AI-Related Disputes and Arbitration Clauses: Legal Guide for Technology Contracts

Artificial intelligence contracts are often negotiated with close attention to price, functionality, data protection, intellectual property and limitation of liability.

But one question is frequently left too late: what happens if the AI system fails, misuses data, generates harmful output, infringes intellectual property, exposes confidential information, produces discriminatory results, breaches service levels or becomes central to a regulatory investigation?

At that point, the contract is no longer only a technology agreement. It becomes the legal map for a dispute.

AI-related disputes are different from ordinary software disputes. They may involve technical uncertainty, probabilistic outputs, black-box systems, model updates, training data, prompts, logs, vendor dependency, cross-border data flows, intellectual property claims, regulatory exposure and urgent confidentiality risks. A standard dispute resolution clause may not be enough.

For companies developing, procuring, investing in or deploying AI systems, the dispute clause should be designed with the same care as the data protection, IP and liability clauses. This guide explains how AI-related disputes arise, why arbitration may be useful in certain AI and technology contracts, and how dispute resolution clauses can be structured to protect commercial, technical and legal interests.

1. AI Contracts Need Dispute Architecture

Many contracts contain a dispute resolution clause at the end, often copied from an old template. For AI contracts, this is dangerous. The dispute clause should not be treated as boilerplate; it should reflect the risk profile of the AI system.

An AI contract may require different mechanisms for different types of dispute: a technical performance dispute; a data misuse dispute; a confidentiality breach; an IP infringement; a service-level failure; a model output harm; a regulatory cooperation failure; termination and data return; a payment dispute; an emergency injunction; expert determination; mediation; arbitration; and court relief for urgent matters. A single generic clause may not manage all of these properly.

The better approach is to design a dispute architecture: a structured system for identifying which dispute goes where, how quickly, under what rules and with what interim protections.

2. Why AI Disputes Are Different

AI disputes are different because AI systems do not always operate like traditional deterministic software. A conventional software dispute may involve whether the system met a specification.

An AI dispute may instead involve whether the model output was reliable enough; whether the model was trained on lawful data; whether the vendor used customer data for model improvement; whether prompts and outputs were retained; whether an output infringed third-party IP; whether the AI system produced biased results; whether human oversight was required; whether the customer misused the system; whether the vendor's disclaimers were sufficient; whether the model changed after deployment; whether logs can prove what happened; and whether the system complied with regulatory expectations.

AI systems may evolve, update or behave differently across contexts. The dispute may require both legal interpretation and technical explanation. That is why AI contracts should anticipate evidentiary, technical and procedural complexity.

3. The Main Types of AI-Related Disputes

AI-related disputes may arise in many forms. Common categories include vendor performance disputes, data protection disputes, training data disputes, confidentiality breaches and trade secret exposure; IP ownership disputes, copyright or trademark infringement and AI-generated code disputes; service availability disputes, API failures and model update disputes; inaccurate output claims, hallucination-related harm and discrimination or bias claims; consumer-facing AI complaints, employment AI disputes and cyber incidents involving AI tools; termination and data deletion disputes, regulatory cooperation disputes and AI due diligence or investment misrepresentation claims; and disputes over warranties and indemnities.

Each category may require a different procedural response. A dispute over model performance may be suitable for expert determination before arbitration; a confidentiality breach may require urgent interim relief; an IP infringement claim may require rapid takedown and indemnity analysis; and a data breach may require immediate regulatory notification and forensic investigation. The dispute clause should be capable of handling these differences.

4. AI Vendor Disputes

AI vendor disputes may arise where a company buys or subscribes to an AI tool and later claims that the vendor failed to deliver what was promised. The dispute may concern model accuracy, uptime, API availability, integration failures and output quality; security, regulatory compliance, data processing, response time and documentation; or support, model changes, discontinued features, excessive downtime and failure to meet enterprise commitments.

