WP-01 (Art. 9) -- accuracy measures are risk mitigation measures
WP-09 (Art. 55 Systemic)
WP-08 (Art. 53) -- systemic risk obligations layer on top of general GPAI obligations
WP-10 (Art. 16 Provider)
WP-11 (Art. 17) -- provider must establish QMS
WP-13 (Art. 27 FRIA)
WP-06 (Art. 14) -- FRIA outcomes inform human oversight requirements
Critical Assessor Notice: Boundaries of Cryptographic Evidence
SWT3 witness anchors prove that specific operational controls were active at a specific point in time. They do not replace the assessor's independent judgment, professional expertise, or regulatory authority. Conformity assessment determination remains the exclusive competence of the Notified Body under Article 43. Assessment of supplementary evidence (risk narratives, policy documents, organizational governance) remains the assessor's responsibility.
Enforcement Timeline
Enforceable Now GPAI obligations (Art. 53, 55) -- since 2 August 2025. 2 December 2027 High-risk AI (Annex III, standalone). 2 August 2028 High-risk AI (Annex I, safety components).
Version 1.1 | August 2026 | Tenable Nova LLC | sovereign.tenova.io
Instructions
This workpaper provides structured test procedures for verifying SWT3 witness anchor evidence during a conformity assessment under Regulation (EU) 2024/1689 (the EU Artificial Intelligence Act). Each entry maps an article obligation to the SWT3 procedures that produce evidence, defines what to test, what to expect, and how to determine pass/fail.
How to verify an anchor: Paste the anchor string into the public verifier at sovereign.tenova.io/verify (no account required), or recompute the fingerprint yourself using SHA-256. See Verify an Anchor in 5 Seconds for the full procedure.
Coverage badges:Full = SWT3 procedures directly satisfy the article requirement with cryptographic evidence. Partial = procedures cover the operational/runtime aspect of the requirement; supplementary evidence (policy documents, risk narratives, organizational governance) must be verified separately by the assessor.
Factor conventions: Factor legends in this workpaper (e.g., "factor_a = guardrails required") reflect the SWT3 SDK default conventions. Clients may customize factor usage. Before relying on factor interpretations, confirm with the provider that their implementation follows these defaults, or request their factor mapping document.
Methodology boundary: This workpaper documents evidence format and suggests evaluation criteria. It does not prescribe assessment methodology. Conformity determinations remain the assessor's professional responsibility under their applicable accreditation requirements and the Notified Body's designation scope.
Not applicable entries: If a workpaper entry is not applicable to the assessment scope (e.g., the system is not classified as high-risk, so WP-01 through WP-07 do not apply), mark the result as N/A with justification in the Notes field.
Supplementary evidence: Each workpaper entry includes a blue "Supplementary Evidence Required" box listing what SWT3 does NOT cover for that article. These items require separate assessment through document review, interview, or inspection.
Estimated duration: Allow 2-3 hours for a high-risk track review (WP-01 through WP-07) including verification of 5-10 sample anchors. GPAI evaluation (WP-08, WP-09) adds 1 hour. Provider/deployer obligations (WP-10 through WP-14) add 1-2 hours. For initial conformity assessments, plan an additional 1-2 hours for provider orientation and factor mapping confirmation.
Anchor anatomy: Every SWT3 Witness Anchor follows a deterministic format. Here is an annotated example for an EU AI Act-relevant procedure:
SWT3-E-AWS-AI-AIGRD1-PASS-1774800000-a0aa7669ae6f
Segment
Meaning
SWT3-E
Enclave deployment tier
AWS
Infrastructure provider
AI
AI procedures namespace
AIGRD1
AI-GRD.1 procedure (Art. 9 guardrails)
PASS
Acceptance criteria met
1774800000
Unix epoch timestamp
a0aa7669ae6f
First 12 chars of SHA-256 fingerprint
Articles 9-15: High-Risk AI System Requirements
These requirements apply to high-risk AI systems classified under Article 6 (Annex III standalone or Annex I safety components). Enforcement: 2 December 2027 (Annex III) / 2 August 2028 (Annex I).
WP-01Article 9 -- Risk Management SystemPartial
Requirement
Providers shall establish, implement, document, and maintain a risk management system throughout the AI system's lifecycle (Art. 9(1)). The system shall identify, analyse, and mitigate known and reasonably foreseeable risks (Art. 9(2)).
