SAST, rebuilt from scratch with AI.Your static scanner reads code. AISLE understands it.
Traditional SAST tools search for patterns. AISLE reasons about your code the way a senior security researcher would - understanding context, logic, and intent. That is why it finds vulnerabilities that other scanners miss and helps you fix them.
SAST was a good idea.
The execution has not kept up.
Static Application Security Testing (SAST) analyzes code before it runs and catches vulnerabilities before they ship. The problem is how it has been implemented. Legacy tools scan code for known patterns and apply human-written rules to bugs humans have already seen. No real analysis is being performed.
That approach has two structural limits it cannot escape:
01It only catches what it's been taught.
Pattern-matching tools are blind to everything outside their ruleset. They are also blind to the same flaw in a different shape. Business logic flaws, race conditions, and complex authentication gaps require understanding code in context.
02It generates overwhelming noise.
Industry averages put false positive rates for pattern-based SAST at around 90%. For every real vulnerability, your engineers are busy investigating nine that do not matter. Alert fatigue, ignored findings, and a security backlog that grows faster than it gets resolved.
These are not fixable with better rules. They are the ceiling of the architecture.
AI-native SAST: the difference between reading code and understanding it.
AISLE is not a pattern matcher with an AI wrapper. It is a fundamentally different architecture where large language models reason about your actual code: what it does, what it is trying to do, and where the logic breaks down in ways an attacker could exploit.
AISLE reads and understands your codebase, analyzing it as an interconnected system while tracing how data flows through functions, modules, and services. This is how it finds business logic errors, broken access control, and subtle race conditions that pattern-based tools cannot detect. AISLE reasons about code across any language and any stack - no rulesets to maintain and no language-specific plugins to configure.
Any language. Any codebase. Including large monorepos that legacy SAST tools time out on.
Before a finding reaches your team, AISLE's agents argue about it. Multiple reasoning layers examine the same code independently - checking context, tracing data flows, and weighing confidence. Only the findings worth an engineer's time make it through. False positive rates drop by up to 90% compared to traditional SAST.
AISLE doesn't scan. It analyzes.
AISLE also covers SCA (open source dependency vulnerabilities), secrets scanning, and imports findings from existing DevOps scanners - consolidating security signal in one place. The same AI-powered fix generation and sandbox verification apply across all of these: findings do not just surface, they get resolved.
All your scanners feed in. One AI layer triages, fixes, and resolves.
The benchmark that matters: what does it find in the real world?
Synthetic benchmarks are easy to game. The real test is production: what the tool finds in real code and whether maintainers confirm those issues. AISLE has proven itself on heavily audited codebases.
Across 2025 and early 2026, AISLE has been credited with hundreds of CVEs across more than 30 critical projects — Linux kernel, glibc, Chromium, Firefox, Apache, curl, NASA's CryptoLib, and OpenSSL, among others. Every finding was externally validated.
OpenSSL may be the most telling example. As the cryptographic library at the foundation of most secure internet communications, it is one of the most heavily audited codebases in existence. In January 2026, OpenSSL released a coordinated security advisory covering 12 vulnerabilities. AISLE discovered all 12, including several that had been present in the codebase since 1998.
"This release is fixing 12 security issues, all disclosed to us by AISLE. We appreciate the high quality of the reports and their constructive collaboration with us throughout the remediation."
"I'm a little amazed by the amount of CVEs released by OpenSSL today. 12(!) of them were reported by people at Aisle... I mean if you are curious what AI can do for Open Source security when used for good."
AISLE vs tools your team is already running
| Capability | Legacy tools | AISLE |
|---|---|---|
| Detection | ||
| Detection method | Pattern matching against known rule sets | AI reasoning that understands code context and intent |
| Finds race conditions and complex auth gaps | Rarely | Multi-agent analysis across the full codebase |
| Language coverage | Rule-dependent, limited | Any language - AI-native, no rules required |
| Remediation | Paid AI add-ons on legacy tools | Full AI coding agent with iterative fix loop and fix verification |
| Noise and Triage | ||
| False positive rate | ~90% industry average | <10% with multi-agent cross-verification |
| Risk triage method | Static CVSS score | Contextual LLM triage that reads your actual code and findings from other tools |
| Enterprise and Security Infrastructure | ||
| Deployment model | Shared instance, isolation costs extra or means self-hosting overhead | Private SaaS instance with complete infrastructure isolation |
| Compliance | Varies by vendor | SOC 2, ISO 27001, ISO 42001; zero data retention — your code is never used for training |
| Single sign-on (SSO) | Paid add-on or enterprise tier only | Included by default |
Run AISLE on your codebase.
Two weeks. Free.
We offer a structured, two-week proof of value for qualified enterprise security teams. You point AISLE at your own codebase and see exactly what it finds, including what your current SAST tooling missed. No sales theater. Just results in your environment, on your code.
Available to qualified enterprise security teams.