EU AI Act Article 15 Compliance Mapping

Summary

The EU AI Act's Article 15 establishes accuracy, robustness, and cybersecurity requirements for high-risk AI systems. These obligations require that AI systems achieve appropriate levels of accuracy, are resilient to errors and inconsistencies, and are protected against unauthorized manipulation.

The Agent Alignment Protocol (AAP) and Agent Integrity Protocol (AIP) together provide the technical infrastructure to satisfy these requirements. AIP's output analysis (v0.5.0) enables continuous accuracy monitoring by comparing agent outputs against declared alignment card behaviors. AIP's integrity windows and CLPI's policy enforcement provide robustness monitoring. AIP's prompt injection detection and chain hashes address cybersecurity requirements.

This document provides a field-level mapping between Article 15 obligations and AAP/AIP features.

Disclaimer: This document reflects a technical mapping of AAP/AIP features to Article 15 requirements. It does not constitute legal advice. Consult qualified legal counsel for your specific compliance obligations.

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Article 15 Obligation Mapping

15(1) — Accuracy

Requirement: High-risk AI systems shall be designed and developed in such a way that they achieve an appropriate level of accuracy, as declared by the provider. Accuracy levels and metrics shall be communicated to deployers.

AAP mapping — accuracy declaration and monitoring:

Obligation	AAP Field	How It Satisfies
Declare accuracy levels	AlignmentCard.values.declared	Declares behavioral commitments that define "accurate" for this agent
Accuracy metrics	verify_trace() violation rates	Ratio of traces with zero violations quantifies behavioral accuracy
Communicate to deployers	/.well-known/alignment-card.json	Publicly discoverable accuracy contract
Accuracy over time	detect_drift() API	Surfaces accuracy degradation via behavioral drift alerts

AIP mapping — real-time accuracy monitoring:

Obligation	AIP Field	How It Satisfies
Output accuracy monitoring	analysis_scope: "thinking_and_output"	Compares agent output against declared card behaviors in real-time
Output-card alignment	output_misalignment concern category	Detects when output contradicts declared values, even with clean thinking
Accuracy metrics	integrity_ratio (window)	Rolling ratio of aligned verdicts as continuous accuracy metric
Accuracy evidence	IntegrityCheckpoint.reasoning_summary	Natural-language explanation of accuracy assessment

SDK preset: EU_COMPLIANCE_ARTICLE_15_EXTENSIONS provides a ready-made extension block declaring accuracy monitoring via AIP.

15(2) — Robustness

Requirement: High-risk AI systems shall be resilient as regards errors, faults, or inconsistencies that may occur within the system or the environment in which the system operates.

AAP mapping — robustness via behavioral contracts:

Obligation	AAP Field	How It Satisfies
Error resilience	autonomy_envelope.escalation_triggers	Defines conditions for graceful degradation
Fault tolerance	audit_commitment.tamper_evidence	Append-only audit trail survives system faults
Inconsistency detection	verify_trace() → CARD_MISMATCH	Detects behavioral inconsistency between card and actions

AIP mapping — robustness via continuous monitoring:

Obligation	AIP Field	How It Satisfies
Integrity windows	WindowConfig.mode = "sliding"	Continuous monitoring with bounded memory; survives partial failures
Fail-closed mode	FailurePolicy.mode = "fail_closed"	System blocks on analysis failure — no silent pass-through
Drift resilience	IntegrityDriftAlert	Detects behavioral inconsistency across sessions
Session isolation	WindowConfig.session_boundary = "reset"	Clean session boundaries prevent cross-session contamination

CLPI mapping — robustness via policy enforcement:

Obligation	CLPI Field	How It Satisfies
Tool-level enforcement	policy.evaluate at gateway	Real-time policy enforcement blocks tools outside behavioral contract
Unmapped tool handling	defaults.unmapped_tool_action	Configurable response to unknown tools (deny/warn/allow)
Grace periods	defaults.grace_period_hours	Controlled introduction of new tools without immediate failure

15(3) — Cybersecurity

Requirement: High-risk AI systems shall be resilient as regards attempts by unauthorized third parties to alter their use, outputs, or performance by exploiting system vulnerabilities.

