EU AI Act Annex IV – AI System Technical Dossier
(Illustrative Project)
AI governance is often framed as restriction — but in practice, it is what allows trust to scale.
This project approaches the EU AI Act’s Annex IV requirements as a design challenge: how to make AI systems transparent, accountable, and governable without undermining innovation.
The result is an illustrative system dossier that shows how regulatory expectations can be transformed into documentation that real teams can understand and use.
-
As artificial intelligence systems move from experimentation into regulated, customer-facing, and high-impact use cases, organizations face a growing gap between AI innovation and regulatory defensibility.
The EU AI Act formalizes this shift by introducing explicit technical documentation requirements for high-risk AI systems. In practice, many organizations struggle with:
Fragmented understanding of Annex IV requirements
Documentation scattered across teams and tools
Limited visibility into how AI systems actually work end-to-end
Late involvement of legal, risk, and security functions
Difficulty demonstrating compliance in a consistent, audit-ready way
This creates both regulatory exposure and delivery friction, especially in highly regulated industries.
-
How can an organization produce a single, defensible, and maintainable technical dossier for a high-risk AI system, one that satisfies EU AI Act Annex IV requirements while remaining usable for engineering, security, and governance teams?
More specifically:
What evidence must exist before deployment?
How do we connect system design, data governance, risk management, and security controls into one coherent record?
How do we give legal and compliance teams visibility without slowing development?
How do we maintain this documentation as systems evolve?
-
This project assembles an end-to-end AI system technical dossier, modeled on EU AI Act Annex IV expectations, for an illustrative high-risk AI use case.
The dossier is designed as a single source of truth that brings together technical, risk, and governance information that is often fragmented across organizations.
Included Documentation Components
System Description & Intended Purpose
Scope, users, deployment context, and limitations
System Architecture Overview
High-level design, components, data flows, and integrations
Data Governance & Data Management
Data sources, quality controls, bias considerations, and lifecycle handling
Risk Management File
Identified AI risks, mitigations, residual risk, and monitoring approach
Human Oversight Design
Where and how humans supervise, intervene, or override system behavior
Accuracy, Robustness, and Performance
Evaluation approach, metrics, and testing considerations
Cybersecurity & Security Controls
Protection of data, models, interfaces, and logs
Logging, Monitoring, and Post-Market Oversight
Operational monitoring, incident handling, and continuous improvement
-
This dossier is designed to support cross-functional AI governance rather than any single function.
Engineering & Product
Clarifies documentation expectations early in the lifecycle
Reduces late-stage compliance surprises
Security
Anchors AI-specific threats, controls, and monitoring in a consistent record
Legal & Compliance
Provides visibility into system design and risk posture without reverse-engineering technical details
Risk & Governance Committees
Enables structured review, approval, and escalation for high-risk AI systems
Executive & Board Oversight
Supports defensible disclosures and assurance discussions
The structure is intentionally reusable across AI systems to promote consistency and scale.
-
Regulatory Readiness
Clear alignment with EU AI Act Annex IV expectations
Auditability & Traceability
One coherent record instead of disconnected documents
Earlier Legal & Risk Involvement
Governance embedded upstream, not bolted on
Reduced Delivery Friction
Clear expectations for teams building AI systems
Improved Executive Confidence
Transparent ownership, controls, and risk posture
Ultimately, this approach shifts AI governance from reactive documentation to designed accountability.
-
This project is an illustrative governance and documentation artifact created for learning and demonstration purposes.
It does not represent any specific organization, system, or regulatory determination.The intent is to demonstrate how Annex IV requirements can be operationalized into practical, structured documentation that supports real-world AI governance.
-
This dossier is designed to integrate with broader AI governance assets, including:
AI system inventories and classification models
AI risk registers mapped to controls
Secure GenAI reference architectures
AI incident response playbooks
Board-level AI risk reporting
-
This project demonstrates representative documentation components aligned to EU AI Act Annex IV, including:System description and intended purpose
Architecture and data flow overview
Risk management file (sample excerpt)
Human oversight and monitoring design
Security and robustness considerations
Post-market monitoring approach
-
This PDF provides a selected, illustrative excerpt of an AI system description prepared to demonstrate how EU AI Act Annex IV technical documentation can be operationalized in practice.
Review here: ai-system-description_adariandewberry.pdf
The document is hypothetical and intended for demonstration purposes only.
Additional governance artifacts (risk management, oversight, monitoring) are described at a high level on this page.
-
This table provides a representative excerpt of an AI risk management file, demonstrating how material risks can be identified, assessed, mitigated, and monitored for a high-risk AI system.
Review here: ai-risk-management-adariandewberry.pdf
Risk identification and treatment are designed to align with broader enterprise risk management practices.
-
This reference architecture (PDF) illustrates how Annex IV documentation maps onto a modern enterprise GenAI system, highlighting where governance, security, and human oversight controls are applied across the lifecycle.
I approach GenAI architecture less as a technical novelty and more as a question of responsibility. When AI systems are introduced into regulated environments, the most important design decisions are often not about model capability, but about where judgment lives, where risk is surfaced, and how accountability is preserved as systems scale.
This architecture reflects a realistic internal GenAI decision-support pattern, similar to what many organizations are already deploying to help employees navigate policies, analyze documents, or support internal decision-making. While large language models sit at the center of the system, they are intentionally surrounded by layers that constrain behavior, protect sensitive information, and make system activity observable rather than opaque.
What matters most to me in this design is that governance is not bolted on after deployment. Controls for access, data use, policy enforcement, logging, and human review are embedded across the flow of the system. This makes it possible for engineering teams to move with clarity, for security teams to understand where risk concentrates, for legal and compliance teams to gain visibility into how the system actually works, and for leadership to retain confidence that AI is being used within defined boundaries.
Rather than treating architecture and governance as separate concerns, this design shows how they reinforce each other. The result is a GenAI system that remains useful and adaptable, while still being explainable, governable, and defensible under scrutiny.
This reference architecture is illustrative and intended to demonstrate how governance and security considerations can be integrated into modern GenAI system design.