Translate AI legislation into accountable operating controls

Executive overview

Regulators worldwide are converging on risk-based governance for artificial intelligence. Regulation (EU) 2024/1689 (the EU AI Act) introduces the first comprehensive statutory framework for AI, classifying systems by risk, imposing obligations on providers, deployers, importers, and distributors, and establishing the European AI Office for oversight.^{Regulation (EU) 2024/1689} The United States’ National AI Initiative Act of 2020, enacted as Division E, Title VI of the National Defense Authorization Act for Fiscal Year 2021, codifies a whole-of-government strategy that coordinates research, standards, workforce development, and international cooperation.^{Pub. L. 116-283, Div. E, Title VI} Executive Order 14110 on Safe, Secure, and Trustworthy AI, issued 30 October 2023, leverages statutory authorities—including the Defense Production Act and the National AI Initiative Act—to mandate reporting on foundation models, expand standards development, and direct agencies to implement safety guardrails.^{Executive Order 14110}

Compliance cannot be an afterthought. Organisations must map AI inventories to risk classes, implement conformity assessments, publish transparency documentation, and respond to reporting deadlines. This guide provides the blueprint: from establishing AI oversight boards and harmonising inventories, to designing conformity assessment pipelines that satisfy Annex IV technical documentation requirements, to orchestrating U.S. agency reporting and voluntary commitments. It bridges policy, engineering, legal, and product teams so compliance accelerates rather than impedes delivery.

Use this guide to complement Zeph Tech’s AI model evaluation operations and AI procurement governance playbooks. Together they form a comprehensive operating model for AI risk management, procurement due diligence, and regulatory engagement.

Legislative baseline

Regulation (EU) 2024/1689 (EU AI Act). Published in the Official Journal on 12 July 2024, the AI Act entered into force on 1 August 2024. Prohibited AI practices (Article 5) must cease within six months (by 2 February 2025). Obligations for general-purpose AI (GPAI) models in Title VIII apply 12 months after entry into force (2 August 2025), while high-risk system requirements in Title III apply 24 to 36 months depending on the Annex category (2 August 2026 for Annex II products already regulated, 2 August 2027 for other high-risk systems). Providers must implement risk management systems (Article 9), data governance (Article 10), technical documentation (Article 11 and Annex IV), record-keeping (Article 12), transparency (Article 13), human oversight (Article 14), accuracy/robustness (Article 15), and quality management systems (Article 17). Deployers share obligations, including ensuring human oversight and post-market monitoring (Article 29). National competent authorities oversee enforcement, coordinated by the European AI Office and the AI Board.

National AI Initiative Act of 2020. The Act establishes the National AI Initiative Office, an interagency committee, and advisory committees to coordinate federal AI research and policy. Section 5204 requires a strategic plan for AI research and development. Section 5205 authorises the development of a National AI Research Resource. Section 5207 emphasises standards and risk management through NIST. Section 5106 (codified at 15 U.S.C. § 9415) tasks agencies with expanding AI education and workforce development.^{Pub. L. 116-283, Div. E, Title VI} These statutory mandates underpin federal guidance and grant programmes. Organisations seeking federal partnerships or funding must align with the initiative’s strategic priorities and reporting expectations.

Executive Order 14110 and implementing guidance. While an executive order is not legislation, it invokes statutory authorities to compel action. For example, Section 4.2 relies on the Defense Production Act to require developers of dual-use foundation models to report safety test results to the Department of Commerce. Section 5 directs the development of NIST AI RMF profiles and establishes a Safety and Security Board. The Office of Management and Budget’s Memorandum M-24-10, issued 28 March 2024, translates these directives into mandatory agency actions, including designating Chief AI Officers, inventorying AI use cases, and implementing risk management processes.^{OMB M-24-10} Private organisations that sell to the U.S. government or follow federal guidance voluntarily should mirror these controls.

Other jurisdictions—from Canada’s proposed Artificial Intelligence and Data Act to Singapore’s Model AI Governance Framework—are evolving rapidly. However, the EU AI Act and U.S. National AI Initiative Act provide the most comprehensive statutory anchors today. Building compliance programmes around these pillars ensures global readiness.

Risk classification and inventorying

Inventory accuracy underpins compliance. Create an AI system registry capturing provider, deployer, intended purpose, users, data sources, model type, deployment status, jurisdictions, and risk classification.

Classification workflow. Apply Article 6 risk classification criteria to determine whether systems are high-risk. Annex III enumerates categories such as biometric identification, critical infrastructure, education, employment, essential services, law enforcement, migration, and justice. Systems that are safety components of Annex II products (e.g., medical devices, machinery) are high-risk by default. GPAI systems require transparency obligations and, if they pose systemic risk, additional assessments. Implement automation that flags high-risk attributes using metadata and triggers legal review.

