Traditional Licensing vs. AI Licensing | From Software to AI Licensing: A Shift in Legal Thinking

4 min 57 sec December 3, 2025 (Edited)

Artificial intelligence (AI) is fundamentally transforming industries, creating the need for AI-specific licensing agreements that go beyond traditional software contracts. Unlike conventional software licenses, which provide a defined product with fixed functionality and govern installation, access, and use under set conditions, AI licensing involves access to machine learning models, algorithms, and datasets that continuously evolve through learning and data inputs.

This dynamic nature introduces unique legal and commercial challenges, including questions of ownership over model improvements and responsibility for unpredictable AI behaviour. High-profile disputes, such as Getty Images v. Stability AI, highlight the significant litigation and regulatory risks associated with unclear AI licensing terms. Consequently, carefully drafted contracts are the primary mechanism for managing compliance, protecting intellectual property (IP), and managing the broader risks inherent in AI deployment.

Data Usage and Ownership

Data is the lifeblood of AI, driving model performance, decision making, and regulatory compliance. Unlike traditional software licenses, AI contracts must clearly define who provides the data, who owns the resulting outputs or derivative models, and which data protection and cross-border transfer laws apply. An example would be the Saudi Personal Data Protection Law (Saudi PDPL), which governs the processing of personal data within the Kingdom of Saudi Arabia (KSA).

Without explicit ownership and usage clauses, vendors may assert rights over refined models or reuse client data, creating significant IP and regulatory risks. Clear contractual terms are therefore essential to safeguard business interests, protect sensitive data, and ensure compliance with regional and international legal frameworks.

In addition to data ownership, the dynamic and evolving nature of AI systems introduces challenges related to performance, accuracy, bias, and fairness. AI contracts must establish acceptable accuracy thresholds, retraining or auditing procedures, and the allocation of responsibilities for addressing bias or harmful outcomes.

Emerging regulatory frameworks, including the EU AI Act and the Saudi Data and Artificial Intelligence Authority’s (SDAIA) AI Principles, reinforce the importance of transparency, accountability, and fairness, making these obligations critical in AI licensing agreements. By embedding these provisions, businesses can better manage operational risks, ensure ethical deployment, and maintain compliance in increasingly regulated AI environments.

Liability and Risk Allocation

Liability in AI licensing is inherently complex due to the unpredictable and adaptive nature of AI systems. Agreements must address responsibility for incorrect or harmful AI decisions, including potential financial, reputational, or moral damages that may arise from such outcomes. Given the shared involvement of developers and users in deploying and operating AI systems, agreements often delineate the allocation of liability to ensure clarity and fairness.

To manage these risks, AI licensing contracts typically incorporate custom indemnities, rigorous testing requirements, and ongoing monitoring obligations. Additionally, because AI models may inadvertently infringe on third-party IP, clearly defining the distribution of responsibility and risk between parties is essential to protect both licensors and licensees from legal exposure and operational uncertainty.

Continuous Learning and Model Updates

Unlike static software, AI models are dynamic systems that continuously learn and adapt based on new data and user interactions. Licensing agreements must clearly define whether the AI model will utilise user-provided data for learning purposes, ensuring that all parties understand how data contributes to the model’s evolution.

Agreements should also outline how updates are implemented, including whether updates occur automatically or require explicit consent from the licensee, to maintain transparency and control over the system’s behaviour.

Furthermore, robust provisions for version control, validation, and audit rights are essential to monitor the AI’s performance, verify compliance with contractual and regulatory obligations, and provide mechanisms for accountability throughout the lifecycle of the model.

AI Licensing in KSA

To make AI licensing agreements resilient and compliant, businesses operating in the KSA should take an AI-specific contractual approach that reflects the unique nature of these systems. Agreements should include clear terms addressing training data, model ownership, and liability for bias or errors, specifying who controls data inputs, model outputs, and derivative works, as well as who is responsible for preventing or correcting harmful outcomes.

Provisions on data ownership, access, and deletion rights should be agreed upon at the outset to minimise IP disputes and ensure compliance with data protection obligations throughout the AI lifecycle.

AI agreements should also align with both international and regional regulatory frameworks, including the EU AI Act, Saudi PDPL, and other applicable Saudi frameworks (such as the SAMA Outsourcing Regulations, where these apply). This means embedding clauses on privacy, cross-border transfers, and automated decision-making, along with transparency and audit obligations — particularly for high-risk AI applications.

Under the Saudi PDPL, AI systems must follow rules on consent, purpose limitation, and cross-border data transfers. Contracts should clearly define the parties’ roles (controller vs. processor) and outline their PDPL responsibilities. Local data hosting is recommended to enhance compliance. Even foreign companies that process the personal data of Saudi residents are subject to these requirements if they target individuals in the KSA.

Finally, agreements should include exit and continuity strategies, covering data return, secure deletion, and model escrow arrangements. These safeguards help to avoid vendor lock-in, maintain business continuity, and ensure ongoing control over AI assets after termination. Together, such measures create a strong legal and operational foundation for trustworthy, compliant, and sustainable AI deployment in the KSA.

Conclusion

The key difference between software and AI licensing lies not in form, but in flexibility. AI is a living system, and its contracts must evolve accordingly. AI licensing is a new legal discipline, not just an evolution of software contracts. It must address dynamic, data-driven systems that learn, evolve, and create legal risks. Companies that adopt AI-ready contractual frameworks aligned with regulatory trends will gain a compliant, competitive edge in the KSA and beyond.

Practical Steps for AI-Ready Contracting

1. Include AI-specific clauses on training data, model ownership, and bias liability.
2. Negotiate data ownership and deletion rights upfront.
3. Align with global and regional regulations (EU AI Act, Saudi PDPL, SDAIA AI Principles).
4. Embed transparency, ethics, and audit rights for high-risk use cases.
5. Plan for exit strategies (data return, deletion, model escrow).