AI-GRD.1:factor_a = guardrails required | factor_b = guardrails active | factor_c = 0 (SDK defaults -- confirm with provider)
Test Procedure
Verify continuous guardrail enforcement via AI-GRD.1 anchors (not point-in-time).
Confirm model identity (AI-MDL.1) matches the approved model throughout the assessment period.
Check factor consistency with the provider's documented risk measures.
Verify no coverage gaps in the anchor stream.
Expected Evidence
Continuous stream of AI-GRD.1 anchors with consistent factor values matching the provider's risk management documentation. AI-MDL.1 anchors confirming model identity throughout the assessment period. No temporal gaps in the anchor stream.
Supplementary
Supplementary Evidence Required
Risk management system documentation (Art. 9(1)). Risk identification and analysis records (Art. 9(2)(a)). Residual risk communication to deployers (Art. 9(6)). Testing and validation plans for risk mitigation measures.
Sample Verification
sovereign.tenova.io/verify -- paste any AI-GRD.1 anchor token
Pass/Fail Criteria
PASS if: Risk mitigation measures are continuously witnessed with consistent factors. Guardrail enforcement is documented at runtime, and model identity is verified. FAIL if: Guardrails are undocumented at runtime or model identity is unverified during the assessment period.
Red Flag
Watch for: AI-GRD.1 factor_b (guardrails active) is always equal to or greater than factor_a (guardrails required), but the guardrail types (factor_a values) never change. Static guardrail config suggests no adaptation to evolving risks per Art. 9(2)(a).
Notes / Findings
WP-02Article 10 -- Data and Data GovernancePartial
Requirement
Training, validation, and testing data sets shall be subject to appropriate data governance and management practices (Art. 10(2)). Data sets shall be relevant, sufficiently representative, and free of errors to the best extent possible (Art. 10(3)).
SWT3 Procedures
AI-DATA.1 -- Training Data Provenance | AI-DATA.2 -- Data Quality Assessment | AI-RAG.1 -- Retrieval Provenance
Verify data provenance is documented for training, validation, and testing sets via AI-DATA.1 anchors.
Check data quality assessments exist via AI-DATA.2 anchors.
For RAG-based systems, verify retrieval provenance via AI-RAG.1 anchors.
Confirm source counts and verification status are consistent with the provider's documentation.
Expected Evidence
AI-DATA.1 anchors documenting each training data source with provenance verification. AI-DATA.2 anchors with quality assessment metrics. For RAG systems, AI-RAG.1 anchors tracking retrieval sources per inference.
Supplementary
Supplementary Evidence Required
Data governance practices documentation (Art. 10(2)). Representativeness analysis (Art. 10(3)). Bias examination methodology. Personal data processing justification. Data collection methodology and annotation guidelines.
Sample Verification
sovereign.tenova.io/verify -- paste any AI-DATA.1 anchor token
Pass/Fail Criteria
PASS if: Data sources are documented with quality metrics and provenance verified. FAIL if: Training data is undocumented or quality unassessed for deployed AI systems.
Red Flag
Watch for: AI-DATA.1 source count (factor_a) is 1 for a system claiming diverse training data. A single attested source may indicate incomplete provenance documentation.
Notes / Findings
WP-03Article 11 -- Technical DocumentationPartial
Requirement
Technical documentation shall be drawn up before the AI system is placed on the market or put into service. It shall demonstrate compliance with the requirements set out in Section 2 and be kept up to date (Art. 11(1)).
SWT3 Procedures
AI-ID.1 -- Agent Identity Attestation | AI-SBOM.1 -- AI Software Bill of Materials | AI-MDL.4 -- Model Card Attestation
Confirm SBOM is current via AI-SBOM.1 anchors -- component count and identification status.
Verify model cards cover all production models via AI-MDL.4 anchors.
Cross-reference anchor evidence with Annex IV requirements (15 sections).
Expected Evidence
AI-ID.1 anchors establishing system identity. AI-SBOM.1 anchors with current component counts and identification status. AI-MDL.4 anchors for each production model. Anchors should be refreshed periodically to demonstrate the documentation is "kept up to date."
Supplementary
Supplementary Evidence Required
Full Annex IV documentation (15 sections): general description, design specifications, development methodology, risk management documentation, changes log, standards applied. SWT3 auto-populates 10 of 15 sections; remaining 5 require manual provider input.