Risk statements. For each system, document risk statements summarising potential impacts on health, safety, and fundamental rights. Include regulatory references (Article 9 risk management requirements, Article 29 deployer obligations). Align risk statements with NIST AI RMF functions (Map, Measure, Manage, Govern). Record severity, likelihood, and existing mitigations.

Governance integration. Sync the AI registry with enterprise GRC tools, product roadmaps, and procurement platforms. Provide access to compliance, legal, engineering, and policy teams. Set review cadences (quarterly for high-risk, semi-annual for limited risk). Track lineage: training data sources, model versions, fine-tuning runs, evaluation datasets, and change history.

Documentation requirements. Annex IV technical documentation requires system descriptions, design specifications, risk management documentation, data governance, performance metrics, and post-market monitoring plans. Build templates pre-populated with risk registry data. Ensure documentation is stored securely but accessible for conformity assessments and regulator inspections.

Governance and oversight

Design governance structures that satisfy EU AI Act quality management expectations and U.S. federal coordination mandates.

AI oversight board. Establish an AI oversight board chaired by the Chief Risk Officer and Chief Technology Officer. Include representatives from legal, compliance, data science, security, ethics, and public policy. The board approves risk classifications, monitors compliance KPIs, and escalates issues to the board of directors. Document charters referencing Article 17 quality management systems.

Chief AI Officer mandate. Mirror OMB M-24-10 by appointing a Chief AI Officer (CAIO) with authority to manage AI inventories, oversee risk assessments, and coordinate with regulators. Define reporting lines to the CEO and board committees. Document responsibilities, including ensuring compliance with EU AI Act obligations for EU operations and aligning with National AI Initiative priorities for U.S. partnerships.

Policies and standards. Develop policies covering AI system development, deployment, monitoring, incident response, and decommissioning. Reference legislative articles explicitly. For example, include sections detailing how Article 10 data governance is operationalised (data quality checks, bias assessments), how Article 14 human oversight is ensured (runbooks, staffing), and how U.S. agencies’ ethical AI principles are integrated. Link to technical standards (ISO/IEC 42001, ISO/IEC 23894, NIST AI RMF, NIST SP 800-171 for security).

Ethics and external advisors. Assemble an ethics panel with external experts to review high-risk deployments. Provide them with documentation, evaluation results, and community feedback. Record deliberations and recommendations. This demonstrates robust oversight to regulators and aligns with National AI Initiative Act goals of fostering public trust.

Training and accountability. Create training curricula for developers, product managers, executives, and board members. Modules should cover AI Act risk tiers, obligations, fines (up to 7 percent of worldwide turnover for prohibited practices), U.S. statutory mandates, and agency guidance. Track completion, comprehension, and refresher cycles. Include scenario-based exercises referencing real enforcement actions.

Lifecycle controls

Embed compliance checkpoints throughout the AI lifecycle.

Design phase. Require Responsible AI Impact Assessments (RAIAs) that cover intended purpose, stakeholders, harm analysis, data sources, legal basis, and mitigation strategies. Align RAIA questions with Article 9 risk management steps and OMB M-24-10 impact assessment requirements. Ensure the RAIA is approved before significant investment.

Development phase. Implement data governance pipelines that document data provenance, consent, representativeness, and bias analysis per Article 10. Use version-controlled data catalogs. For high-risk systems, enforce design controls (requirements traceability, verification plans) and maintain design history files akin to Annex IV documentation.

Validation phase. Conduct pre-deployment testing that covers accuracy, robustness, cybersecurity, fairness, and explainability. Align with NIST AI RMF Measure function and EU AI Act Annex IV requirements. Use Zeph Tech’s model evaluation guide to structure tests. Document results, acceptance criteria, and residual risk. Ensure human oversight plans (Article 14) are implemented, including roles, training, and decision thresholds.

Deployment phase. Implement release gating requiring CAIO and product owner approval. Document compliance sign-offs. Provide user-facing transparency per Article 52 (e.g., informing users they are interacting with AI systems). Maintain logs for traceability, aligning with Article 12 record-keeping.

Monitoring phase. Establish post-market surveillance programmes. Collect performance metrics, incidents, user complaints, and monitoring results. Review quarterly for high-risk systems. Update risk assessments, retraining processes, and documentation. Report significant changes to regulators where required. Align with National AI Initiative Act reporting obligations when participating in federal programmes.

Retirement phase. Define decommissioning procedures, including data archival, access revocation, incident closure, and stakeholder notification. Document the rationale, lessons learned, and knowledge transfer.

Procurement and third parties

Third-party AI systems introduce compliance risk. Align procurement processes with Article 28 (obligations for importers, distributors, and deployers) and U.S. federal acquisition expectations.

