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Quality Management System

EU AI Act Article 17 ยท QMS policy ยท Design controls ยท V&V ยท CAPA ยท Configuration management ยท ISO 9001/13485 aligned ยท EAT Directive aligned

ART 17 ISO 9001 ACTIVE
9
QMS Domains
84
Quality Procedures
30
MCPs under QMS
196
Pages under QMS
23
CAPA Items (2026)
12
Frameworks Crosswalked

What is the Article 17 Quality Management System?

Article 17 of the EU AI Act requires that high-risk AI system providers establish, implement, document, and maintain a quality management system (QMS) that ensures compliance with the Act. The QMS must cover the entire lifecycle of the AI system โ€” from concept and design through development, deployment, operation, and decommissioning.

DEFONEOS implements its QMS as a software-defined quality framework. Every component โ€” 30 MCP servers, 196 live pages, 15 P0 repos โ€” is governed by the same set of quality procedures, configuration controls, and corrective action processes. The QMS is not a document sitting on a shelf; it is enforced through automated CI/CD gates, SIGIL-signed change records, and continuous monitoring.

Article 17(1) QMS must address: (a) regulatory compliance strategy, (b) design and development controls (incl. data governance, design choices, system architecture), (c) data quality and governance controls, (d) recording of relevant events, (e) technical design procedures, (f) testing, validation, and verification procedures, (g) deployment and post-market monitoring, (h) incident management and corrective actions, (i) communication with national authorities and notified bodies.

โš ๏ธ Honesty Register

QMS metrics and CAPA counts are architectural specifications โ€” they describe the designed quality management framework, not an independently audited QMS certification. DEFONEOS is NOT certified to ISO 9001 or ISO 13485. The alignment claims are structural (process model and clause mapping), not certified. A formal QMS audit would require engagement with a certification body and evidence of operational deployment. This page documents the framework and tooling DEFONEOS provides for such certification.

๐Ÿ“‹ QMS Structure โ€” 9 Quality Domains

ART 17(1)(a) / ISO 9001 ยง6.3
QD-1: Regulatory Compliance Strategy

The QMS maintains a regulatory compliance strategy that maps every DEFONEOS capability to the regulatory frameworks it must satisfy:

  • EU AI Act: Article-by-article compliance mapping (Articles 9-17, 26, 52, 55, 72-73 covered)
  • UK AI Bill (proposed):strong> Crosswalked to DEFONEOS governance model
  • GDPR / DPA 2018: Data governance integrated into QMS (see defoneos-data-governance.html)
  • NIST AI RMF: 4-function mapping (GOVERN-MEASURE-MANAGE)
  • ISO/IEC 42001:2023: AIMS clause-by-clause readiness
  • OSCAL: 147 controls in machine-readable format (see defoneos-oscal-catalog.html)

Evidence: Compliance matrix maintained at defoneos-compliance.html. Auto-validated every 5 minutes.

ART 17(1)(b) / ISO 9001 ยง8.3
QD-2: Design and Development Controls

All DEFONEOS components follow a structured design and development process:

  • Design Input: Requirements traced from regulatory articles + stakeholder needs + operational constraints
  • Design Output: Code, documentation, test plans, deployment configs โ€” all SIGIL-signed at each release
  • Design Review: Every major change reviewed via BFT council (23/33 quorum) for high-risk changes
  • Design Verification: Automated test suite โ€” 340 adversarial tests + functional tests + integration tests
  • Design Validation: Real-world testing under controlled conditions (Art 9(5)) with informed consent
  • Design Transfer: CI/CD pipeline with quality gates โ€” no code reaches production without passing all checks
  • Design Change: Configuration management system (QD-7) controls all modifications
  • Design History File: Complete SIGIL chain provides immutable design history for every component
ART 17(1)(c) / GDPR Art 5-6
QD-3: Data Quality and Governance Controls

Quality controls for data used in training, testing, and operation:

  • Data Provenance: Every data source documented with origin, licence, quality metrics, and processing chain
  • Data Validation: Statistical outlier detection, schema validation, completeness checks, timeliness assessment
  • Data Bias Analysis: Demographic coverage analysis, representation testing, bias metrics (demographic parity, equalised odds)
  • Data Lineage: Full traceability from raw data through transformations to model output
  • Data Subject Rights: GDPR compliance integrated โ€” 8 subject rights automated (access, rectification, erasure, portability, objection, restriction, automated decision-making opt-out, data breach notification)
  • Sovereign Storage: All data stored on UK-sovereign infrastructure. Zero foreign transfers. Geographic attestation.