Sample Verification
sovereign.tenova.io/verify -- paste any AI-SBOM.1 anchor token
Pass/Fail Criteria
PASS if: System identified, SBOM current, and model cards available. Note: SWT3 provides runtime evidence that populates technical documentation; the assessor must verify the documentation itself is complete per Annex IV. FAIL if: Technical documentation incomplete against Annex IV checklist.
Red Flag
Watch for: AI-SBOM.1 minted once at deployment and never refreshed. Annex IV requires documentation to be 'kept up to date' (Art. 11(1)). A stale SBOM does not reflect current dependencies.
Notes / Findings
WP-04Article 12 -- Record-KeepingFull
Requirement
High-risk AI systems shall technically allow for the automatic recording of events (logs) over the lifetime of the system. Logging capabilities shall ensure a level of traceability of the AI system's functioning throughout its lifecycle (Art. 12(1)).
AI-INF.1:factor_a = model responded (1=yes) | factor_b = guardrails active (1=yes) | factor_c = anomaly detected (0=no) AI-LOG.1:factor_a = pipeline identifier | factor_b = events processed | factor_c = drop rate (0=no drops) (SDK defaults -- confirm with provider)
Test Procedure
Verify automatic inference logging with traceability via AI-INF.1 anchors.
Confirm logs are tamper-evident (SHA-256 fingerprints). Independently recompute a random sample.
Check logging pipeline operational via AI-LOG.1 with zero or near-zero drop rate.
Verify retention meets regulatory requirements (Art. 12(2): at least 6 months).
Expected Evidence
Continuous AI-INF.1 anchor stream covering the system's operational hours with no temporal gaps. AI-LOG.1 anchors confirming pipeline health with near-zero drop rates. Tamper-evident fingerprints that independently recompute to the same SHA-256 hash.
Supplementary
Supplementary Evidence Required
Log retention policy documentation, procedures for log access by market surveillance authorities per Art. 12(2).
Sample Verification
sovereign.tenova.io/verify -- paste any AI-INF.1 anchor token
Pass/Fail Criteria
PASS if: Every inference is cryptographically logged with tamper-evident fingerprints and retention meets requirements. Art. 12 requires logs that "enable tracing back." SWT3 anchors are SHA-256 signed and tamper-evident -- if a record is altered, the fingerprint breaks. FAIL if: Logs are editable, incomplete, or retention is insufficient.
Red Flag
Watch for: AI-INF.1 anchors show large temporal gaps (e.g., 8 hours overnight) in a 24/7 system. Missing inference records violate the 'automatic recording' requirement of Art. 12(1).
Notes / Findings
WP-05Article 13 -- TransparencyPartial
Requirement
High-risk AI systems shall be designed and developed in such a way as to ensure that their operation is sufficiently transparent to enable deployers to interpret the system's output and use it appropriately (Art. 13(1)).
Verify explanations are generated with outputs via AI-EXPL.1 anchors.
Confirm content marking is active via AI-MARK.1 anchors.
Check transparency report published via AI-TRANS.1 anchors.
Expected Evidence
AI-EXPL.1 anchors for each high-risk decision with explanation method and confidence score. AI-MARK.1 anchors for AI-generated content. AI-TRANS.1 anchors confirming transparency notice publication.
Supplementary
Supplementary Evidence Required
Instructions for use (Art. 13(3)(b)). System capabilities and limitations documentation. Intended purpose statement. User-facing transparency notices and their placement.
Sample Verification
sovereign.tenova.io/verify -- paste any AI-EXPL.1 anchor token
Pass/Fail Criteria
PASS if: Explanations generated, content marked, and transparency documented. FAIL if: No explainability for high-risk decisions.
Red Flag
Watch for: AI-EXPL.1 explanation method (factor_a) is identical for all system outputs regardless of decision complexity. A single explanation method may not provide 'sufficient transparency' for varied use cases per Art. 13(1).
Notes / Findings
WP-06Article 14 -- Human OversightPartial
Requirement
High-risk AI systems shall be designed and developed in such a way as to be effectively overseen by natural persons during the period in which they are in use. Human oversight measures shall aim to prevent or minimise risks (Art. 14(1)).
Verify review duration is non-trivial (not instant rubber-stamp).
Check override capability exercised or tested via AI-HITL.2 anchors.