Due diligence checklists. Require vendors to provide conformity assessment declarations, Annex IV documentation, cybersecurity measures, and human oversight plans. Evaluate compliance with Article 53 obligations for emotion recognition, biometric categorisation, or biometric identification. Verify whether the vendor’s GPAI models meet Title VIII requirements (training data summaries, evaluation metrics, systemic risk assessments).

Contractual clauses. Include audit rights, incident notification, update commitments, and indemnification for regulatory fines. Require alignment with National AI Initiative standards and Executive Order 14110 reporting obligations when relevant (e.g., foundation model compute thresholds). Mandate adherence to NIST AI RMF and ISO/IEC 42001.

Supply chain monitoring. Maintain a third-party risk register tracking compliance status, remediation actions, and renewal decisions. Integrate with third-party oversight processes. Perform periodic audits, requesting logs, evaluation results, and incident reports.

Government contracting readiness. For organisations bidding on U.S. federal contracts, align with Federal Acquisition Regulation (FAR) updates implementing Executive Order 14110 directives. Prepare to document AI inventories, risk assessments, and safeguards in proposals. Maintain readiness to share documentation with contracting officers.

Transparency, documentation, and reporting

Transparency builds trust and satisfies legal mandates.

Technical documentation. Maintain Annex IV packages for each high-risk system, including system description, training data characteristics, risk management, design and development procedures, post-market monitoring, and human oversight. Keep documents current and accessible to market surveillance authorities within 15 days of request.

Public disclosures. Publish AI system summaries covering intended use, limitations, and safety controls. For GPAI systems, share training data summaries, evaluation metrics, energy consumption, and safeguards, as required by Title VIII. Provide user-facing documentation and API references for developers and customers.

Regulatory reporting. Submit notifications to national competent authorities when launching high-risk systems (Article 49). Report serious incidents or malfunction leading to breach of fundamental rights within 15 days (Article 65). Track deadlines for EU-level reporting, such as AI Office monitoring requests. In the U.S., align with agency reporting obligations tied to grants or contracts and respond to requests from the National AI Initiative Office.

Serious-incident reporting readiness

The European Commission’s Article 73 consultation closed 7 November 2025 with draft templates that require 24-hour initial notifications, seven-day interim reports, and 30-day remediation updates. Providers and deployers must stage reporting workflows before the AI Act’s August 2026 enforcement to avoid penalties and corrective measures.^{Policy Briefing — November 7, 2025Article 73 consultationEuropean AI Office implementing notice}

• Map detection sources. Connect monitoring tooling, human escalation channels, and third-party notices to a central incident registry that can trigger EU and U.S. policy obligations simultaneously.^{Regulation (EU) 2024/1689}
• Pre-build reporting packets. Populate Commission templates with system identifiers, risk classifications, and mitigation plans so legal teams can file within 24 hours.^{Article 73 consultation}
• Establish AI Office liaison roles. Assign policy and technical leads to interface with the European AI Office, coordinate post-incident remediation evidence, and log supervisory feedback.

Transparency logs. Maintain logs of transparency outputs: notices, publications, user communications, research collaborations. Use dashboards to track updates and deadlines. Provide evidence during audits or investigations.

Safety and robustness testing

High-quality testing demonstrates compliance and reduces risk.

Evaluation frameworks. Implement evaluation frameworks aligned with Article 15 (accuracy, robustness, cybersecurity) and NIST AI RMF. Define metrics for functional performance, adversarial robustness, bias, explainability, and environmental impact. Maintain benchmarking datasets with governance controls.

Red teaming. Conduct AI red teaming for high-risk and GPAI systems, simulating misuse scenarios (prompt injection, model extraction, data poisoning). Document methods, findings, and remediation. Align with Executive Order 14110 Section 4 directives on dual-use foundation model red-teaming.

Continuous monitoring. Deploy automated monitoring for drift, bias, and performance degradation. Set thresholds that trigger alerts and retraining. Document decisions, linking to risk management logs and incident response plans.

Independent validation. Engage third-party auditors or accredited bodies to validate conformity assessments. Maintain independence by separating validation teams from development teams. Provide regulators with validation reports during assessments.

Incident response and enforcement readiness

AI incidents range from safety failures to regulatory breaches. Establish structured response playbooks.

Trigger matrix. Define thresholds for incident severity (critical, high, medium, low). Critical incidents include breaches of Article 5 prohibitions, systemic failures of high-risk systems, or national security events. High incidents cover significant harm requiring regulator notification. Map triggers to escalation paths, communication protocols, and regulatory reporting obligations.

