Trace-AI

Product Introduction

1. Trace-AI is a supply-chain security platform that predicts and prevents attacks through metadata-driven analysis of open-source dependencies, registries, and maintainer activity without requiring access to source code. It generates real-time Software Bill of Materials (SBOMs) in CycloneDX and SPDX formats, tracks direct and transitive dependencies, and provides exploit-aware risk scoring. The platform integrates with CI/CD pipelines to automate vulnerability detection, license compliance validation, and vendor risk monitoring. Built by engineers with experience scaling systems to millions of users, it prioritizes developer efficiency while hardening software supply chains.
2. The core value lies in its ability to reduce false positives by focusing on actively exploitable vulnerabilities and contextualizing risks through maintainer behavior and dependency metadata. It replaces manual audits with automated, audit-ready reports mapped to ISO 27001, SOC 2, and other compliance frameworks. By correlating package registry data with threat intelligence, it identifies compromised maintainers, suspicious version updates, and third-party API risks. This enables teams to prioritize critical fixes and maintain compliance without slowing development cycles.

Main Features

1. Trace-AI generates live SBOMs in CycloneDX and SPDX formats by analyzing dependency manifests (e.g., package-lock.json, requirements.txt) and lock files during CI/CD execution. It automatically tracks version changes, transitive dependencies, and dependency graphs across npm, PyPI, Maven, and 10+ other package managers. SBOMs update in real time as code evolves, providing an immutable record for audits.
2. Exploit-aware vulnerability scanning filters CVEs using threat intelligence from NVD, GitHub Advisory, and proprietary exploit databases to flag only actively exploitable risks. It provides remediation context, including affected dependency paths, severity tags, and exploit maturity levels. This reduces alert fatigue by suppressing theoretical vulnerabilities without known attack vectors.
3. Vendor risk monitoring tracks API/SDK dependencies, SLA expiry dates, and historical breaches of third-party services integrated into the codebase. It correlates vendor uptime metrics with dependency versions to alert teams about unsupported or deprecated integrations. License compliance features auto-detect copyleft licenses (GPL, LGPL) and generate evidence for enterprise audits.

Problems Solved

1. Traditional software composition analysis (SCA) tools overwhelm teams with unprioritized CVE lists and static SBOMs that quickly become outdated. Trace-AI solves this by providing live SBOMs updated with every commit and exploit-contextualized alerts. It reduces remediation time by 60% compared to manual triage processes.
2. The platform targets engineering teams managing cloud-native applications, DevSecOps professionals responsible for compliance, and open-source maintainers securing their dependency trees. Enterprise security teams use it to enforce supply-chain policies across distributed development environments.
3. Typical use cases include pre-release vulnerability sweeps, continuous license compliance for regulated industries, and post-merger integration audits to identify legacy dependencies. It also detects compromised maintainer accounts by flagging abnormal release patterns or unsigned package updates.

Unique Advantages

1. Unlike black-box SCA tools, Trace-AI’s ZSBOM engine is fully open-source, allowing users to audit classification logic and customize risk scoring algorithms. All compliance policies are published as versioned YAML/JSON files that teams can fork and modify.
2. The platform uniquely combines SBOM generation with vendor risk profiles, tracking external services like AWS SDKs, payment gateways, and AI APIs alongside code dependencies. It maps vulnerabilities to specific API endpoints and service-level agreements (SLAs).
3. Competitive advantages include metadata-only analysis that avoids source code scanning, reducing compliance overhead for proprietary systems. Its exploit-aware filtering uses a proprietary scoring model that cross-references CVEs with dark web monitoring feeds and GitHub commit patterns.

Frequently Asked Questions (FAQ)

1. What is an SBOM and why do I need one? A Software Bill of Materials (SBOM) is a machine-readable inventory of all components in a software product, including direct and transitive dependencies. Trace-AI generates SBOMs in standard formats (CycloneDX, SPDX) required for compliance with regulations like the US Executive Order 14028. Real-time SBOMs help teams respond to zero-day vulnerabilities by instantly identifying affected dependencies.
2. How is exploit-aware scanning different from traditional CVE scanning? Traditional tools list all CVEs, including patched or non-exploitable vulnerabilities, creating noise. Trace-AI filters CVEs using exploit maturity levels (e.g., "weaponized" vs. "theoretical") from sources like CISA’s Known Exploited Vulnerabilities catalog. It also detects risks from malicious package updates by analyzing maintainer account activity patterns.
3. Which programming languages and package managers do you support? The platform supports npm/yarn (JavaScript), pip (Python), Maven/Gradle (Java), Go modules, RubyGems, NuGet (.NET), Cargo (Rust), and CPAN (Perl). It parses lock files like yarn.lock, Gemfile.lock, and go.mod to build dependency graphs. New ecosystems are added through ZSBOM’s open-source parser framework.