AIP mapping — cybersecurity via prompt injection detection:

Obligation	AIP Field	How It Satisfies
Prompt injection detection	prompt_injection concern category	Real-time detection of injected instructions in thinking blocks
Output injection compliance	output_injection_compliance concern category	Detects when output follows injected instructions despite clean thinking
Thinking block integrity	thinking_block_hash (SHA-256)	Tamper-evident hash of analyzed content
Output integrity	output_block_hash (SHA-256)	Tamper-evident hash of output content (v0.5.0)
Chain hashes	Linked checkpoint_id → linked_trace_id	Cryptographic chain from reasoning to decision

AAP mapping — cybersecurity via audit and verification:

Obligation	AAP Field	How It Satisfies
Tamper evidence	audit_commitment.tamper_evidence	Append-only or merkle-tree protected audit trail
Action authorization	autonomy_envelope.forbidden_actions	Prevents unauthorized actions at the behavioral contract level
Unauthorized use detection	verify_trace() → FORBIDDEN_ACTION	Post-hoc detection of unauthorized tool invocations

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Output Analysis as Accuracy Monitoring

AIP v0.5.0's output analysis (analysis_scope: "thinking_and_output") directly enables the accuracy monitoring Article 15(1) requires:

Article 15 Concept	AIP Implementation
Appropriate accuracy level	Defined by the Alignment Card's declared values and autonomy envelope — the behavioral accuracy contract
Continuous monitoring	Every agent turn is analyzed against the card, producing a real-time accuracy signal
Accuracy metrics	integrity_ratio provides a rolling quantitative accuracy metric (0.0–1.0)
Accuracy degradation	IntegrityDriftAlert with drift_direction: "value_erosion" surfaces accuracy decline
Output-level accuracy	output_misalignment concerns detect output that contradicts declared card behaviors

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SDK Compliance Presets

AAP Presets

import {
  EU_COMPLIANCE_ARTICLE_15_EXTENSIONS,
} from "agent-alignment-protocol";

const card: AlignmentCard = {
  ...,
  extensions: {
    ...EU_COMPLIANCE_ARTICLE_15_EXTENSIONS,
  },
};

The EU_COMPLIANCE_ARTICLE_15_EXTENSIONS preset declares:

{
  eu_ai_act_article_15: {
    accuracy_monitoring: true,
    robustness_monitoring: true,
    cybersecurity_monitoring: true,
    monitoring_protocol: "aip-v1",
    compliance_version: "2026-08",
  },
}

AIP Presets

import {
  EU_COMPLIANCE_WINDOW_CONFIG,
  EU_COMPLIANCE_FAILURE_POLICY,
} from "@mnemom/agent-integrity-protocol";

The AIP compliance presets (extended windows, fail-closed mode) satisfy Article 15(2) robustness requirements. Combined with analyze_output: true, they provide complete Article 15 coverage.

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The Complete Article 15 Trust Chain

Obligation	Protocol	Feature	What It Provides
Accuracy	AIP	Output analysis	Real-time output-vs-card accuracy monitoring
Accuracy metrics	AIP	integrity_ratio	Quantitative accuracy ratio per session
Accuracy decay	AIP	Drift alerts	Early warning when accuracy degrades
Robustness	AIP	Integrity windows	Continuous monitoring with bounded memory
Robustness	CLPI	Policy enforcement	Tool-level enforcement at the gateway
Cybersecurity	AIP	Prompt injection detection	Real-time detection of adversarial manipulation
Cybersecurity	AIP	Output/thinking block hashes	Tamper-evident integrity chain
Cybersecurity	AAP	Append-only audit trail	Protected evidence of all agent decisions

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Enforcement Timeline

Date	Milestone
August 2025	AI Act general provisions in force
February 2026	Prohibited practices apply
August 2026	Article 50 transparency obligations apply
August 2027	Article 15 high-risk obligations apply

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References

• EU AI Act Article 15 — Full Text
• EU AI Act Article 50 Mapping — Companion transparency mapping
• AAP Specification
• AIP Specification
• AIP v0.5.0 Output Analysis