Response teams. Assemble cross-functional teams with legal, product, engineering, communications, and policy leads. Assign incident commanders and scribe roles. Maintain contact lists for national competent authorities, the European AI Office, and relevant U.S. agencies.

Investigation steps. Preserve evidence, capture system logs, interview stakeholders, and conduct root cause analysis. Document timeline, decision points, and remediation. Evaluate whether the incident reveals systemic non-compliance requiring updates to risk management or quality systems.

Regulator engagement. Notify regulators within mandated timelines. Provide interim reports, remediation plans, and follow-up updates. Maintain consistent messaging across jurisdictions. Document communications in the regulatory correspondence repository.

Post-incident learning. Run post-mortems with action items, owners, and deadlines. Update training, policies, and technical controls. Share lessons with the AI oversight board and board of directors.

Guide changelog

Policy, compliance, and CAIO teams can reference the changelog to coordinate implementation backlogs with new regulatory guidance.

Last refreshed

23 November 2025 — added a policy research crosslink section and execution plan so teams can reference Zeph Tech’s Colorado SB24-205 runway, EU Data Act application, Delete Act buildout, EU AI Office labelling consultation, and Article 73 incident reporting guidance while presenting this roadmap.^{Policy Briefing — November 14, 2025Policy Briefing — September 12, 2025Policy Briefing — October 6, 2025Policy Briefing — November 5, 2025Policy Briefing — November 7, 2025}

Next planned review

30 April 2026 — align with final Commission implementing acts on reporting portals and the first AI Office supervisory updates.

Metrics and assurance

Define metrics that evidence compliance and guide continuous improvement.

Core metrics.

• Inventory coverage. Percentage of AI systems captured in the registry relative to discovered systems via telemetry or audits.
• Risk assessment timeliness. Ratio of high-risk systems with completed Article 9 risk assessments before deployment.
• Documentation readiness. Percentage of high-risk systems with Annex IV packages updated within the last quarter.
• Incident closure velocity. Average days to close AI incidents, segmented by severity.
• Audit findings remediation. Percentage of audit findings resolved within 60 days.
• Training completion. Proportion of staff completing AI compliance training within required timeframe.
• Regulator engagement SLA. On-time response rate to regulator information requests.

Dashboards and reporting. Present metrics quarterly to the AI oversight board and board committees. Include narrative analysis, trends, and remediation plans. Integrate with enterprise risk dashboards. Publish select metrics externally to build trust (e.g., number of high-risk systems, oversight outcomes).

Independent assurance. Commission periodic internal audits evaluating compliance with EU AI Act obligations, U.S. statutory commitments, and internal policies. Document scope, methodology, findings, and management responses. Share results with regulators when appropriate, and publish anonymised takeaways for industry benchmarking.

Roadmap and calendar

Map out actions aligned with enforcement milestones.

2025 priorities. Eliminate prohibited practices by February 2025. Finalise GPAI transparency documentation by August 2025. Complete CAIO-led inventories and publish updated AI governance policies. Engage with the European AI Office on GPAI systemic risk thresholds.

2026 priorities. Implement conformity assessments for Annex II high-risk systems by August 2026. Prepare notified body interactions, quality management audits, and post-market monitoring infrastructure. Align with U.S. National AI Research Resource pilots and report contributions.

2027 priorities. Ensure all remaining high-risk systems meet obligations by August 2027. Mature continuous monitoring, expand red-teaming, and integrate AI safety metrics into enterprise risk appetite statements. Participate in global standards development through ISO/IEC and NIST. Coordinate with EU-U.S. Trade and Technology Council working groups to anticipate joint conformity assessment pilots.

Calendar integration. Maintain a shared calendar covering EU enforcement dates, U.S. reporting cycles, NIST standards releases, and internal review meetings. Link to policy calendar updates for upcoming votes, consultations, and regulatory guidance.

Appendix: Tools and artefacts

Templates. Provide RAIA templates, Annex IV documentation outlines, CAIO dashboard formats, incident report forms, and regulator notification checklists. Store in the compliance knowledge base with version control.

Standards crosswalk. Maintain crosswalks mapping EU AI Act articles to NIST AI RMF, ISO/IEC 42001, ISO/IEC 23894, and IEEE 7000-series standards. Update as new guidance releases.

Resource library. Curate official legislative texts, European Commission guidance, AI Office Q&As, NIST RMF profiles, and OMB memoranda. Provide summaries and implementation notes. Link to AI workforce enablement resources for training alignment.

Engagement tracker. Track participation in regulatory sandboxes, standards bodies, and advisory committees. Document contributions, feedback received, and follow-up actions. Share insights with advocacy teams.

Continuous improvement. Schedule quarterly retrospectives on AI governance, capturing wins, challenges, and roadmap adjustments. Align with enterprise OKRs and report progress to senior leadership.