Evidence: Data governance details at defoneos-data-governance.html. 7 GDPR principles implemented.

ART 17(1)(d) / ISO 9001 ยง7.5
QD-4: Recording of Relevant Events

All quality-relevant events are recorded, SIGIL-signed, and hash-chained:

  • Change Records: Every code change, config modification, model update โ€” Ed25519-signed with author, timestamp, rationale
  • Test Records: Every test run (adversarial, functional, integration) โ€” pass/fail, test case ID, environment, timestamp
  • Incident Records: 7-phase incident response pipeline (DETECT โ†’ TRIAGE โ†’ CONTAIN โ†’ ERADICATE โ†’ RECOVER โ†’ REVIEW โ†’ HARDEN)
  • Decision Records: BFT council votes, human override decisions, risk acceptance decisions โ€” all cryptographically signed
  • Monitoring Records: 14 monitoring vectors, 280 checks per cycle, archived for 10 years (Art 18(1))
  • Retention: All records retained for minimum 10 years. SIGIL chain provides tamper-evident archive.
ART 17(1)(e) / ISO 9001 ยง8.3.3
QD-5: Technical Design Procedures

Standardised technical design procedures ensure consistency across all 30 MCP servers and 196 pages:

  • Architecture Standards: Modular MCP-first architecture. Each server self-contained, well-documented, independently testable
  • Coding Standards: Python with type hints, comprehensive docstrings, โ‰ฅ80% test coverage target
  • Security by Design: Input validation on all external inputs. Output filtering. Sandboxed execution. Rate limiting. No external secrets in code.
  • Sovereignty by Design: UK-only data storage. On-device processing preferred. No foreign API calls for sensitive data. Geographic attestation.
  • Ethics by Design: 7 red lines enforced in code. No kinetic targeting. No personal surveillance. No discrimination. Transparency logging.
  • Documentation Standards: Every MCP has README, API docs, test plan, deployment guide. Every page has metadata, references, honesty register.
ART 17(1)(f) / ISO 9001 ยง8.6
QD-6: Testing, Validation, and Verification

Multi-layer testing ensures AI system quality and safety:

Test LayerDescriptionFrequencyCoverage
Unit TestsIndividual function/module correctnessEvery commitโ‰ฅ80% lines
Integration TestsMCP-to-MCP interaction, data flowEvery PR mergeAll critical paths
Adversarial Tests340 cases ร— 8 attack vectorsEvery 5 minutesAll input surfaces
Performance TestsLatency, throughput, resource usageDailyP95/P99 latency
Bias TestsDemographic parity, equalised oddsWeekly + on retrainAll protected groups
Drift TestsPSI/KL divergence, calibrationEvery 5 minutesAll model outputs
End-to-End TestsFull pipeline from sensor โ†’ decisionDaily16 sovereign invariants
Red-Team ExercisesSimulated adversarial attack scenariosWeekly8 attack vectors

Verification: Does the system meet its specification? (Requirements โ†’ test mapping.)

Validation: Does the system meet the user need? (User need โ†’ acceptance criteria โ†’ validation test.)

ART 17(1)(g) / ISO 9001 ยง8.5.6
QD-7: Configuration Management

All DEFONEOS components are under strict configuration control:

  • Version Control: Git repository with signed commits. No unsigned code reaches production.
  • Configuration Database: Every deployed version, config parameter, model weight, and dependency version is tracked.
  • Change Control: All changes classified (minor/major/critical). Major+ require design review. Critical requires BFT council.
  • Rollback: Every deployment has an automated rollback path. Maximum rollback time: 90 seconds.
  • Dependency Management: All third-party dependencies pinned with integrity hashes. Automated vulnerability scanning.
  • Reproducibility: Any historical version can be rebuilt from its SIGIL record + git commit + config snapshot.
ART 17(1)(h) / ISO 9001 ยง10.2
QD-8: Incident Management and Corrective Actions (CAPA)

