Go From CVE to Contract in 60 Seconds

Most security teams today are drowning in CVEs. Dashboards light up red, vulnerability counts spike into the thousands, and yet nobody can answer the only question that matters: which of these issues truly block our revenue, and which ones can be safely accepted with evidence?

Trace-AI was built to solve exactly that. It takes you from a raw CVE to a contract-level business decision in under 60 seconds.

Why Traditional CVE Scanners Fail at Supply Chain Security

The promise of software supply chain security has been undermined by the limits of CVE scanners. They report the symptoms but fail to expose the underlying risks that slow sales, drain engineering time, and leave compliance gaps.

Consider four moments when CVE management fell short even in security-first enterprises:

• XZ Utils (2024): a backdoor slipped into a critical Linux compression library. At discovery, there was no CVE. Only maintainer churn and anomalous commit metadata could have revealed it earlier.

• SolarWinds Orion (2020): trojanised updates shipped with valid signatures. CVE scanners were blind; the weak signals were in build provenance and release metadata.

• Log4j (2021): dashboards flooded red. Yet most teams could not separate production-critical risks from benign dev/test installs. That context only exists in dependency metadata.

• PyPI/NPM Typosquatting: malicious lookalikes rarely carry CVEs, but infect thousands of developers. The only defence lies in namespace similarity and publication metadata.

The lesson: raw CVE counts create noise. Without vulnerability prioritisation rooted in metadata, enterprises remain exposed — and startups lose deals to slow, noisy security reviews.

Research Foundations

Trace-AI is built on years of university research into software supply chain security, drawing on work from the University of London (Royal Holloway), the University of Adelaide, and the University of California. Studies on dependency graphs, package metadata, and attack vectors like typosquatting and dependency confusion directly informed the model.

We developed and refined Trace-AI through active pilots and laboratory observations in startups across the UK, Czechia, the US and India, ensuring the metadata-first approach is both research-driven and field-tested.

How Trace-AI Delivers Vulnerability Prioritisation in 60 Seconds

At the core is a metadata-first model — the ZSBOM. It treats package metadata as the primary signal, not exhaust.

Detect

Pull vulnerability truth from OSV and dependency graphs from deps.dev. Know precisely which versions are affected.

Contextualise

Check where the package sits in your graph, whether it touches production, and assess maintainer activity, release cadence, and ownership changes.

Map

Link risks to ISO 27001, SOC 2, Cyber Resilience Act compliance, and the actual clauses inside your contracts and DPAs.

Act

Fix in one click with Remed-AI or generate a policy-backed exception with audit trail. Either way, you go from CVE to contract-level posture in a single motion.

Who Is This For?

• SaaS founders looking for an alternative to CVE scanners that slow down sales.

• Engineering teams needing a modern dependency scanning tool that runs fast, respects privacy, and cuts noise.

• Security leaders adopting Application Security Posture Management (ASPM) but without enterprise bloat.

• GRC and compliance managers who must automate vendor security assessments and evidence collection.

Why This Matters

• Engineers: Less noise, fewer context switches, faster fixes.

• Founders: Deals keep moving because every finding maps to a contract clause.

• CISOs & Investors: Security posture expressed in business terms, not just counts.

Research-Driven, Market-Ready

Frameworks like SLSA and regulations such as the Cyber Resilience Act demand provenance, secure development, and responsible disclosure. ENISA continues to flag software supply chain security as one of the top risks to European enterprises.

Trace-AI’s metadata-first and black-box stance was designed for this reality. It enables small teams to reduce CVE noise, achieve Cyber Resilience Act compliance, and satisfy customers faster — without handing over their source code.

Commonly Asked Questions: Answered

Q1: What is metadata-based security scoring?

 It is a model that prioritises vulnerabilities using package metadata — ownership changes, release cadence, exploitability — rather than CVE counts alone.

Q2: How does Trace-AI help with Cyber Resilience Act compliance? 

Trace-AI links each finding to compliance controls and generates the audit trail required under CRA, helping SaaS teams demonstrate continuous due diligence.

Q3: Can Trace-AI replace my existing CVE scanner? It complements scanners. Trace-AI turns raw CVE alerts into business-level risk signals and actionable evidence.

Be There on Launch

We built Trace-AI so you never lose a deal — or a night of sleep — to CVE noise again. If you want fewer alerts, faster fixes, and cleaner audit trails, support our Product Hunt launch and see how quickly you can go from CVE to contract.