AI-SCA

Open source security reimagined. From dependency to runtime. The risk validation engine for agentic software development.

Guardian Agent

Legacy SCA matches CVE to dependency. AI-SCA reasons over your Software Graph like an expert application security engineer: confirms reachable, exploitable vulnerabilities from code to runtime, dismisses theoretical ones with evidence, and fixes without breaking changes.

What AI-SCA delivers

AI-SCA shifts open source security from CVE matching to risk validation and fix. Three outcomes follow.

Eliminate noise

Contextual analysis decides whether a vulnerability is used in code, internet exposed, and exploitable. The flood of contextless CVE alerts collapses into a short list of real risks.

Focus on real risk

Reachability, exploitability, and business impact decide priority, not CVSS score alone. Malicious packages, license conflicts, and AI components are covered beyond CVEs.

Reduce backlog and MTTR

Version bump fixes arrive pre-validated for compatibility, routed to the right code owner. Remediation accelerates without breaking builds or blocking delivery.

Legacy SCA was built for the pre-AI age

AI coding agents pull in dependencies faster than humans can review them, and attackers noticed: third-party breaches doubled in a single year, and malicious OSS packages grew 75% in 2025. Legacy scanners answer with CVE lists. Four systemic failures follow.

Endless noise

Every flagged CVE is treated as equal. Teams drown in contextless alerts and backlogs while developers lose trust and stop acting on findings.

Missed real threats

Malicious packages, repo confusion campaigns, and deep transitive dependencies evade database matching entirely. No CVE, no detection.

No reachability or runtime context

Legacy SCA can't tell whether the vulnerable function is ever executed, deployed, internet exposed, or touching sensitive data.

Breaking-change remediation

Blind version bumps break builds. License conflicts surface after the merge. Fixing one alert creates two new problems.

How AI-SCA works

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

Deep Code Analysis builds your dependency graph

AI is only effective when it has the right map. Deep Code Analysis (DCA) continuously builds a Software Graph of your entire software architecture across files, code modules, repositories, code owners and more - updated on every commit and material change, before the AI ever looks at a finding.

• Dependency scanning to the leaf node: direct, transitive, and custom-built internal dependencies
• Dynamic XBOM, the extended SBOM: every OSS package, internal package, API, AI model, data model, and license, refreshed with every code change
• Technology stack mapped across 750+ categories, including authn, authz, encryption, data access and AI

Database matching meets human-level reasoning built on your AppSec Data Fabric

Two complementing layers. Deterministic matching scans every component in your software architecture against vulnerability databases fast, generating every possible lead while identifying blast radius and toxic combinations. A specialized AI agent then acts as a virtual researcher on each lead, reasoning over your Software Graph and Risk Graph with a tailored triage workflow per risk level.

• Usage analysis: is the vulnerable function imported, called, and on an executed path
• Multidimensional risk factors beyond CVSS score, including packages free of CVEs that still pose risk
• Coverage of a scanner. Precision of a human researcher.

Code-to-Runtime matching answers the question no SCA ever could

Code-to-Runtime matching is part of Deep Code Analysis (DCA) technology. It answers the question that impacts the risk: is this vulnerable dependency actually reachable? Applicative fingerprinting automatically maps dependencies to deployed containers, API gateways, public endpoints, traffic patterns, and active security controls. No human labor required.

A critical CVE in a dev-only package never deployed stops paging your team. The same CVE in a dependency serving an internet-facing payment API gets escalated, enriched with exploitability intelligence like EPSS.

AI Fix upgrades dependencies without breaking changes

Detecting a vulnerable dependency is half the battle. Fixing what is a risk to your business without breaking the application is the real one. Blind version bumps break builds. AI-SCA uses Software Graph and Risk Graph (the Data Fabric) to guide risk-based remediation at the source.

• Version bump fixes pre-validated for API compatibility across your actual usage
• Smart location finds the single manifest change that remediates every affected service
• Risks tied to code owners, so the fix lands with the developer who can merge it

Beyond CVEs: malware, licenses, and the AI supply chain

The most dangerous supply chain risks have no CVE. AI-SCA reasons over behavior and context to catch what database matching never will.

• Malicious package detection: repo confusion, dependency confusion, self-replicating worm/malware like shai-hulud 2.0 and poisoned components. Apiiro research uncovered 100,000+ infected repos on GitHub in a single campaign.
• License compliance enforced as policy: restrictive and conflicting licenses flagged before the merge, not after legal exposure
• AI supply chain coverage: models, datasets, and AI-generated dependencies inventoried and secured alongside OSS

One CVE. Two verdicts.

Same critical CVE severity. Opposite risk reality. Both verdicts are possible only with contextual AI reasoning over the Software Graph. Database matching gets both wrong.

Critical CVE, reachable from a payment API

Why legacy SCA buries it: one row among thousands of equal-severity alerts, hidden three levels deep in the tree. AI-SCA traces the call path, confirms the parser runs on an internet-facing refund flow handling payment data, and escalates with exploit intelligence attached.

Same CVE severity, dismissed with evidence

Why legacy SCA flags it: the package and version match the database. The Software Graph proves the vulnerable function is never called and the dependency never ships to production. AI-SCA dismisses it with evidence, and your developers never see it.

The supply chain threat is compounding

Open source accelerated development. It also became the attack surface of choice. The numbers leave no room for contextless scanning.

• 30% of all breaches involve a third party, doubled from 15% in one year (Verizon DBIR 2025)
• 454K+ new malicious OSS packages in 2025, up 75% year over year (Sonatype State of the Software Supply Chain 2026)
• $4.91M average cost of a supply chain breach, 267 days to contain (IBM Cost of a Data Breach 2025)
• 100K+ infected GitHub repos uncovered in a single repo confusion campaign (Apiiro research)

Every dependency is a component you don't control. AI-SCA puts each one in the context of your application attack surface, continuously.