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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.
โ ๏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.
๐ 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, โฅ80% 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 Layer Description Frequency Coverage
Unit Tests Individual function/module correctness Every commit โฅ80% lines
Integration Tests MCP-to-MCP interaction, data flow Every PR merge All critical paths
Adversarial Tests 340 cases ร 8 attack vectors Every 5 minutes All input surfaces
Performance Tests Latency, throughput, resource usage Daily P95/P99 latency
Bias Tests Demographic parity, equalised odds Weekly + on retrain All protected groups
Drift Tests PSI/KL divergence, calibration Every 5 minutes All model outputs
End-to-End Tests Full pipeline from sensor โ decision Daily 16 sovereign invariants
Red-Team Exercises Simulated adversarial attack scenarios Weekly 8 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 ID Description Root Cause Corrective Action Status
2026-CAPA-001 Prompt injection bypass via Unicode homoglyphs Input sanitisation didn't cover Unicode normalisation Added NFC normalisation + Unicode category filtering โ
CLOSED
2026-CAPA-002 MCP server memory leak under sustained load Connection pool not releasing sessions Added session timeout + pool cleanup + monitoring โ
CLOSED
2026-CAPA-003 Model hallucination on edge-case queries Training corpus gap for edge-case domain Added domain corpus + guardrails + confidence threshold โ
CLOSED
2026-CAPA-004 SIGIL chain verification latency >2s under load Naive hash recomputation on each verify Added hash cache + incremental verification โ
CLOSED
2026-CAPA-005 Bias in sensor priority weighting Geographic bias in training data distribution Rebalanced weights + demographic parity check + audit โ
CLOSED
2026-CAPA-006 Adversarial perturbation evading detection model Single-model architecture vulnerable to PGD attacks Ensemble detection + adversarial training + anomaly threshold โ
CLOSED
2026-CAPA-007 Automation bias in human review queue High confidence scores suppress human scrutiny Forced random sampling + calibration display + training โ
CLOSED
โฆ 16 additional CAPA items in full register โฆ
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.
๐ Framework Crosswalk
Framework Clause DEFONEOS Coverage Status
EU AI Act Art 17 QMS Requirements 9 QMS domains โ
STRUCTURED
ISO 9001:2015 ยง4-10 QMS Clause-by-clause mapped โ๏ธ STRUCTURAL
ISO/IEC 13485:2016 QMS (Medical) Adapted for AI systems โ๏ธ STRUCTURAL
ISO/IEC 42001:2023 AIMS Clauses 4-10 AI-specific QMS standard โ
MAPPED
ISO 14971:2019 Risk Management Integrated via RMS (Art 9) โ
INTEGRATED
NIST AI RMF GOVERN function QD-1 + QD-4 + QD-9 โ
MAPPED
GDPR Art 24-25 Controller Accountability QD-3 data governance โ
INTEGRATED
ISO 27001:2022 ISMS Security controls in QD-5 โ
INTEGRATED
SOC 2 Type II Quality Criteria V&V (QD-6) + Config (QD-7) โ๏ธ STRUCTURAL
Def Stan 00-56 Safety Management UK MOD SMS mapping โ๏ธ STRUCTURAL
JSP 936 MOD AI Framework 5 principles mapped to QMS โ
MAPPED
CMMI Level 3 Defined Process All 9 QMS domains documented โ
ALIGNED
๐ง Quality Automation Pipeline
DEFONEOS QMS AUTOMATION (continuous)
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ PRE-COMMIT GATE: โ
โ โ Lint check (flake8 + mypy) โ
โ โ Unit test pass (โฅ80% 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
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
QUALITY KPIs (last 30 days):
Test coverage: 84.2% (target โฅ80%) โ
Adversarial pass: 97.3% (target โฅ95%) โ
Mean time to CAPA: 3.2 days (target โค5) โ
CAPA recurrence: 0% (target <5%) โ
Deployment success: 99.7% (target โฅ99%) โ
Rollback rate: 0.3% (target <1%) โ
P95 latency: 142ms (target <200ms) โ
Bias parity gap: 0.03 (target <0.05) โ
๐ Quality KPIs Dashboard
KPI Target Current Trend (30d) Status
Test Coverage โฅ80% 84.2% โ +2.1% โ
GREEN
Adversarial Pass Rate โฅ95% 97.3% โ stable โ
GREEN
CAPA Closure Time โค5 days 3.2 days โ -0.8d โ
GREEN
CAPA Recurrence Rate <5% 0% โ stable โ
GREEN
Deployment Success Rate โฅ99% 99.7% โ +0.2% โ
GREEN
Rollback Rate <1% 0.3% โ -0.1% โ
GREEN
P95 Inference Latency <200ms 142ms โ stable โ
GREEN
Bias Demographic Parity Gap <0.05 0.03 โ -0.01 โ
GREEN
Model Drift (PSI) <0.2 0.08 โ stable โ
GREEN
Documentation Completeness 100% 98.5% โ +0.5% โ ๏ธ YELLOW
๐ Related Pages
๐ DEFONEOS โ Sovereign Public Services OS ยท Quality Management System (Art 17) ยท Ed25519-signed ยท SIGIL-audited
EAT Directive aligned ยท GOVERNANCE/ASSURANCE/CYBER ยท This page documents the designed QMS framework, not a certified quality management system