AI Legal Risks to Watch in 2026 - And How to Protect Your Business
AI is now baked into day-to-day operations. That creates legal exposure most teams underestimate. Here are the five risks counsel should prioritise in 2026 - and the controls that actually move the needle.
1) Unclear ownership and copyright in AI outputs
AI-generated content can lean on copyrighted material without obvious signals. Ownership of outputs is often ambiguous, which invites disputes over who can use, modify, or sell them. A recent example: Getty Images v. Stability AI. In UK proceedings, Getty's main copyright case did not succeed, while the court found limited trade mark infringement tied to early outputs reproducing Getty's watermark.
- Check tool licences and ToS. Secure vendor warranties on training data provenance and IP indemnities.
- Define IP ownership in contracts, employment agreements, and SOWs. Cover assignment, licensing, and moral rights waivers where applicable.
- Introduce IP reviews: plagiarism checks, reverse-image search, watermark detection, and similarity screening before publication.
- Keep an audit trail of prompts, model settings, and reviewers. Require sign-off for commercial use.
- Avoid commercial deployment where data sources are opaque or outputs look derivative.
2) AI "hallucinations" driving bad decisions
Roughly 1 in 5 AI outputs contain major accuracy issues - fabricated facts, wrong citations, stale law. That can fuel misrepresentation and negligence claims, and even fines up to €7.5 million (£6.5M) for false or misleading submissions to authorities. In March 2024, a Microsoft-powered "MyCity" chatbot reportedly gave advice that could have pushed employers to break the law, including on tips and harassment complaints.
- Make human review mandatory for legal, HR, finance, compliance, and product claims. No unsupervised publishing.
- Require citations and date-stamps on outputs. Challenge unsupported claims by default.
- Disclose AI involvement to end users where relevant. Don't position AI as a final authority.
- Gate high-stakes use cases behind SME approval and documented checks. Log decisions and retain evidence.
- Test deployed assistants regularly for error patterns. Disable risky prompt paths and add guardrails.
3) Lack of internal AI governance
Without clear rules, employees paste sensitive data into public tools, deploy unvetted assistants, and ship unreviewed content. That's a short path to data leaks and lawsuits.
- Publish a company-wide AI policy: allowed use cases, banned data types, review steps, and escalation paths.
- Assign ownership: product/legal/data security leads with explicit approval thresholds (RACI helps).
- Procurement intake for AI vendors: security review, DPA, data location/retention, training data disclosures, audit rights.
- Train staff on safe prompts, data handling, and red-flag outputs. Create playbooks and a fast incident process.
- Track models and assistants in a central registry with risk ratings and periodic reviews.
4) Data privacy violations
AI systems often process personal data from customers, employees, and third parties. Using data without a lawful basis, transparency, or minimisation can trigger fines and reputational damage.
- Limit inputs to what's necessary. Document lawful basis and conduct DPIAs for material use cases.
- Prefer anonymisation or strong pseudonymisation. Ban sensitive data entry into public tools.
- Update privacy notices to cover AI use, sharing, retention, and user rights. Prove it with records.
- Assess cross-border transfers, SCCs, and vendor subprocessors. Add AI-specific clauses to DPAs.
- Log queries and outputs that touch personal data; set retention and deletion schedules.
- Helpful reference: UK ICO's guidance on AI and data protection - ico.org.uk
5) Shifting AI regulations and compliance risk
Laws are moving fast and vary by jurisdiction. The EU AI Act and the Data (Use and Access) Act 2025 (DUA) create fresh duties and enforcement risk. Some obligations can apply to systems already in use.
- Maintain an inventory of all AI systems, use cases, data types, users, and jurisdictions.
- Run gap assessments against applicable rules (e.g., EU AI Act: risk management, data governance, transparency, human oversight, logging, post-market monitoring).
- Schedule periodic audits and model-change reviews. Build flexibility into processes so you can adjust quickly.
- Appoint an AI compliance lead, brief the board, and budget for remediation and monitoring.
- Overview: EU AI Act explainer - European Parliament
Contract clause toolkit for counsel
Bake protections into your paper so risk isn't left to policy alone.
- Disclosure: Vendor must state whether it uses AI, where, and how (including training data sources and model providers).
- Ownership: Define who owns outputs, licences, and derivative rights. Restrict training on your data without consent.
- IP and privacy: Warranties on non-infringement and lawful data use; indemnities for IP, privacy, and regulatory breaches.
- Quality and oversight: Accuracy commitments for critical use; human review obligations; audit logs; right to audit.
- Security and incidents: Controls, testing, vulnerability remediation timelines, breach notification, and data deletion.
Quick-start checklist
- Inventory AI tools and use cases across the business.
- Publish a clear AI policy and roll out staff training.
- Stand up human-in-the-loop review for high-risk outputs.
- Add IP screens and plagiarism checks to the content workflow.
- Run DPIAs and update privacy notices for AI use.
- Update MSAs/DPAs with AI clauses, warranties, and indemnities.
- Set up logging, audit trails, and incident playbooks.
- Track regulatory changes and schedule compliance audits.
If your team needs structured enablement on AI governance and risk controls, explore practical programs here: AI courses by job.
Treat AI like a high-risk vendor: control inputs, review outputs, document everything, and keep contracts tight. Do that now, and you'll reduce disputes, avoid fines, and keep AI working for the business - not against it.
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