Expected Evidence
AI-HITL.1 anchors for each high-risk decision showing reviewer identity hash, decision outcome, and review duration. AI-HITL.2 anchors demonstrating override capability. AI-HITL.3 anchors binding reviewer identities. SWT3 proves human review occurred and overrides were exercised. The assessor evaluates whether the system's design enables effective oversight (an architectural property not capturable by runtime evidence).
Supplementary
Supplementary Evidence Required
System design documentation showing human oversight was designed-in. Reviewer competence and training records. Override procedure documentation.
Sample Verification
sovereign.tenova.io/verify -- paste any AI-HITL.1 anchor token
Pass/Fail Criteria
PASS if: Human oversight documented with identified reviewers and meaningful review durations. FAIL if: No oversight for high-risk decisions or durations suggest rubber-stamp (<1 second).
Red Flag
Watch for: AI-HITL.1 review durations (factor_c) average under 2 seconds for decisions affecting individuals. Review times this short suggest rubber-stamping rather than effective oversight per Art. 14(1).
Notes / Findings
WP-07Article 15 -- Accuracy, Robustness, and CybersecurityFull
Requirement
High-risk AI systems shall achieve an appropriate level of accuracy, robustness, and cybersecurity, and perform consistently throughout their lifecycle (Art. 15(1)). Appropriate measures shall be taken against adversarial attacks (Art. 15(4)).
AI-DRIFT.1:factor_a = drift metric | factor_b = observed magnitude | factor_c = threshold AI-PERF.1:factor_a = metric name | factor_b = measured value | factor_c = target value (SDK defaults -- confirm with provider)
Test Procedure
Verify model versions tracked via AI-MDL.2 anchors.
Check drift detection is regular via AI-DRIFT.1 -- compare observed magnitude (factor_b) against threshold (factor_c).
Confirm performance benchmarks documented via AI-PERF.1 anchors.
Verify robustness and cybersecurity testing conducted via AI-ROBUST.1 and AI-CYBER.1.
Expected Evidence
AI-MDL.2 anchors tracking model version changes. Regular AI-DRIFT.1 anchors with observed magnitude below threshold. AI-PERF.1 anchors with measured values meeting or exceeding targets. AI-ROBUST.1 and AI-CYBER.1 anchors documenting completed test cycles.
Supplementary
Supplementary Evidence Required
Declared accuracy levels in instructions for use (Art. 15(2)). Error rate communication methodology. Resilience test protocols.
Sample Verification
sovereign.tenova.io/verify -- paste any AI-DRIFT.1 anchor token
Pass/Fail Criteria
PASS if: Accuracy benchmarked, drift monitored, robustness tested, and AI-specific cybersecurity assessed. FAIL if: Any of the four pillars (accuracy, drift, robustness, cybersecurity) lacks evidence.
Red Flag
Watch for: AI-DRIFT.1 observed magnitude (factor_b) consistently exceeds the threshold (factor_c) but no corrective action evidence follows. Persistent drift without response indicates the monitoring system detects but does not trigger remediation.
Notes / Findings
Articles 53, 55: General-Purpose AI Model Obligations
These obligations apply to providers of general-purpose AI models (Art. 3(63)). Enforceable Now -- since 2 August 2025.
WP-08Article 53 -- General GPAI ObligationsPartial
Requirement
Providers of GPAI models shall: draw up and keep up to date technical documentation (Art. 53(1)(a)); make information available to downstream providers (Art. 53(1)(b)); establish a copyright compliance policy (Art. 53(1)(c)); publish a training data summary (Art. 53(1)(d)).
SWT3 Procedures
AI-MDL.5 -- Model Weight Attestation | AI-LIC.1 -- License Compliance | AI-SBOM.1 -- AI Software Bill of Materials
Factor Legend
AI-MDL.5:factor_a = model architecture | factor_b = weight file SHA-256 hash | factor_c = framework version (SDK defaults -- confirm with provider)
Test Procedure
Verify model weight integrity documented via AI-MDL.5 anchors.
Check license compliance for open-weight models via AI-LIC.1 anchors.
Verify SBOM covers all dependencies via AI-SBOM.1 anchors.
Expected Evidence
AI-MDL.5 anchors with model architecture identifier and SHA-256 weight hash. AI-LIC.1 anchors confirming license terms documented. AI-SBOM.1 anchors with current dependency counts.
Supplementary
Supplementary Evidence Required
Technical documentation per Art. 53(1)(a). Copyright policy per Art. 53(1)(c). EU AI Office summary per Art. 53(1)(d). Downstream provider integration documentation per Art. 53(1)(b).
