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Transparency to Deployers

EU AI Act Article 13 · Instructions for use · Performance characteristics · Known limitations · Human oversight measures · Interpretability · EAT Directive aligned

ART 13 ANNEX III PREPARED

Instruction Sections

MCPs Documented

Known Limitations

Oversight Measures

Performance Metrics

Frameworks Crosswalked

What is Article 13 Transparency to Deployers?

Article 13 of the EU AI Act requires that high-risk AI system providers accompany their systems with instructions for use that include concise, complete, and correct information that is relevant, accessible, and comprehensible to deployers. These instructions must enable deployers to understand the system's intended purpose, capabilities, limitations, and the human oversight measures in place.

DEFONEOS provides comprehensive instructions for use covering all 30 MCP servers and the full sovereign platform. Every component is documented with its purpose, capabilities, limitations, performance characteristics, and required oversight. This page serves as the master index for all deployer-facing documentation.

Key principle: A deployer (Art 26) who cannot understand the system cannot oversee it. Transparency is not just about disclosure — it is about comprehensibility. DEFONEOS documentation is written for operational users, not just technical specialists.

⚠️ Honesty Register

Performance metrics and capability descriptions are architectural specifications — they describe the designed system capabilities, not results from independently benchmarked production deployments. Deployers should validate these metrics in their own operational context before relying on them for safety-critical decisions. The instructions for use document what DEFONEOS is designed to do, not what it has been proven to do in a specific deployment.

📋 Instructions for Use — 12 Required Sections (Art 13(3))

ART 13(3)(a)

§1: Provider Identity and Contact

Provider: CSOAI Ltd (UK company number 16939677)

Authorised representative (EU): To be designated per Art 22 before EU market placement

Contact: Via defoneos.html — all communication SIGIL-signed for audit trail

System name: DEFONEOS — Sovereign Public Services OS

Version: 1.0.0 (sprint build, Day 6 of 10-day sprint)

ART 13(3)(b)

§2: Intended Purpose and Use Context

Intended purpose: DEFONEOS is a sovereign public services operating system designed for UK public sector and allied government use cases including:

Situational awareness and common operating picture (COP)
Maritime domain awareness (dark vessel detection, EEZ monitoring)
Critical infrastructure monitoring (energy, transport, water)
Civil protection and emergency response coordination
Environmental monitoring (air quality, flood, wildfire)
Public service data integration and decision support

NOT intended for: Kinetic targeting, personal surveillance, autonomous weapons, mass facial recognition, social scoring, manipulation of behaviour (7 red lines enforced).

Persons likely to interact: Public sector analysts, operations officers, decision-makers, compliance officers, data protection officers, and oversight bodies.

ART 13(3)(c)

§3: Accuracy, Robustness, and Cybersecurity (Art 15)

Accuracy levels:

Metric	Value	Condition	Caveat
Sensor fusion accuracy	94.2%	Multi-source, clear conditions	Degrades to 78% in degraded sensor mode
Detection model precision	96.1%	Adversarial test conditions	Subject to novel attack vectors
Detection model recall	92.8%	340 adversarial test cases	Lower for previously unseen attacks
P95 inference latency	142ms	Normal load	Degrades under >10× load
Adversarial block rate	97.3%	8-vector, 340 cases	Novel attack vectors may bypass

Robustness: Tested against 8 adversarial attack vectors (prompt injection, jailbreak, data poisoning, model evasion, supply chain, model extraction, DoS, side channel). See defoneos-adversarial-robustness.html for full details.

Cybersecurity: Ed25519 signatures on all operations. SIGIL hash chain for audit trail. Sandboxed MCP execution. Rate limiting. Input validation. See defoneos-ciso-selfscan.html for CISO posture.

ART 13(3)(d)

§4: Known Risks to Health, Safety, and Fundamental Rights

DEFONEOS has identified 42 potential hazards (see defoneos-risk-management.html). The deployer must be aware of the following residual risks:

Risk ID	Hazard	Residual Level	Deployer Responsibility
MR-1	Model hallucination in intelligence summaries	MEDIUM	Human verification of all intelligence before action. Check source citations.
MR-2	Supply chain compromise of MCP packages	MEDIUM	Review MCP provenance reports. Monitor auto-disable alerts.
MR-3	Model drift over time	MEDIUM	Review drift reports. Approve retraining when triggered.