The first issue is usually contractual: what did the vendor actually promise? AI vendors often include broad disclaimers, stating that outputs are not guaranteed, that the system may be inaccurate, that customer review is required and that the tool should not be used for certain high-risk purposes. A customer may argue that the vendor's sales materials, service descriptions, technical documentation or negotiated commitments created stronger obligations.

Vendor disputes therefore require careful review of the entire contractual record, including the master services agreement, the order form and the service description; the data processing agreement, the acceptable use policy and the security documentation; and sales representations, technical specifications, emails, implementation plans, support tickets and product updates. AI vendor disputes often turn on the gap between marketing language and contractual obligation — the same gap that careful AI vendor and procurement contracts are designed to close before a dispute begins.

5. Customer Misuse and Acceptable Use Disputes

AI vendors may allege that the customer misused the system. This may include using the AI tool for prohibited purposes; uploading unlawful data; using the system in a regulated sector without approval; relying on outputs without human review; using the tool for employment, credit or health-related decisions; violating acceptable use policies; reverse engineering, scraping or exceeding API limits; attempting to bypass safety measures; or integrating the AI system into an unauthorised product.

The customer may respond that the restrictions were unclear, hidden in online terms, changed after signing or inconsistent with the vendor's sales representations. This creates an important drafting issue. Acceptable use policies should be clear, incorporated properly and aligned with the actual business model. If a vendor sells an AI tool for enterprise use, it should not later rely on vague terms to deny responsibility for foreseeable uses; and if a customer intends to use AI in a sensitive context, it should confirm that the use is permitted before deployment.

6. Data Use and Training Disputes

One of the most serious AI disputes concerns the use of customer data. A customer may allege that the vendor used its data for model training, fine-tuning, service improvement, analytics, product development, benchmarking, debugging, sharing with subprocessors or generating outputs for other users. The data at stake may include confidential documents, personal data, trade secrets, source code, financial information, client materials, employee data, legal documents, customer databases and product data.

The dispute may turn on precise contractual language. Important questions include whether the vendor had the right to use customer data for training; whether training use was opt-in or opt-out; whether the customer was properly informed; whether the data processing agreement restricted use; whether prompts and outputs were treated differently; whether enterprise settings disabled training; whether subprocessors were permitted; whether personal data was transferred abroad; whether deletion was possible; and whether the vendor can prove what happened. AI data disputes are often evidence-heavy, with logs, platform settings, product documentation, data flow diagrams and vendor records becoming central.

7. Confidentiality and Trade Secret Disputes

AI systems create confidentiality risk because users may input sensitive information into tools they do not control. Disputes may arise where employees upload confidential documents; vendor personnel access customer prompts; prompts are retained longer than expected; trade secrets are exposed through outputs; customer data is used to improve a shared model; unauthorised users access AI records; AI integrations leak information; a vendor breach exposes customer material; or outputs reveal information derived from confidential inputs.

In these disputes, timing is critical. The customer may need urgent relief to stop further use of data, require deletion, prevent disclosure, suspend processing, obtain forensic evidence, preserve logs, notify affected parties and protect trade secrets. A contract should allow emergency relief where confidentiality is at risk. Arbitration can handle damages, but urgent protection may require emergency arbitration or court measures depending on the clause and jurisdiction — and where the exposure follows a security incident, it should be managed alongside a proper cybersecurity and incident response plan.

8. Intellectual Property Disputes

AI-related IP disputes may arise over both inputs and outputs. Input disputes may concern copyrighted training data, licensed datasets, scraped website content, third-party code, images, music, text, confidential databases or proprietary documents. Output disputes may concern AI-generated text, images, logos, code, design concepts, translations, reports, product descriptions, marketing content or software components.

Legal questions may include who owns the AI-generated output; whether the output can be protected; whether the vendor assigns rights; whether the customer has commercial use rights; whether the output infringes third-party rights; whether the customer prompted the system to imitate protected works; whether an IP indemnity applies; whether open-source obligations are triggered; whether exclusions are hidden in the terms; and what happens if a third-party claim is made. AI IP disputes can be difficult because the factual chain may be complex, requiring technical evidence about model behaviour, training data, similarity, prompts, outputs, human modification and commercial use. A dispute clause for an intellectual property, media and technology matter should therefore allow expert evidence and strong confidentiality protection.

9. AI-Generated Code Disputes

AI-generated code deserves separate attention. Software teams may use AI tools to generate or assist with code, and disputes may arise where AI-generated code contains open-source elements; licence obligations are triggered; code is insecure; code resembles third-party code; ownership is unclear; customer contracts prohibit AI-assisted development; confidential source code was uploaded to a tool; vulnerabilities cause damage; development milestones are missed; or the vendor denies responsibility for AI-assisted work.

For software companies, this can become a serious contractual and IP issue. The contract should specify whether AI-assisted development is permitted; whether disclosure is required; who owns AI-assisted code; whether open-source scanning is required; whether security testing is mandatory; whether warranties cover AI-generated components; and whether customer consent is needed. AI coding disputes are likely to grow as AI-assisted development becomes normal.

10. Output Liability and Reliance

AI outputs may be wrong. They may be inaccurate, incomplete, misleading, biased, outdated or unsuitable for the intended purpose. Disputes may arise where an output causes financial loss, a wrong business decision or a customer complaint; a professional negligence claim, a regulatory breach or an employment claim; a discriminatory outcome, a defective product or a misleading consumer communication; incorrect medical, financial or legal guidance; or reputational damage.

The legal issue is responsibility. The vendor may argue that outputs are not guaranteed and must be reviewed by humans; the customer may argue that the vendor sold the tool for a specific purpose and should be responsible for foreseeable use; and the end user may argue that both vendor and deployer contributed to the harm. A strong contract should define permitted use, prohibited use, human review requirements, output disclaimers, liability allocation, indemnities, documentation duties, audit logs, an escalation process and customer-facing disclosures. AI output disputes often depend on whether the system was used as a decision-support tool or as a substitute for human judgment.

11. Bias, Discrimination and High-Impact Decisions

AI disputes may arise where systems affect individuals — in recruitment screening, employee monitoring, credit assessment, insurance underwriting, fraud detection, education assessment, healthcare triage, housing eligibility, consumer profiling, pricing or account suspension. Claims may involve bias, discrimination, lack of transparency, unfair treatment, data misuse or the inability to challenge an automated decision.

In these cases, arbitration clauses must be approached carefully. Some disputes may involve individual rights, consumers, employees or mandatory statutory protections that cannot be fully removed from court or regulatory jurisdiction. Companies should not assume every AI-related dispute can safely be sent to arbitration: for B2B AI contracts arbitration may be appropriate, but for consumer, employment or rights-sensitive disputes additional legal review is needed, and the broader AI law and governance framework will shape what can lawfully be agreed.

12. Regulatory Cooperation Disputes

AI systems may attract regulatory attention. A customer may need vendor cooperation to respond to data protection authority requests, AI regulatory inquiries, cybersecurity incidents, consumer authority investigations, sector regulator reviews, audit requests, government information demands, litigation disclosure or investor due diligence. Disputes may arise if the vendor refuses to provide documentation, logs, security information, model details, data processing records or incident reports.

The contract should include cooperation obligations covering regulatory assistance, data subject requests, audit support and incident response; documentation delivery, subprocessor information and technical explanations; and model change notifications and evidence preservation. Regulatory cooperation is often overlooked during negotiation, but when a regulator asks questions, silence from the vendor can become a serious problem.

13. Why Arbitration May Be Useful for AI Disputes

Arbitration may be useful for AI and technology disputes because it can offer confidentiality, specialist tribunal selection and procedural flexibility; cross-border enforceability, a neutral forum and a tailored evidence procedure; the ability to appoint technical experts and privacy for sensitive business information; flexibility for urgent procedural directions and adaptability to digital evidence; and finality.

AI disputes may involve commercially sensitive information, including source code, trade secrets, model architecture, customer data, security systems and proprietary algorithms. Public litigation may expose information that parties prefer to keep confidential. Arbitration may also allow the parties to select arbitrators familiar with technology, software, data protection, IP or international commercial disputes. However, arbitration is not always the answer; the clause must be designed well.

14. When Arbitration May Not Be Enough

Arbitration may not be sufficient for every AI dispute. Limitations may arise where an urgent court injunction is needed; third parties or regulators are involved; consumers or employees have statutory claims; criminal conduct is alleged; public law issues arise; multiple contracts have inconsistent clauses; evidence must be obtained from non-parties; class or collective claims are possible; emergency relief must be enforced immediately; or IP registry or public authority action is needed.

AI contracts often involve complex ecosystems: vendor, customer, cloud provider, model provider, integrator, subprocessor, end user, regulator, insurer and affected individuals. A bilateral arbitration clause may not bind all relevant actors. The contract should therefore preserve appropriate rights to seek urgent court relief and manage multi-party disputes where possible.

15. Expert Determination for Technical Issues

Some AI disputes are technical before they are legal. They may turn on whether the system met the agreed performance benchmark; whether downtime was above the SLA threshold; whether the API failed; whether the model update was responsible for the error; whether the output was generated by the relevant system; whether data was retained after deletion; whether the vendor disabled training use; or whether a security incident was caused by customer misuse or vendor failure.

These questions may be suitable for expert determination, which can be faster and narrower than arbitration. The contract may provide that certain technical disputes are first referred to an independent expert, who may decide or give a binding or non-binding opinion on technical issues while legal claims remain for arbitration. This can prevent arbitrators from spending months on issues that a qualified technical expert can resolve more efficiently.

16. Multi-Tier Clauses for AI Contracts

AI contracts may benefit from a multi-tier dispute resolution clause. A possible structure may move through operational escalation between technical teams; senior executive negotiation; expert determination for defined technical issues; mediation for commercial settlement; emergency relief for confidentiality, data or IP risk; and, finally, arbitration for final determination.

This structure can be useful because not every AI dispute should immediately become full arbitration: a service-level dispute may be resolved operationally; a technical disagreement may go to an expert; a commercial relationship may be preserved through mediation; and a serious breach may require arbitration. But multi-tier clauses must be drafted clearly. They should specify time limits, who participates, what issues go to expert determination, whether the expert's decision is binding, whether emergency relief is preserved, when arbitration may commence, whether limitation periods are affected, and what happens if a party refuses to participate. Unclear escalation clauses create disputes about the dispute process itself.

17. Emergency Arbitration and Court Relief

AI disputes may require urgent protection. Urgent relief may be needed to stop misuse of confidential data, prevent further model training on customer data, or require deletion or isolation of data; to preserve logs, prevent disclosure of trade secrets or stop infringing outputs; to prevent termination of a critical AI service or require continued access during transition; or to protect source code, stop unauthorised customer-facing AI deployment and prevent destruction of evidence.

Contracts should preserve the right to seek urgent relief. Depending on the rules and jurisdiction, this may be through emergency arbitration, interim measures by the tribunal, national courts, injunctive relief, evidence preservation orders or confidentiality orders. A clause that sends every dispute only to ordinary arbitration may be too slow for urgent AI-related harms.

18. Confidentiality in AI Arbitration

Confidentiality is one of the main reasons parties may prefer arbitration for AI disputes. The dispute may involve source code, model architecture and training data; customer data, cyber vulnerabilities and trade secrets; pricing, the product roadmap and security controls; and internal governance documents, regulatory correspondence, proprietary prompts and technical benchmarks.

The arbitration clause and procedural orders should address the confidentiality of proceedings, submissions and documents; restricted access, redaction and protective orders; secure file-sharing and expert confidentiality undertakings; closed hearings and the treatment of awards; the destruction or return of documents; and the cybersecurity of arbitration platforms. Confidentiality should not be assumed; it should be expressly protected.