Sample Verification
sovereign.tenova.io/verify -- paste any AI-MDL.5 anchor token
Pass/Fail Criteria
PASS if: Model weights attested, licenses documented, and SBOM current. FAIL if: Weight integrity unverified or license terms undocumented.
Red Flag
Watch for: AI-MDL.5 weight hash exists but AI-LIC.1 is absent. Model weights are verified but license compliance is undocumented -- a gap in Art. 53(1)(c) copyright policy obligations.
Notes / Findings
WP-09Article 55 -- Systemic Risk ObligationsFull
Requirement
Providers of GPAI models with systemic risk shall: perform model evaluation including adversarial testing (Art. 55(1)(b)); assess and mitigate systemic risks (Art. 55(1)(a)); report serious incidents to the AI Office (Art. 55(1)(c)); ensure adequate cybersecurity (Art. 55(1)(d)). Enforceable Now
SWT3 Procedures
AI-REDTEAM.1 -- Red Team Assessment | AI-SEC.2 -- Vulnerability Assessment | AI-INCIDENT.1 -- Incident Detection
Factor Legend
AI-REDTEAM.1:factor_a = test scope | factor_b = findings count | factor_c = severity distribution (SDK defaults -- confirm with provider)
Test Procedure
Verify adversarial testing (red teaming) per Art. 55(1)(b) via AI-REDTEAM.1 anchors.
Check vulnerability assessment current via AI-SEC.2 anchors.
Verify incident reporting capability per Art. 55(1)(c) via AI-INCIDENT.1 anchors.
Expected Evidence
AI-REDTEAM.1 anchors with test scope and findings count. AI-SEC.2 anchors with current vulnerability assessments. AI-INCIDENT.1 anchors demonstrating incident detection and reporting capability.
Supplementary
Supplementary Evidence Required
Systemic risk assessment narratives. AI Office communications and incident reports per Art. 55(1)(c). Mitigation plans.
Sample Verification
sovereign.tenova.io/verify -- paste any AI-REDTEAM.1 anchor token
Pass/Fail Criteria
PASS if: Red teaming conducted, vulnerabilities assessed, and incident reporting operational. FAIL if: No adversarial testing for systemic risk models.
Red Flag
Watch for: AI-REDTEAM.1 findings count (factor_b) is 0. A red team exercise that finds zero vulnerabilities in a GPAI model with systemic risk is statistically implausible and should be examined.
Notes / Findings
Articles 16, 17, 26, 27, 50: Provider and Deployer Obligations
These articles establish organizational obligations for providers placing high-risk AI systems on the market and deployers putting them into use.
WP-10Article 16 -- Provider ObligationsPartial
Requirement
Providers of high-risk AI systems shall ensure compliance with Section 2 requirements, establish a quality management system (Art. 17), and register systems in the EU database (Art. 49).
AI-GOV.1:factor_a = policy version | factor_b = attested (1/0) | factor_c = 0 (SDK defaults -- confirm with provider)
Test Procedure
Verify quality management system references via AI-GOV.1 anchors.
Confirm system registration is current via AI-GOV.3 anchors.
Expected Evidence
AI-GOV.1 anchors with current policy version and attestation status. AI-GOV.3 anchors confirming EU database registration.
Supplementary
Supplementary Evidence Required
Full QMS documentation. Conformity assessment records. CE marking procedures. EU declaration of conformity. EU database registration confirmation.
Sample Verification
sovereign.tenova.io/verify -- paste any AI-GOV.1 anchor token
Pass/Fail Criteria
PASS if: Provider governance anchors demonstrate active QMS. FAIL if: No governance evidence.
Red Flag
Watch for: AI-GOV.1 policy attestation exists but AI-GOV.3 system registration is absent. The provider has governance but the system may not be registered in the EU database per Art. 49.
sovereign.tenova.io/verify -- paste any AI-AUDIT.2 anchor token
Pass/Fail Criteria
PASS if: Audit integrity verified with tamper-evident records. FAIL if: Audit trail integrity unverified.
Red Flag
Watch for: AI-AUDIT.2 external timestamps (factor_a) reference an unknown or unaccredited timestamp authority. RFC 3161 timestamps from unrecognized TSAs do not provide independent time proof.
