SWT3 Protocol

EU AI Act
Assessor Workpaper

Regulation (EU) 2024/1689 -- Cryptographic Evidence Verification

Engagement Details

AI System Provider
Assessment Date(s)
Lead Assessor
Notified Body / CAB
System Classification
(High-Risk Annex III / Safety Component Annex I / GPAI / GPAI Systemic Risk)

Workpaper Usage by Assessment Track

TrackFocusWorkpaper Entries
High-Risk RequirementsArt. 9-15WP-01 through WP-07
GPAI ObligationsArt. 53, 55WP-08 through WP-09
Provider/DeployerArt. 16, 17, 26, 27, 50WP-10 through WP-14

Entry Cross-References

EntryDepends On / See Also
WP-01 (Art. 9 Risk Mgmt)WP-07 (Art. 15) -- risk measures must produce measurable accuracy/robustness outcomes
WP-03 (Art. 11 Tech Doc)WP-04 (Art. 12) -- technical documentation must describe logging capabilities
WP-04 (Art. 12 Logging)WP-07 (Art. 15) -- logged metrics support accuracy claims
WP-06 (Art. 14 Oversight)WP-05 (Art. 13) -- transparency enables effective oversight
WP-07 (Art. 15 Accuracy)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.

Sampling guidance: For sample size, red flags, and CSV query instructions, see Assessor Sampling Instructions.

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
SegmentMeaning
SWT3-EEnclave deployment tier
AWSInfrastructure provider
AIAI procedures namespace
AIGRD1AI-GRD.1 procedure (Art. 9 guardrails)
PASSAcceptance criteria met
1774800000Unix epoch timestamp
a0aa7669ae6fFirst 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).

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)).
SWT3 Procedures
AI-GRD.1 -- Guardrail Enforcement | AI-GRD.2 -- Content Safety Classification | AI-MDL.1 -- Model Identity Verification | AI-GOV.1 -- Acceptable Use Policy | AI-SEC.1 -- Adversarial Threat Detection | AI-ACC.1 -- Agent Access Control | AI-FAIR.2 -- Fairness Threshold Calibration
Factor Legend
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
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
Factor Legend
AI-DATA.1: factor_a = source count | factor_b = verified (1/0) | factor_c = 0 (SDK defaults -- confirm with provider)
Test Procedure
  • 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
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
Factor Legend
AI-SBOM.1: factor_a = component count | factor_b = identified (1/0) | factor_c = 0 (SDK defaults -- confirm with provider)
Test Procedure
  • Verify system identity via AI-ID.1 anchors.
  • 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
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)).
SWT3 Procedures
AI-INF.1 -- Inference Provenance | AI-INF.2 -- Inference Latency Monitoring | AI-AUDIT.1 -- Audit Log Integrity | AI-LOG.1 -- Logging Pipeline
Factor Legend
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
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)).
SWT3 Procedures
AI-EXPL.1 -- Explainability Attestation | AI-MARK.1 -- Content Marking | AI-TRANS.1 -- Transparency Notice
Factor Legend
AI-EXPL.1: factor_a = explanation method | factor_b = confidence score | factor_c = factors cited (SDK defaults -- confirm with provider)
Test Procedure
  • 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
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)).
SWT3 Procedures
AI-HITL.1 -- Human Review Attestation | AI-HITL.2 -- Override Capability | AI-HITL.3 -- Reviewer Identity Binding
Factor Legend
AI-HITL.1: factor_a = reviewer identity hash | factor_b = decision (1=approved, 0=rejected) | factor_c = review duration (seconds) (SDK defaults -- confirm with provider)
Test Procedure
  • Verify human review occurs for high-risk decisions via AI-HITL.1 anchors.
  • Check reviewer identity is documented -- reviewer identity hash provides non-repudiation.
  • 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
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)).
SWT3 Procedures
AI-MDL.2 -- Version Tracking | AI-DRIFT.1 -- Drift Detection | AI-PERF.1 -- Performance Monitoring | AI-ROBUST.1 -- Robustness Testing | AI-CYBER.1 -- Cybersecurity Posture
Factor Legend
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.

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
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.

