KINGSLEY WOOD INSIGHTS
AI Systems – Legal and Commercial Risks, and How to Manage Them
Tim Carswell • August 13, 2026
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Artificial intelligence is now central to digital transformation strategies across almost every sector. Organisations are increasingly integrating AI into core business functions, from decision-making and customer engagement to operations and risk management. However, the pace of adoption has outstripped the development of robust controls, leaving organisations exposed to a rapidly evolving landscape of legal, regulatory and commercial risk.
To effectively identify and mitigate these risks, organisations should focus on five key areas when deploying AI systems: regulation, governance, data privacy, intellectual property, and commercial contracts.
Regulatory Landscape – UK and EU
The UK has adopted a decentralised, principles-based approach to AI regulation. Rather than introducing a single AI-specific statute, sectoral regulators such as the ICO, FCA, CMA, and Ofcom are issuing guidance aligned with five core principles: safety, transparency, fairness, accountability, and contestability.
This flexible approach allows innovation but places the burden on organisations to interpret and apply these principles in practice. Increasingly, regulators expect evidence of risk assessments, explainability mechanisms, human oversight and ongoing monitoring.
In contrast, the EU AI Act introduces a comprehensive, rules-based framework with extraterritorial reach. Organisations operating in or supplying into the EU must comply with strict requirements, particularly for high-risk AI systems. These include conformity assessments, technical documentation, data governance standards and post-market monitoring. The divergence between UK and EU regimes creates additional complexity for cross-border businesses.
Governance
Effective governance is critical to ensuring AI systems operate safely, ethically and in line with regulatory expectations. Poor governance can lead to legal liability, reputational damage and operational failure.
Key risks include unclear accountability, lack of board oversight, ethical concerns such as bias and model drift where system performance deteriorates over time.
Organisations should implement structured governance frameworks, including clear ownership of AI systems, regular model validation and testing, ethical review processes and defined escalation procedures. Embedding governance at both operational and board level is increasingly seen as a regulatory expectation rather than best practice.
Data Privacy and GDPR
AI systems are inherently data-driven, creating significant exposure under the UK GDPR and Data Protection Act 2018. Many AI use cases involve large-scale data processing, profiling, and automated decision-making, all of which carry heightened regulatory scrutiny.
Key risks include processing personal data without a valid legal basis, failing to provide adequate transparency, and generating biased or discriminatory outcomes. Generative AI tools also introduce risks around data leakage, particularly where sensitive or confidential information is input into external systems.
To mitigate these risks, organisations should conduct Data Protection Impact Assessments (DPIAs), implement data minimisation principles and ensure clear and accessible privacy notices. Robust technical and organisational measures should be in place to safeguard data, alongside thorough due diligence on third-party providers.
Intellectual Property
AI raises complex and evolving intellectual property issues. Organisations must consider both the inputs used to train AI systems and the outputs they generate.
Key risks include using datasets without proper licensing, generating outputs that infringe third-party rights and uncertainty over ownership of AI-generated content. In many jurisdictions, copyright protection may not apply to purely AI-generated works without sufficient human input.
Mitigation strategies include auditing training data for provenance and licensing rights, clearly defining ownership of outputs in contracts, and restricting the reuse of data by AI vendors. Organisations should also implement internal policies governing the use of public AI tools to protect confidential information.
Commercial Agreements
Robust contractual frameworks are essential to allocate risk and ensure accountability in AI deployments. Many organisations rely on third-party vendors for AI solutions, making contractual protections critical.
Key risks include unclear liability for errors or system failures, lack of transparency into how AI models operate and vendor lock-in due to limited portability.
Contracts should include clear performance standards, warranties and service levels. Organisations should seek transparency obligations, data protection safeguards, audit rights and indemnities covering intellectual property and regulatory breaches. Exit provisions are also important to ensure flexibility if systems underperform or regulatory requirements change.
Practical Steps for Organisations
In addition to legal compliance, organisations should take practical steps to embed responsible AI use across their operations. This includes training staff on AI risks, establishing internal policies for AI usage and regularly reviewing systems to ensure ongoing compliance.
Organisations should also monitor regulatory developments, as the legal landscape is evolving rapidly. Proactive engagement with legal and compliance teams can help identify risks early and avoid costly issues later.
How Kingsley Wood Can Help
Kingsley Wood’s AI team combines expertise across commercial, regulatory and dispute resolution matters to support organisations navigating AI-related risks. We provide tailored advice on governance frameworks, regulatory compliance, contract structuring and risk mitigation.
We also offer a complimentary AI Audit to help organisations identify key risks, assess current controls and develop practical strategies for safe and effective AI deployment.
ABOUT THE AUTHOR
Tim Carswell
PARTNER | COMMERCIAL & AI
Tim is a solicitor with many years’ experience advising businesses of all sizes, from start-ups to global corporations, on a diverse range of commercial and corporate issues.
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