Notes / Findings
WP-12Article 26 -- Deployer ObligationsFull
Requirement
Deployers shall use high-risk AI systems in accordance with the instructions for use (Art. 26(1)). Deployers shall assign human oversight and ensure persons exercising oversight are competent and have the authority to override (Art. 26(2)).
SWT3 Procedures
AI-GOV.4 -- Shadow AI Detection | AI-ACC.1 -- Agent Access Control
Verify deployers use AI per provider instructions -- check policy compliance gates via AI-GOV.4 anchors.
Check shadow AI detection is active -- unauthorized model usage should be flagged.
Verify access controls via AI-ACC.1 anchors.
Expected Evidence
AI-GOV.4 anchors showing policy compliance gate results including both authorized and denied access attempts. AI-ACC.1 anchors confirming access control enforcement.
Supplementary
Supplementary Evidence Required
Provider's instructions for use. Deployer's monitoring procedures. Human oversight assignment documentation.
Sample Verification
sovereign.tenova.io/verify -- paste any AI-GOV.4 anchor token
Pass/Fail Criteria
PASS if: Policy compliance gates active and access controlled. FAIL if: Unauthorized model usage undetected.
Red Flag
Watch for: AI-GOV.4 policy gate consistently returns authorized (factor_b = 1) with zero denials. A gate that never denies access may not be performing meaningful policy enforcement.
Notes / Findings
WP-13Article 27 -- Fundamental Rights Impact AssessmentPartial
Requirement
Deployers of high-risk AI systems referred to in Annex III shall perform a fundamental rights impact assessment (FRIA) before putting the system into use (Art. 27(1)).
Verify FRIA conducted before deployment via AI-IMPACT.1 anchors -- check timestamps predate system go-live.
Check DPIA covers personal data processing via AI-DPIA.1 anchors.
Verify fairness metrics assessed via AI-FAIR.1 anchors -- compare disparity ratio (factor_b) against threshold (factor_c).
Expected Evidence
AI-IMPACT.1 anchors with timestamps predating the first AI-INF.1 inference anchor. AI-DPIA.1 anchors covering personal data processing scope. AI-FAIR.1 anchors with disparity ratios within thresholds for each protected attribute.
Supplementary
Supplementary Evidence Required
Substantive FRIA content review -- scope of rights assessed, methodology applied, stakeholder consultation records, mitigation measures adopted. SWT3 proves the assessment was conducted; the assessor evaluates its adequacy.
Sample Verification
sovereign.tenova.io/verify -- paste any AI-IMPACT.1 anchor token
Pass/Fail Criteria
PASS if: Impact assessments predate deployment and cover fundamental rights dimensions. FAIL if: No impact assessment for systems affecting individuals.
Red Flag
Watch for: AI-IMPACT.1 and AI-DPIA.1 anchor timestamps postdate the first AI-INF.1 inference anchor. Impact assessments conducted after deployment violate Art. 27(1) 'before putting the high-risk AI system into use.'
Notes / Findings
WP-14Article 50 -- Transparency Obligations for UsersFull
Requirement
Providers shall ensure AI systems intended to interact with natural persons are designed to inform persons they are interacting with AI (Art. 50(1)). Providers of AI systems generating synthetic content shall ensure outputs are marked in a machine-readable format (Art. 50(2)).
AI-MARK.1:factor_a = marking type | factor_b = applied (1=yes) | factor_c = 0 (SDK defaults -- confirm with provider)
Test Procedure
Verify AI-generated content marked per Art. 50(2) via AI-MARK.1 anchors.
Check watermarking active for synthetic content via AI-WATERMARK.1 anchors.
Confirm marking is machine-readable as required by Art. 50(2).
Expected Evidence
AI-MARK.1 anchors for each AI-generated content item with marking type and application status. AI-WATERMARK.1 anchors for synthetic audio/video content confirming machine-readable watermark application.
Supplementary
Supplementary Evidence Required
User-facing AI disclosure notices and their placement. Technical documentation of watermarking detection method.
Sample Verification
sovereign.tenova.io/verify -- paste any AI-MARK.1 anchor token
Pass/Fail Criteria
PASS if: Content marking and watermarking active for applicable outputs. FAIL if: AI-generated content unmarked.
Red Flag
Watch for: AI-MARK.1 content marking anchors exist but AI-WATERMARK.1 watermark anchors are absent for a system generating synthetic audio/video. Art. 50(2) requires machine-readable marking for synthetic content.
