You do not need to trust the client's system. The developer or CISO will provide you with one or more anchor strings from their AI system. Take any one and paste it into the public verification endpoint below. No account required. No software to install.
Paste the full anchor string. The verifier will decompose it into its constituent fields and confirm the cryptographic fingerprint matches the original witness event. If it resolves, the evidence is intact and has not been tampered with since minting.
An SWT3 Witness Anchor is a cryptographic proof that an AI system was evaluated against a specific compliance procedure at a specific time and received a deterministic verdict. Here is how to read one:
The fingerprint is computed from: tenant identity + procedure ID + three evaluation factors + timestamp. It cannot be forged without access to the original inputs at the original time.
A failing anchor looks the same: SWT3-E-VULTR-AI-GRD1-FAIL-1773316622-a1b2c3d4e5f6. The verdict field changes; everything else is identical. Both PASS and FAIL anchors are independently verifiable. Failures in the ledger demonstrate the system is honest, not optimistic.
Each SWT3 procedure maps to the controls you are assessing. Find your framework below and note which procedures satisfy which requirements.
| Your Requirement | SWT3 Procedure | What It Proves |
|---|---|---|
| EU AI ACT (High-Risk) | ||
| Art. 9(2)(a) -- Risk management system | AI-GRD.1 | Safety guardrails were active at inference time |
| Art. 10(2)(a) -- Data governance | AI-DATA.1 | Training data provenance is documented |
| Art. 10(2)(f) -- Bias examination | AI-FAIR.1 | Disparity ratio measured across protected groups |
| Art. 12(1) -- Automatic logging | AI-INF.1 | Every inference was cryptographically fingerprinted |
| Art. 13(1) -- Transparency | AI-EXPL.1 | Explanation generated with AI output |
| Art. 14(1) -- Human oversight | AI-HITL.1 | Human reviewed and approved high-risk decision |
| NIST 800-53 / AI RMF | ||
| SI-4 -- System monitoring | AI-INF.1 | Continuous inference monitoring with provenance |
| MEASURE 2.5 -- AI system trustworthiness | AI-INF.1, AI-FAIR.1 | Trustworthiness metrics captured per inference |
| MANAGE 4.1 -- Risk treatment | AI-GRD.1 | Safety controls confirmed active |
| GOVERN 1.1 -- Accountability structures | AI-HITL.1 | Human oversight exercised and recorded |
| CMMC LEVEL 2 | ||
| CA.L2-3.12.1 -- Security assessments | AI-INF.1, AI-GRD.1 | AI controls assessed continuously, not annually |
| SI.L2-3.14.6 -- Monitor communications | AI-INF.1 | AI inference traffic monitored and recorded |
| SR 11-7 (Model Risk Management) | ||
| Effective challenge -- independent review | AI-FAIR.1, AI-EXPL.1 | Model outputs independently evaluated for bias and explainability |
| Ongoing monitoring | AI-INF.1, AI-DRIFT.2 | Continuous monitoring with drift detection thresholds |
Full crosswalk (108 procedures across 31 frameworks): Assessor Evidence Matrix
If you need to examine evidence beyond individual anchors, the organization's audit portal provides:
Request a demo portal walkthrough: sovereign.tenova.io/axm_audit_demo
Get early access to assessment tooling, framework updates, and the assessor directory.
Request AccessBefore the meeting, identify which anchors matter for your specific assessment. Pull evidence for these procedures based on what you are being assessed against. If you see procedures you haven't implemented yet, check the SDK quickstart for integration steps -- better to address gaps before the meeting than explain them during.
| If Your Assessment Is... | Focus On These Anchors |
|---|---|
| EU AI Act (Notified Body) | AI-INF.1, AI-GRD.1, AI-FAIR.1, AI-DATA.1, AI-EXPL.1, AI-HITL.1 |
| NIST 800-53 / FedRAMP (3PAO/C3PAO) | AI-INF.1, AI-GRD.1, AI-HITL.1, AI-SEC.1, AI-ACC.1 |
| CMMC Level 2 (C3PAO) | AI-INF.1, AI-GRD.1, AI-ACC.1 |
| SR 11-7 (OCC/Fed Examiner) | AI-INF.1, AI-FAIR.1, AI-EXPL.1, AI-DRIFT.2, AI-MDL.5 |
| NIST AI RMF (Internal/External) | AI-INF.1, AI-GRD.1, AI-FAIR.1, AI-HITL.1, AI-DATA.1 |
| ISO 42001 (Certification Body) | AI-INF.1, AI-GRD.1, AI-FAIR.1, AI-EXPL.1, AI-DATA.1, AI-HITL.1 |
GET /api/v1/passport/export. This is the one-page posture summary the assessor can keep.package.json or pyproject.toml.When the assessor opens the auditor portal, they will see:
| Question | Where to Find the Answer |
|---|---|
| "How do I know this wasn't generated after the fact?" | The epoch timestamp in the anchor is locked at mint time. The fingerprint includes the timestamp. Changing it would break verification. |
| "Can you show me a failure?" | Filter the ledger by verdict=FAIL. Showing failures proves the system is honest, not just passing everything. |
| "What happens when a model drifts?" | AI-DRIFT.2 anchors show threshold breaches. Lifecycle chains show your response sequence (detect, investigate, remediate, re-assess). |
| "Is this independent from your dev team?" | The SDK witnesses at the point of execution. The fingerprint is computed client-side. The assessor verifies on public infrastructure. No human in the loop. |
| "How does this map to [specific control]?" | Open the Evidence Matrix (printable reference) or use the crosswalk search in your dashboard to look up any control in real time. |
| "What if you stop using this tool?" | Anchors are immutable once minted. Historical evidence remains verifiable indefinitely. The protocol is open; the anchors are not vendor-locked. |
At the end of the meeting, give the assessor this document (both sides). It gives them: