Policy to Playbook: Operationalization of the EU GPAI Code of Practice

The EU’s new Code of Practice for General-Purpose AI (GPAI) changes the game for Cyber-GRC teams. Published in July 2025 as a practical instrument to help organizations meet the AI Act’s GPAI obligations, the Code focuses on transparency, safety & security, and copyright, and it will strongly influence what regulators expect from providers and users of large, general-purpose models.

Below I turn that policy into a runnable playbook — what Cyber-GRC teams must own now, and a concrete 90-day sprint to get audit-ready.

📌Why this matters now

• The Code of Practice was published by the European Commission in July 2025 as a voluntary industry tool to help comply with the AI Act’s GPAI provisions.
• Core GPAI obligations under the AI Act take effect 2 August 2025.
• The Commission also released a mandatory training-data summary template to standardise how providers describe the data used to train GPAI models; the template and disclosure requirement are part of the implementation steps announced in July 2025.

These developments mean cyber security and GRC teams must deliver reproducible artefacts (model cards, training-data summaries, adversarial test records, incident workflows) that regulators and auditors can verify.

📌Five operational controls Cyber-GRC must own (immediately)

1. Model documentation & inventory
   Maintain a central catalogue of every GPAI model in use (internal, vendor, modified/open models), with a living model card, version history, and deployment context (business function, data flows, exposure). This is the single source of truth for audits and investigations.
2. Training-data summaries & provenance
   Use the Commission’s summary template (or an aligned internal template) to capture what training data was used, how it was sourced, and any copyright or licensing checks performed. Track modifications and fine-tuning separately.
3. Security-by-design controls & adversarial testing
   Define baseline security controls (authentication, access control, monitoring, rate-limits, supply-chain checks) and run regular adversarial tests / red-team exercises focused on model manipulation, prompt attacks, and data poisoning.
4. AI incident & escalation playbook
   Extend incident response to cover AI-specific incidents (e.g., model hallucinations with safety implications, copyright infringement claims, exfiltration via model outputs). Define severity thresholds, reporting lines (legal / PR / regulator), and regulatory reporting timelines.
5. KRI/KPI & audit evidence
   Publish measurable KRIs (e.g., % of models with up-to-date model cards, frequency of adversarial tests, time to revoke/patch model endpoints, % of models with completed copyright/IP risk assessment) and ensure evidence is exportable for regulators and auditors. Where possible automate the measurement and bind evidence to control owners

📌Example risk-register entries (short + measurable KRIs)

• Risk: Unauthorized data leakage from model outputs.
  Controls: Output filtering, usage logging, access controls, prompt sanitisation.
  KRI: % of flagged outputs detected by filter; time to revoke model endpoint access.
• Risk: Copyright exposure from training data.
  Controls: Training-data inventory, licensing checks, legal review.
  KRI: % of training datasets with completed copyright assessment; # outstanding issues.
• Risk: Model drift causing safety failures.
  Controls: Drift monitoring, scheduled retraining governance, rollback procedures.
  KRI: Drift metric threshold breaches per month; MTTR (hours) to rollback.

📌90-Day sprint to stand up AI Governance Checklist (😎specially prepared for you😎)

Day 0–14: Rapid discovery

• Inventory all LLM/GPAI usage (vendors, open-source forks, in-house).
• Prioritise models by exposure & business criticality.

Day 15–45: Baselines & artefacts

• Create model-card and training-data summary templates (use the Commission template as a reference).
• Map controls to the AI Act obligations (transparency, safety/security, copyright).

Day 46–70: Testing & playbooks

• Run an initial adversarial test and one tabletop AI incident exercise.
• Finalise escalation path and regulatory reporting checklist.

Day 71–90: Evidence & handover

• Produce an audit pack for top 3 critical models (model card, training-data summary, test reports, incident playbook).
• Train SOC, legal, procurement, and DR teams on the new playbooks.

