Bridging the Metrics-to-Evidence Gap

Why TRACE turns raw model scores into audit-ready proof

Imagine This

You’re showcasing your model’s performance—fairness scores, accuracy benchmarks, bias heatmaps. The dashboards look sharp. The numbers are solid.

Then a compliance lead leans in and asks:
“Can you provide verifiable proof that this model is safe and compliant?”

In that moment, your beautiful charts become… screenshots.
Your insights become… a slide deck.
Your confidence becomes… silence.

That breakdown is the Metrics-to-Evidence Gap—the disconnect between technical evaluation and regulatory-grade assurance.
And it’s exactly what modern AI regulations are closing in on:

• EU AI Act
• NIST AI RMF
• ISO/IEC 42001
  All now demand a clear, auditable chain from metric → threshold → action → accountability.

TRACE was built to close that gap—permanently.

Evidence Beats Intention

Model cards, policies, and ethical AI statements show where you intend to go.
But in a regulated world, good intentions aren’t enough—auditors want deterministic proof.

They ask:

• What threshold turns “acceptable” into “unacceptable”?
• Who is accountable for the fix, and how quickly must it happen?
• Which regulation does this directly satisfy—EU AI Act Article 10? ISO 42001 Clause 5.3? GDPR Article 25?

If you can’t answer with documented, traceable links from metric → rule → control → clause…
then even the best evaluation suite won’t stand up to scrutiny.

TRACE: Turning AI Evaluation into Audit-Ready Assurance

TRACE isn’t just a framework—it’s a governance backbone.
It stands for Trust, Risk, Action, Compliance, and Evidence—the five pillars every AI system must satisfy to move from performance to provability.

• T — Trust
  Key Question: Is the metric reliable?
  TRACE ensures every input is authentic and traceable. It logs the canonical ID, source hash, and version history of each evaluation metric—so your stakeholders can trust what’s being measured, and how.
• R — Risk
  Key Question: How serious is the exposure right now?
  TRACE doesn’t just collect scores—it interprets them. It classifies metrics into Low / Elevated / Unacceptable buckets, tied to your business impact thresholds and model context.
• A — Action
  Key Question: What must we do about it?
  When a risk crosses the line, TRACE triggers a pre-approved control playbook—launching retraining, escalation, or intervention with a real-time SLA timer attached.
• C — Compliance
  Key Question: Which rule does it satisfy?
  Every action is mapped to a live clause ledger—linking directly to relevant regulations such as the EU AI Act, NIST AI RMF, ISO 42001, or GDPR Article 25. No ambiguity. No manual cross-referencing.
• E — Evidence
  Key Question: Can an auditor verify it?
  

TRACE packages the full assurance chain—metrics, rules, actions, owners, timestamps—into a cryptographically sealed Assurance Envelope that’s immutable, replayable, and audit-ready.

Example: A 7% fairness gap is flagged as “Unacceptable.”

TRACE instantly triggers retraining, assigns ownership, maps it to EU AI Act Article 10, and seals the record into an Assurance Envelope—ready for audit or procurement.

Seamlessly Integrates Into Your Stack

TRACE is designed for flexibility—whether you're building in a fast-moving startup or a regulated enterprise. It plugs into your workflow with minimal friction and maximum transparency.

• Interfaces: Use our CLI for local workflows or REST API for real-time integration with your platforms.
• Configuration: Define thresholds and controls using human-readable YAML or JSON—easy to version, review, and automate.
• Environments: Run TRACE in your existing CI/CD pipelines, Jupyter notebooks, or agent-driven workflows.
• Model Compatibility: Works with predictive, generative, and multi-agent systems—regardless of framework or provider.
• Transparency by Design: Every rule is explicitly defined, fully auditable, and version-controlled. No black boxes. No vendor lock-in.

Whether you're running nightly validations or building real-time model oversight, TRACE fits where your team works—without forcing a new toolchain or rewriting your governance logic.

What Teams Gain with TRACE

TRACE doesn’t just help you monitor—it helps you prove, respond, and scale AI responsibly across teams and workflows.

Built for Regulators. Loved by Engineers.

TRACE bridges the gap between technical metrics and legal requirements—without adding overhead.

It comes preloaded with out-of-the-box mappings to the world’s leading AI governance frameworks:

• EU AI Act – Full support for risk classification, Article 10 (data governance), and Article 15 (transparency & record-keeping)
• ISO 42001 – Aligns with AI-specific management system requirements across governance, lifecycle, and monitoring
• NIST AI RMF – Seamlessly integrates into MEASURE → GOVERN workflows for risk-aware AI development
• GDPR Article 25 – Supports “privacy by design” obligations through traceable mitigation and evidence controls

From code commit to production, TRACE embeds compliance directly into your ML lifecycle—so your assurance story isn’t just told, it’s proven.

Ready to Close the Metrics-to-Evidence Gap?

Whether you're preparing for regulatory audits, accelerating enterprise deals, or simply building AI systems you can stand behind—TRACE makes verifiable assurance part of your daily workflow.

✅ See it in action: View a live TRACE-RAI Scorecard to watch raw evaluation metrics transform into defensible, audit-ready evidence.
✅ Automate go/no-go decisions: Plug the TRACE Metrics API into your CI/CD pipeline to assess models against customizable risk thresholds—ensuring only compliant, low-risk versions are promoted to production.
✅ Start instantly: No integration needed—just upload a spreadsheet and generate a full compliance and risk view in minutes.

Because in Responsible AI, it’s not just the metric that matters—it’s the proof.

With TRACE, that proof isn’t just possible—it’s built in.