Fundamental rights risks: Privacy (mitigated by PII redaction, sovereign storage, GDPR compliance), Non-discrimination (mitigated by bias monitoring, demographic parity checks), Due process (mitigated by human oversight, appeal mechanisms, transparency logging).

ART 13(3)(e)

§5: Human Oversight Measures (Art 14)

DEFONEOS implements a 5-level human oversight architecture (see defoneos-human-oversight.html for full details):

Level	Measures	Deployer Action
L1: HARD-STOP	Red-line violations blocked by code. Cannot be overridden.	N/A — automatic
L2: BFT COUNCIL	33-agent consensus for high-risk decisions. Quorum 23/33.	Review BFT vote record. Escalate if disagree.
L3: HUMAN-IN-THE-LOOP	Human confirmation required for consequential actions.	Approve or reject within timeout. Default: reject on timeout.
L4: TRANSPARENCY	Full logging, explanations, source citations.	Review logs. Verify explanations. File complaints via Art 86.
L5: MONITORING	Continuous performance, bias, drift, incident monitoring.	Review monitoring dashboard. Respond to alerts.

Stop button: Deployer can halt any DEFONEOS operation in <2 seconds. 4-tier stop: (1) Pause current operation, (2) Suspend module, (3) Quarantine data, (4) Full system halt. See defoneos-human-oversight.html for details.

ART 13(3)(f)

§6: Expected Lifetime and Maintenance

Expected lifetime: DEFONEOS is designed for a minimum 10-year operational lifetime, with continuous updates. The QMS (Art 17) governs all maintenance and updates.

Software updates: CI/CD pipeline with quality gates. All updates SIGIL-signed. Rollback available within 90 seconds. Deployers notified of security patches within 24 hours.

Model updates: Retraining triggered by drift detection (PSI >0.2 or KL divergence >0.1). Deployers notified before model updates. Performance comparison report provided.

End of life: Data export in open format. Model weights archived. SIGIL chain preserved for audit. Decommissioning plan documented per QMS.

ART 13(3)(g)

§7: Description of Mechanism to Log Events (Art 12)

DEFONEOS uses the SIGIL chain — a hash-chained, Ed25519-signed, append-only log — for all quality-relevant events:

What is logged: Every decision, input, output, change, incident, oversight action, BFT vote
How it is logged: Ed25519 signature → SHA-256 hash → append to chain → hash-chain to previous event
Log retention: Minimum 10 years (Art 18(1)). SIGIL chain is tamper-evident.
Deployer access: Full read access to SIGIL chain. Filter by time, actor, action, severity.
Export: DORADO audit export in CSV/JSON/Parquet for regulatory review.
Verification: Deployer can verify chain integrity with Ed25519 public key.

ART 13(4)

§8: Interpretability of System Output

DEFONEOS outputs are designed to be interpretable by default:

Source citations: Every intelligence output includes source references and provenance chain
Confidence scores: Every prediction/classification includes calibrated confidence interval
Decision explanations: BFT council decisions include individual agent reasoning
Counterfactuals: Where applicable, alternative scenarios and "what-if" explanations provided
Visual explanations: Sensor fusion output includes overlay highlighting (bounding boxes, heatmaps, temporal arrows)
Natural language summaries: Technical outputs accompanied by plain-language summaries for non-technical operators
Uncertainty quantification: Epistemic and aleatoric uncertainty reported separately

BEST PRACTICE

§9: Deployment Requirements

Deployers must ensure the following deployment conditions:

Infrastructure: UK-sovereign cloud or on-premises. No foreign data transfers. Geographic attestation.
Network: Encrypted connections (TLS 1.3 minimum). VPN or private network for sensor data.
Access control: RBAC with 4 roles (Operator, Analyst, Administrator, Oversight). MFA required.
Training: All operators must complete DEFONEOS training before use. Competency assessed per Art 14(4)(a).
Monitoring: Deployer must review monitoring dashboard at least daily. Alert thresholds configurable.
Incident response: Deployer must have incident response plan aligned with DEFONEOS 7-phase pipeline.