📌Organization & RACI: who owns what

• GRC (owner): model documentation, compliance evidence, KRI reporting.
• InfoSec / SOC (owner): runtime protection, detection, adversarial testing cadence.
• Data Science / MLOps (owner): model lifecycle, retraining, technical fixes.
• Legal / IP (owner): copyright checks, licensing decisions, regulatory communications.
• Procurement (owner): vendor attestations, contractual clauses for AI models.

Cross-functional governance boards are useful, but operational ownership must be clear. Weekly or fortnightly syncs turn policy into practice.

📌What “Good” looks like in 6 months

• All critical GPAI models have living model cards and training-data summaries.
• Regular adversarial tests and a rehearsed AI incident response.
• Measurable KRIs feeding a monthly AI risk digest for executives and the board.
• Contractual clauses that require vendors to provide model transparency and security attestations.

📌Final thought

The EU’s GPAI Code and the AI Act don’t merely add paperwork — they raise the bar for what “reasonable” AI risk management looks like. Cyber-GRC teams that move today from policies to reproducible artefacts (model cards, training-data summaries, adversarial test records, incident playbooks) will not only reduce regulatory risk — they’ll build trustworthy, resilient AI operations.

Comments and experiences welcome — what’s the biggest AI risk you’ve uncovered in your organisation so far?

References & Citations

Below are the key sources referenced in this article and the official links to access the full EU General-Purpose AI (GPAI) Code of Practice / guidance and related materials — with a short note on what you’ll find at each resource.

1. Official Code of Practice (European Commission — Code page) — Full Code of Practice text and context (structure, chapters, purpose). Digital Strategy EU
2. Commission press release — “General-Purpose AI Code of Practice now available” — Official announcement and summary of timing (GPAI rules entry into application). European Commission
3. Training-data summary template & explanatory notice (downloads: PDF / DOC) — The Commission’s public template and explanatory note for summarising the data used to train GPAI models (essential for compliance artefacts). Digital Strategy EU+1

BSA / industry commentary — analysis of practical challenges and timelines for providers.
Summary: Industry associations’ viewpoints on the timeframes and practicalities of implementing the training-data template and other requirements — useful to understand sector concerns and operational trade-offs. BSA

European Commission — “The General-Purpose AI Code of Practice” (Digital Strategy / AI Office).
Summary: The official Code of Practice (published July 2025) describing the three chapters (Transparency, Copyright, Safety & Security) and providing model documentation guidance and tools to help comply with the AI Act. Digital Strategy EU

European Commission press release — “General-Purpose AI Code of Practice now available” (Commission Press Corner, July 2025).
Summary: Commission announcement confirming publication of the Code and noting the AI Act GPAI rules will enter into application on 2 August 2025; explains intent and next steps. European CommissionDigital Strategy EU

Commission news — “Commission presents template for General-Purpose AI model providers to summarise the data used to train their model.”
Summary: Announces the Commission’s standardized training-data summary template, the practical format GPAI providers should use to disclose training data information. Essential for GRC teams building templates and processes. Digital Strategy EU

WilmerHale briefing — “European Commission Releases Mandatory Template for Public Disclosure of AI Training Data.”
Summary: Legal analysis of the training-data summary requirement, its effective date (Aug 2, 2025), and transitional arrangements for models already on the market — useful context for compliance timelines. WilmerHale

Mayer Brown / Crowell / Skadden briefings (legal firms) — various summaries of the Code and compliance timeline.
Summary: Practical legal guidance explaining the compliance timeline (obligations apply from Aug 2, 2025; enforcement powers phased in later), and breakdowns of the transparency, safety and copyright chapters. These are helpful to translate policy into obligations for operations. Mayer BrownCrowell & Moring – HomeSkadden

News coverage — Financial Times / AP / ITPro reporting on signatories and industry reactions.
Summary: Reporting on which major providers are engaging with (or resisting) the Code, and broader industry reaction — useful for procurement and vendor-risk conversations. Financial TimesAP NewsIT Pro