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).
SWT3 Procedures
AI-GOV.1 -- Acceptable Use Policy Attestation | AI-GOV.3 -- Registration Attestation
Factor Legend
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.
Notes / Findings
Requirement
Providers shall put in place a quality management system covering design, development, testing, and deployment procedures (Art. 17(1)).
SWT3 Procedures
AI-AUDIT.1 -- Audit Log Integrity | AI-AUDIT.2 -- External Timestamp
Factor Legend
AI-AUDIT.2: factor_a = timestamp authority | factor_b = verification status (1=confirmed) | factor_c = time delta (seconds) (SDK defaults -- confirm with provider)
Test Procedure
  • Verify audit trail integrity via AI-AUDIT.1 anchors.
  • Check external timestamps (RFC 3161) via AI-AUDIT.2 anchors.
  • Confirm time delta from trusted source is within acceptable range.
Expected Evidence
AI-AUDIT.1 anchors demonstrating tamper-evident audit trail. AI-AUDIT.2 anchors with recognized timestamp authority, confirmed verification status, and acceptable time delta.
Supplementary
Supplementary Evidence Required QMS documentation covering design, development, testing, deployment per Art. 17(1). Organizational procedures. Compliance management documentation. Post-market monitoring plan.
Sample Verification
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
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
Factor Legend
AI-GOV.4: factor_a = policy scope | factor_b = authorized (1=yes, 0=no) | factor_c = 0 (SDK defaults -- confirm with provider)
Test Procedure
  • 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
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)).
SWT3 Procedures
AI-IMPACT.1 -- Societal Impact Assessment | AI-DPIA.1 -- Data Protection Impact Assessment | AI-FAIR.1 -- Bias Detection
Factor Legend
AI-DPIA.1: factor_a = assessment scope | factor_b = risk rating (1=low, 2=medium, 3=high) | factor_c = supervisory authority
AI-FAIR.1: factor_a = protected attribute | factor_b = disparity ratio | factor_c = threshold (SDK defaults -- confirm with provider)
Test Procedure
  • 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
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)).
SWT3 Procedures
AI-MARK.1 -- Content Marking | AI-WATERMARK.1 -- Watermark Attestation
Factor Legend
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. 9PartialGuardrails active at runtime, model identity verified, access controls enforcedRisk management system documentation, risk identification records, residual risk communication to deployers, testing plans
Art. 10PartialData sources documented, quality metrics witnessed, retrieval provenance recordedData governance policies, representativeness analysis, bias examination methodology, annotation guidelines
Art. 11PartialSystem identity, SBOM, model cards attested at runtimeAnnex IV narrative documentation (5 of 15 sections), design specifications, development methodology
Art. 12FullAutomatic event recording, tamper-evident logs, traceability via SHA-256 fingerprintsRetention policy documentation (minimum 6 months per Art. 12(2))
Art. 13PartialExplanations generated, content marked, transparency notices attestedInstructions for use, capabilities/limitations documentation, user-facing notice placement
Art. 14PartialHuman review exercised, reviewer identity, override capability, review durationSystem design enabling effective oversight (architectural property). Reviewer competence and training records
Art. 15FullDrift monitored, performance benchmarked, robustness tested, cybersecurity assessedDeclared accuracy levels in instructions for use, error rate communication methodology
Art. 53PartialModel weight integrity, license compliance, SBOMTechnical documentation narrative, training data summary, copyright policy, downstream provider documentation
Art. 55FullAdversarial testing conducted, vulnerabilities assessed, incident reporting operationalSystemic risk assessment narratives, AI Office communications, mitigation plans
Art. 16PartialGovernance attestation, registration statusFull QMS documentation, conformity assessment records, CE marking, EU declaration of conformity
Art. 17PartialAudit trail integrity, external timestamp verificationQMS documentation, organizational procedures, compliance management, post-market monitoring plan
Art. 26FullPolicy compliance gates active, shadow AI detection, access controlNone for operational compliance (design-time instruction adherence verified through policy gates)
Art. 27PartialImpact assessment conducted, DPIA completed, fairness metrics assessedFRIA substantive content: scope of rights assessed, methodology, stakeholder consultation, mitigation measures adopted
Art. 50FullContent marked, watermarking activeNone 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.

EntryArticle / ObligationResultEvidence ReferenceInitials
HIGH-RISK AI REQUIREMENTS
WP-01Art. 9 Risk Management System
WP-02Art. 10 Data and Data Governance
WP-03Art. 11 Technical Documentation
WP-04Art. 12 Record-Keeping
WP-05Art. 13 Transparency
WP-06Art. 14 Human Oversight
WP-07Art. 15 Accuracy, Robustness, Cybersecurity
GPAI OBLIGATIONS
WP-08Art. 53 General GPAI Obligations
WP-09Art. 55 Systemic Risk Obligations
PROVIDER AND DEPLOYER OBLIGATIONS
WP-10Art. 16 Provider Obligations
WP-11Art. 17 Quality Management System
WP-12Art. 26 Deployer Obligations
WP-13Art. 27 Fundamental Rights Impact Assessment
WP-14Art. 50 Transparency for Users
Lead Assessor Signature / Date
Technical Expert Signature / Date