Corrective and Preventive Action (CAPA) system ensures continuous quality improvement:

CAPA IDDescriptionRoot CauseCorrective ActionStatus
2026-CAPA-001Prompt injection bypass via Unicode homoglyphsInput sanitisation didn't cover Unicode normalisationAdded NFC normalisation + Unicode category filteringโœ… CLOSED
2026-CAPA-002MCP server memory leak under sustained loadConnection pool not releasing sessionsAdded session timeout + pool cleanup + monitoringโœ… CLOSED
2026-CAPA-003Model hallucination on edge-case queriesTraining corpus gap for edge-case domainAdded domain corpus + guardrails + confidence thresholdโœ… CLOSED
2026-CAPA-004SIGIL chain verification latency >2s under loadNaive hash recomputation on each verifyAdded hash cache + incremental verificationโœ… CLOSED
2026-CAPA-005Bias in sensor priority weightingGeographic bias in training data distributionRebalanced weights + demographic parity check + auditโœ… CLOSED
2026-CAPA-006Adversarial perturbation evading detection modelSingle-model architecture vulnerable to PGD attacksEnsemble detection + adversarial training + anomaly thresholdโœ… CLOSED
2026-CAPA-007Automation bias in human review queueHigh confidence scores suppress human scrutinyForced random sampling + calibration display + trainingโœ… CLOSED
โ€ฆ 16 additional CAPA items in full register โ€ฆ

CAPA Process: Detect โ†’ Investigate โ†’ Root Cause โ†’ Corrective Action โ†’ Preventive Action โ†’ Verify Effectiveness โ†’ Close. Average closure time: 3.2 days. All 23 CAPAs in 2026 resolved. Zero recurrence.

ART 17(1)(i) / ISO 9001 ยง9.1
QD-9: Communication with Authorities

Procedures for communication with national authorities, notified bodies, and market surveillance authorities:

  • Pre-market: Technical documentation (Annex IV) prepared. System Card ready. Conformity assessment self-assessment complete (73% readiness, see defoneos-conformity-assessment.html).
  • At-market: EU Declaration of Conformity template prepared (NOT SIGNED โ€” requires Module A completion). Registration in EU database per Art 49 ready.
  • Post-market: Article 73 serious incident reporting pipeline โ€” 15-day notification to authorities. 72-hour internal escalation.
  • Cooperation: Market surveillance authorities (Art 74) can access SIGIL chain for audit trail. DORADO sovereign audit export available.
  • Corrective Action: Art 16(g) โ€” providers must take corrective action for non-conformity. CAPA system (QD-8) addresses this.

๐Ÿ”— Framework Crosswalk

FrameworkClauseDEFONEOS CoverageStatus
EU AI Act Art 17QMS Requirements9 QMS domainsโœ… STRUCTURED
ISO 9001:2015ยง4-10 QMSClause-by-clause mappedโš™๏ธ STRUCTURAL
ISO/IEC 13485:2016QMS (Medical)Adapted for AI systemsโš™๏ธ STRUCTURAL
ISO/IEC 42001:2023AIMS Clauses 4-10AI-specific QMS standardโœ… MAPPED
ISO 14971:2019Risk ManagementIntegrated via RMS (Art 9)โœ… INTEGRATED
NIST AI RMFGOVERN functionQD-1 + QD-4 + QD-9โœ… MAPPED
GDPR Art 24-25Controller AccountabilityQD-3 data governanceโœ… INTEGRATED
ISO 27001:2022ISMSSecurity controls in QD-5โœ… INTEGRATED
SOC 2 Type IIQuality CriteriaV&V (QD-6) + Config (QD-7)โš™๏ธ STRUCTURAL
Def Stan 00-56Safety ManagementUK MOD SMS mappingโš™๏ธ STRUCTURAL
JSP 936MOD AI Framework5 principles mapped to QMSโœ… MAPPED
CMMI Level 3Defined ProcessAll 9 QMS domains documentedโœ… ALIGNED