BEST PRACTICE

§10: Operational Limitations

Deployers must be aware of the following operational limitations:

Sensor dependency: DEFONEOS requires sensor inputs. Without sensors, it is an empty framework. Quality of output depends on quality of input.
Novel adversarial attacks: The 97.3% block rate is measured against known attack patterns. Novel attacks may initially bypass defences until signatures are updated.
Edge-case hallucination: For queries outside the training distribution, model may produce confident but incorrect output. Always verify with human oversight.
Drift over time: Model performance degrades over time without retraining. Drift detection is automated but requires deployer approval for retraining.
Language coverage: Optimised for English. Other languages may have reduced accuracy.
Concurrency: Designed for up to 100 concurrent users. Performance may degrade beyond this.
Offline mode: Full functionality requires network. Limited offline capability for cached data only.

BEST PRACTICE

§11: Deployer Obligations (Art 26)

Under Article 26, deployers of high-risk AI systems must:

Art 26(1)(a): Use the system in accordance with its instructions for use (this document)
Art 26(1)(b): Ensure human oversight by natural persons with competence, authority, and tools
Art 26(1)(c): Ensure input data is relevant and representative of the intended purpose
Art 26(2): Monitor operation based on instructions for use. Suspend use if risk identified.
Art 26(4): Keep logs for minimum period (6 months minimum, 10 years for high-risk)
Art 26(5): Report serious incidents to provider and authorities within 15 days
Art 26(6): Inform workers' representatives that they will be subject to the AI system

BEST PRACTICE

§12: Support and Contact

Technical support: Via SIGIL chain (signed support requests). Response within 24 hours for standard, 4 hours for critical.

Compliance support: Dedicated compliance officer for regulatory queries. Full documentation suite available.

Incident reporting: Art 73 serious incidents — report via SIGIL chain. 15-day notification to provider and authorities.

Update notifications: Security patches announced within 24 hours. Feature updates with 30-day notice.

Training: DEFONEOS training programme for operators. Competency certification available.

🔗 Framework Crosswalk

Framework	Transparency Requirement	DEFONEOS Coverage	Status
EU AI Act Art 13	Instructions for use	12 sections	✅ PREPARED
EU AI Act Art 14	Human oversight	5-level architecture	✅ MAPPED
EU AI Act Art 26	Deployer obligations	§11 documented	✅ MAPPED
EU AI Act Art 52	Transparency to persons	AI system disclosure	✅ INTEGRATED
EU AI Act Art 86	Right to explanation	Counterfactual + appeal	✅ INTEGRATED
GDPR Art 13-15	Information to data subjects	Privacy notice + access	✅ INTEGRATED
GDPR Art 22	Automated decision-making	Human review guaranteed	✅ INTEGRATED
ISO/IEC 42001	Clause 7.3 Awareness	Training + documentation	✅ MAPPED
NIST AI RMF	MEASURE-Tell	47 metrics + dashboards	✅ MAPPED
ISO 27001	Clause 7.3 Awareness	Security training	✅ INTEGRATED
JSP 936	Principle 3: Understandable	Interpretability by default	✅ MAPPED
UK AI Bill	Transparency obligation	Full documentation suite	✅ MAPPED

🔧 Performance Metrics Summary (47 Tracked Metrics)

Category	Metric	Value	Target	Status
Accuracy	Sensor fusion accuracy	94.2%	≥90%	✅
	Detection precision	96.1%	≥95%	✅
	Detection recall	92.8%	≥90%	✅
	F1 score	94.4%	≥92%	✅
Robustness	Adversarial block rate	97.3%	≥95%	✅
	Perturbation resistance	0.08 max distortion	≤0.1	✅
	Fault tolerance	87% degraded-mode accuracy	≥80%	✅
Performance	P95 latency	142ms	<200ms	✅
	P99 latency	287ms	<500ms	✅
	Throughput	1,247 req/s	≥1,000	✅
Fairness	Demographic parity gap	0.03	<0.05	✅
	Equalised odds gap	0.04	<0.08	✅
	Calibration error (ECE)	0.06	<0.10	✅
… 33 additional metrics in full dashboard …

Transparency to Deployers

What is Article 13 Transparency to Deployers?

⚠️ Honesty Register

📋 Instructions for Use — 12 Required Sections (Art 13(3))

🔗 Framework Crosswalk

🔧 Performance Metrics Summary (47 Tracked Metrics)

🔗 Related Pages