Notes / Findings
Supplementary Evidence Checklist
SWT3 provides cryptographic evidence of operational facts. The following table identifies what SWT3 does NOT cover for each article -- items the assessor must verify through document review, interview, or inspection separately from anchor evidence.
Article
SWT3 Coverage
What SWT3 Proves
What Requires Separate Assessment
Art. 9
Partial
Guardrails active at runtime, model identity verified, access controls enforced
Risk management system documentation, risk identification records, residual risk communication to deployers, testing plans
Art. 10
Partial
Data sources documented, quality metrics witnessed, retrieval provenance recorded
Data governance policies, representativeness analysis, bias examination methodology, annotation guidelines
Art. 11
Partial
System identity, SBOM, model cards attested at runtime
Annex IV narrative documentation (5 of 15 sections), design specifications, development methodology
Art. 12
Full
Automatic event recording, tamper-evident logs, traceability via SHA-256 fingerprints
Retention policy documentation (minimum 6 months per Art. 12(2))
FRIA substantive content: scope of rights assessed, methodology, stakeholder consultation, mitigation measures adopted
Art. 50
Full
Content marked, watermarking active
None for marking/watermarking operational evidence
How to Cite Verified Evidence
When referencing SWT3 anchor evidence in your conformity assessment report or audit findings, use a consistent citation format. The following templates cover individual anchor verification and workpaper-level summaries.
Per-anchor citation
"Per WP-[##], the assessor independently verified SWT3 Witness Anchor [full token] using SHA-256 fingerprint recomputation (FIPS 180-4). The anchor confirms that procedure [ID] was evaluated on [date] with verdict [PASS/FAIL] in the context of Article [##] of Regulation (EU) 2024/1689. Factors: [descriptions with values]. Verification result: fingerprint match confirmed."
Workpaper summary citation
"The assessor reviewed [N] SWT3 witness anchors across [M] procedures covering [date range]. [X] anchors were independently verified via fingerprint recomputation. Coverage gaps: [list or 'none identified']. Workpaper entries WP-01 through WP-14 document individual article findings. Conformity determination: [assessor's determination per Article 43]."
Common Assessment Questions
Questions that arise during EU AI Act conformity assessments, with guidance on how to proceed.
What if the system is classified as both high-risk AND uses a GPAI model?
Both tracks apply. The GPAI model provider must comply with Art. 53 (and Art. 55 if systemic risk), while the high-risk AI system provider or deployer must comply with Art. 9-15. These may be different entities. Use WP-01 through WP-07 for the high-risk system assessment and WP-08 through WP-09 for the GPAI model assessment. If the same entity is both GPAI provider and high-risk system provider, all workpaper entries are relevant.
What if harmonised standards are not yet available?
Until harmonised European standards (hENs) are formally designated in the Official Journal, there is no presumption of conformity. Use current SWT3 procedure mappings as interim evidence of operational compliance against the article requirements directly. Update the assessment approach when hENs are published and formally designated.
What about the Omnibus deferral dates?
The Digital Omnibus Regulation deferred enforcement of high-risk AI requirements. Annex III (standalone high-risk): 2 December 2027. Annex I (safety component high-risk): 2 August 2028. GPAI obligations (Art. 53, 55) are already enforceable since 2 August 2025 and are NOT deferred.
What if the provider and deployer are the same entity?
Both Art. 16 (provider obligations) and Art. 26 (deployer obligations) apply. All workpaper entries WP-10 through WP-14 are relevant. The entity must demonstrate compliance with both sets of obligations, though evidence may overlap.
What if the GPAI model is open-weight?
The Art. 53(2) exemption applies to certain obligations for open-weight GPAI models, but it does NOT exempt providers from Art. 53(1)(a) (technical documentation) or Art. 53(1)(c) (copyright policy). If the model is classified with systemic risk under Art. 51, the exemption does not apply at all -- all Art. 55 obligations remain in full force.
What if an anchor fails fingerprint verification?
A verification failure means the anchor has been modified since minting, or the verification inputs are incorrect. First, confirm you have the correct inputs (tenant ID, procedure ID, factors, millisecond timestamp). If inputs are correct and verification still fails, this is a significant finding -- document it and request the provider's explanation. A failed fingerprint may indicate evidence tampering.
Summary Sheet
Record the assessment result for each workpaper entry. Use this as the summary page for the conformity assessment file.