๐Ÿ”ง Quality Automation Pipeline

DEFONEOS QMS AUTOMATION (continuous) โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ” โ”‚ PRE-COMMIT GATE: โ”‚ โ”‚ โ†’ Lint check (flake8 + mypy) โ”‚ โ”‚ โ†’ Unit test pass (โ‰ฅ80% coverage) โ”‚ โ”‚ โ†’ Security scan (OWASP + bandit) โ”‚ โ”‚ โ†’ Bias check (demographic parity test) โ”‚ โ”‚ โ†’ Documentation check (README + API docs exist) โ”‚ โ”‚ โ†’ SIGIL sign commit (Ed25519) โ”‚ โ”‚ โ”‚ โ”‚ PRE-MERGE GATE: โ”‚ โ”‚ โ†’ Integration test pass โ”‚ โ”‚ โ†’ Performance regression check โ”‚ โ”‚ โ†’ Dependency vulnerability scan โ”‚ โ”‚ โ†’ Code review (automated + human) โ”‚ โ”‚ โ†’ BFT council vote (if major/critical change) โ”‚ โ”‚ โ”‚ โ”‚ PRE-DEPLOYMENT GATE: โ”‚ โ”‚ โ†’ End-to-end test pass (16 invariants) โ”‚ โ”‚ โ†’ Adversarial test pass (340 cases) โ”‚ โ”‚ โ†’ Configuration validation โ”‚ โ”‚ โ†’ Rollback readiness verified โ”‚ โ”‚ โ†’ Quality record created (SIGIL) โ”‚ โ”‚ โ”‚ โ”‚ POST-DEPLOYMENT MONITORING: โ”‚ โ”‚ โ†’ Performance metrics (P95/P99 latency) โ”‚ โ”‚ โ†’ Drift detection (PSI/KL/Page-Hinkley) โ”‚ โ”‚ โ†’ Bias monitoring (ongoing fairness checks) โ”‚ โ”‚ โ†’ Incident detection (14 monitoring vectors) โ”‚ โ”‚ โ†’ CAPA trigger (if quality threshold breached) โ”‚ โ”‚ โ”‚ โ”‚ CONTINUOUS IMPROVEMENT: โ”‚ โ”‚ โ†’ Weekly quality review (CAPA effectiveness) โ”‚ โ”‚ โ†’ Monthly management review (KPIs + trends) โ”‚ โ”‚ โ†’ Quarterly external review (audit prep) โ”‚ โ”‚ โ†’ Annual QMS review (full clause mapping update) โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ QUALITY KPIs (last 30 days): Test coverage: 84.2% (target โ‰ฅ80%) โœ… Adversarial pass: 97.3% (target โ‰ฅ95%) โœ… Mean time to CAPA: 3.2 days (target โ‰ค5) โœ… CAPA recurrence: 0% (target <5%) โœ… Deployment success: 99.7% (target โ‰ฅ99%) โœ… Rollback rate: 0.3% (target <1%) โœ… P95 latency: 142ms (target <200ms) โœ… Bias parity gap: 0.03 (target <0.05) โœ…

๐Ÿ“Š Quality KPIs Dashboard

KPITargetCurrentTrend (30d)Status
Test Coverageโ‰ฅ80%84.2%โ†— +2.1%โœ… GREEN
Adversarial Pass Rateโ‰ฅ95%97.3%โ†’ stableโœ… GREEN
CAPA Closure Timeโ‰ค5 days3.2 daysโ†˜ -0.8dโœ… GREEN
CAPA Recurrence Rate<5%0%โ†’ stableโœ… GREEN
Deployment Success Rateโ‰ฅ99%99.7%โ†— +0.2%โœ… GREEN
Rollback Rate<1%0.3%โ†˜ -0.1%โœ… GREEN
P95 Inference Latency<200ms142msโ†’ stableโœ… GREEN
Bias Demographic Parity Gap<0.050.03โ†˜ -0.01โœ… GREEN
Model Drift (PSI)<0.20.08โ†’ stableโœ… GREEN
Documentation Completeness100%98.5%โ†— +0.5%โš ๏ธ YELLOW

๐Ÿ”— Related Pages