Decoding the EU General Purpose AI Code of Practice: What It Means for Risk, Compliance, and Responsible Innovation

On July 9, 2025, the European Commission released the final version of the General-Purpose AI (GPAI) Code of Practice. This framework is voluntary but highly influential. It is designed to help AI model developers, deployers, and integrators align with the EU AI Act, which will apply to GPAI models starting August 2, 2025.

For our clients at Socium Security, especially mid-market software and AI providers who serve Fortune 500 enterprises, this Code represents both a compliance signal and a product assurance roadmap.

In this article, we will break down the Code’s key components, how they align with leading frameworks (NIST AI RMF, ISO/IEC 42001, ISO 31000), and what risk leaders, CISOs, and GRC professionals must do now.

Understanding the EU GPAI Code of Practice

The Code of Practice is a non-binding, voluntary instrument. It was created in collaboration with major GPAI providers, the European Commission, and independent experts. It serves as a “safe harbor”—those who align with the Code are presumed to be in compliance with relevant sections of the AI Act.

This document isn't limited to generative AI; it encompasses any model that could be widely deployed and adapted across various contexts (e.g., foundation models).

Five Core Areas of the Code

1. Transparency and Model Documentation

• Providers must disclose:

• Model architecture

• Training methodologies

• Data provenance (if possible)

• Intended and high-risk use cases

• Evaluation methods and known limitations



💡 Why it matters: This aligns with ISO/IEC 42001’s documentation requirements and emphasizes explainability and transparency per NIST AI RMF. Enterprises expect this information in vendor assessments and third-party reviews.



2. Copyright and Data Governance

• The Code mandates:

• Respect for rights holders opting out of AI training (per the EU Copyright Directive)

• Proof of lawful data sourcing

• Processes for downstream IP risk mitigation



💡 Why it matters: This is a significant shift in how training datasets must be collected and validated. Organizations need to strengthen legal review and data governance controls over training pipelines and model fine-tuning workflows.



3. Systemic Risk Management

• For GPAI models that may present societal or systemic risk, providers must:

• Conduct ongoing risk assessments

• Perform adversarial and safety testing

• Report incidents and enable traceability



💡 Why it matters: This mirrors NIST AI RMF’s “Govern” and “Map” functions, along with ISO 31000’s risk treatment lifecycle. GRC teams need to create AI-specific risk registers and incident escalation paths.



4. Security and Model Integrity

• Model providers should implement:

• Red-teaming and penetration testing against prompt injection and misuse

• Abuse prevention mechanisms

• Secure model release and monitoring practices



💡 Why it matters: These requirements borrow from secure software development lifecycle (SDLC) and offensive security disciplines. This combines AI safety with traditional information security operations and will directly impact SOC and DevSecOps playbooks.



5. Voluntary Today, But Binding in Practice

• While the Code is voluntary, adherence:

• Provides a legal presumption of compliance under the AI Act

• Will likely be requested by large enterprise buyers as a procurement condition

• Signals maturity and commitment to responsible AI



💡 Why it matters: For AI builders targeting regulated or European markets, Code alignment is essential. Many organizations will incorporate it into their third-party AI risk checklists.

Enforcement Timeline

Date	Requirement
Aug 2, 2025	AI Act obligations for GPAI models become enforceable
Aug 2, 2027	Transition period ends for existing GPAI models
2025–2026	Increasing enterprise pressure for Code alignment

Socium Security’s Perspective

At Socium Security, we believe that the GPAI Code of Practice serves as a practical blueprint for trustworthy AI operations. For our clients, especially those in regulated industries or delivering AI-enabled capabilities to Fortune 100 companies, the Code provides clarity on several aspects:

• What to document, disclose, and validate

• What controls auditors and procurement teams will expect

• How to balance innovation with governance

  
  

Next Steps for GRC and Security Leaders

1. Assess Model Exposure

• Inventory which internal and third-party models fall under the GPAI scope.




2. Perform a Readiness Gap Assessment

• Compare current controls to the Code’s five areas using ISO 42001, ISO 27017, and NIST AI RMF mappings.




3. Update AI Governance Policies

• Codify transparency, data usage, and incident response protocols for AI systems.




4. Engage Key Stakeholders

• Involve product, engineering, and legal early to ensure alignment across the AI lifecycle.




5. Educate and Influence Upstream

• Ask vendors about their Code alignment. Expect this to become a key factor in security reviews and contracts.

Final Thoughts

The EU’s General-Purpose AI Code of Practice may be voluntary, but its influence is crucial. For organizations operating in the AI space or embedding AI into their products, it is a vital signal of trust, risk awareness, and market readiness.

Socium Security can help you operationalize these expectations. We can prepare your AI practices for future challenges.

Let’s Talk.

Whether you’re conducting a readiness assessment, preparing for procurement reviews, or integrating AI risk into your security program, Socium Security is here to guide the way.

📩 Contact us to schedule a session with one of our AI governance advisors.