

Five capabilities replicate the cognitive process of an expert application security researcher: automated, continuous, at enterprise scale.

24-27%

Apiiro's AI-SAST, powered by Deep Code Analysis, reduced false positives by over 85% within weeks. By mapping SAST risks to internet-facing API entry points, we can confidently prioritize real exploited risks and help our developers increase development velocity.

Precision: how many detected results were true. Recall: how many real vulnerabilities in the True Set were detected. Legacy recall stays low because pre-AI tools exclude high-noise rules instead of triaging them.

Curated open source benchmark apps create misleading results. AI-SAST is measured against a True Set of 322 validated vulnerabilities mapped by Apiiro researchers across tens of production repositories.

Recall

Legacy SAST can't tell whether vulnerable code is deployed, reachable from the internet, or already mitigated by existing controls.

Precision

Recall

Adaptive Feedback

Precision