Protecting IP and Trade Secrets in AI Development: Contracts and Technical Safeguards for Cross‑Border Collaborations

Introduction

Collaborative AI projects commonly involve sharing models, training datasets, code and operational know-how across organisations and borders. Protecting intellectual property and trade secrets in that environment demands a combination of careful contractual drafting and technical safeguards. This article outlines pragmatic measures lawyers and in-house teams should adopt when negotiating AI development collaborations, with particular attention to enforceability and operational feasibility for parties in Türkiye.

Understand what needs protection

AI development assets fall into distinct categories: source code and model weights; training datasets and data pipelines; hyperparameter settings and model development logs; documentation and evaluation metrics; and operational deployment configurations. Some of these may qualify as trade secrets if they derive commercial value from secrecy and reasonable secrecy measures are in place.

Contractual architecture

Contracts should address ownership, licensing, confidentiality, access and post-termination rights. Key contractual elements include:

• IP ownership and background IP – define background IP and foreground IP. Decide whether models are owned outright by the commissioning party, jointly owned, or licensed with defined scopes.
• License scope and restrictions – where ownership transfers are impractical, grant exclusive or non-exclusive licenses tailored by field, geography and duration. Restrict sublicensing and downstream transfers where necessary.
• Confidentiality and trade-secret clauses – robust NDAs that specify technical and organisational measures required to maintain secrecy; define permitted disclosures and carve-outs for required regulatory reporting.
• Source code escrow and reproducibility – consider escrow arrangements for critical source code or model checkpoints, with clear release conditions tied to vendor insolvency or failure to meet obligations.
• Audit and verification rights – allow periodic audits of security and access controls, subject to reasonable confidentiality protections.
• Injunctive relief and remedies – specify injunctive relief as an available remedy for misappropriation, and align monetary damages regimes with commercial risk tolerances.

Technical and operational safeguards

Contractual promises must be backed by operational controls. Practical technical measures include:

• Role-based access control and least-privilege principles for model and dataset repositories;
• Encrypted storage of model weights and datasets, with key management separated from development hosts;
• Use of watermarking or fingerprinting techniques in model outputs or parameters to trace unauthorised use;
• Audit logging with tamper-evident records for access and model training runs;
• Controlled compute enclaves for sensitive training where data cannot be exported.

Cross-border and data-transfer considerations

When collaborations span jurisdictions, additional constraints arise from data protection rules and export controls. For Türkiye-based datasets, ensure compliance with KVKK regarding transfers and sensitive personal data. Where models are trained on personal data, contractual terms should address lawful basis, consent where required, and obligations to support data-subject rights. In some cases, anonymisation or federated learning architectures can reduce cross-border transfer risks.

Evidence preservation and enforcement

To enforce trade-secret protections, parties must demonstrate reasonable steps taken to maintain confidentiality. Practical steps include documenting security policies, maintaining access logs, and issuing internal directives limiting disclosure. Preserve forensic evidence promptly if misappropriation is suspected: snapshots of repositories, access records and system logs are critical.

Negotiation tips for counsel

1. Prioritise clarity on ownership early; ambiguity is the chief source of later disputes.
2. Match legal remedies to technical reality: if technical segregation isn’t feasible, consider licensing rather than ownership transfer.
3. Insist on minimum security baselines and measurable SLAs; avoid vague promises.
4. Plan for the end of collaboration: specify return or secure deletion of datasets and models, and post-termination use limits.

Conclusion

Protecting IP and trade secrets in AI development requires coordinated contractual and technical measures. For Türkiye-based parties collaborating internationally, the combination of robust NDAs, clear IP allocation, escrow mechanisms, and concrete security controls maximises enforceability. Av. Burak Şahin and Şahin Hukuk assist clients in designing negotiating strategies and technical specifications that align legal protections with operational constraints.