19. Digital Evidence in AI Disputes

AI disputes will often depend on digital evidence. Relevant evidence may include prompts, outputs, logs, API calls and timestamps; model version records, data processing records, access logs and settings; training configuration, user permissions and deletion records; support tickets, incident reports and audit trails; and technical documentation, system architecture diagrams, emails and messages, code repositories and change logs.

Evidence preservation should begin early. A party should consider litigation hold procedures as soon as a serious dispute becomes likely, because if logs are overwritten, prompts deleted or model version records lost, it may become difficult to prove what happened. AI contracts should require preservation of relevant records during disputes, and the handling, authentication and exchange of that material should follow the same discipline as any digital arbitration and online dispute resolution process.

20. Model Versioning and Change Control

AI systems change over time, and a dispute may depend on which version of the model was used. Questions may include which model version generated the disputed output; whether there was a model update before the incident; whether the vendor changed safety settings; whether performance benchmarks were affected; whether the customer received notice of the change; whether the customer was able to test before rollout; whether previous versions were available; and whether the contract allowed unilateral changes.

Model versioning and change control should be addressed in the contract. Without records of model changes, dispute resolution becomes harder. A vendor should not be able to change a critical system without accountability, and a customer should not deploy AI in business-critical workflows without understanding how updates are managed.

21. Allocation of Liability

AI contracts must allocate liability carefully. Possible liability issues include inaccurate outputs, data breach and confidentiality breach; IP infringement, regulatory fines and business interruption; discrimination claims, customer claims and employment claims; security incidents, failure to delete data and unauthorised training; and third-party model failure, integrator error and customer misuse.

The allocation should reflect control. If the vendor controls model behaviour, security and data use, it should accept responsibility for those areas; if the customer controls inputs, user deployment and human review, it should accept responsibility for those areas. The contract should avoid vague language that creates uncertainty at the moment of dispute. A dispute clause cannot fix poor liability drafting, but it can ensure that liability disputes are resolved in the right forum.

22. Indemnities in AI Disputes

Indemnities are central in AI contracts. A vendor may provide indemnity for IP infringement, breach of confidentiality, data protection breach caused by the vendor, unauthorised training use, security incidents caused by the vendor and third-party claims arising from vendor technology. A customer may provide indemnity for unlawful input data, prohibited use, customer instructions, use of outputs without required review, breach of the acceptable use policy and third-party claims caused by customer deployment.

Indemnities should be tied to procedural control. The indemnifying party may want control over defence, settlement approval and cooperation. In arbitration, indemnity claims may become part of the main dispute or a separate proceeding; the clause should avoid fragmentation where possible.

23. Multi-Party and Supply Chain Disputes

AI systems often involve multiple parties: customer, vendor, model provider, cloud provider, system integrator, data processor, subprocessor, reseller, implementation consultant, end user, insurer and regulator. A dispute may involve more than one contract, and if the contracts contain inconsistent dispute clauses, the result may be fragmentation — the customer–vendor contract going to arbitration in London, the vendor–cloud contract going to courts in another jurisdiction, the data processing agreement carrying a different governing law, the reseller agreement requiring mediation first, and the insurance policy specifying local court jurisdiction.

This can make resolution expensive and inefficient. AI supply chain contracts should be aligned where possible; at minimum, the main customer contract should require the vendor to manage subcontractor responsibility. Where a single problem spreads across several contracts and jurisdictions, cross-border legal coordination — aligning counsel, forums and timing across each agreement — often determines whether the dispute is contained or fragmented.

24. Governing Law and Seat of Arbitration

The governing law and the seat of arbitration matter. The governing law determines contractual interpretation; the seat determines the procedural law of the arbitration and the courts that supervise it.

In AI contracts, parties should consider neutrality, enforceability and court support for arbitration; interim relief availability, confidentiality and technology familiarity; emergency relief, arbitrator availability, language and cost; and the relationship with data protection law, IP law and mandatory local rules. The seat should not be selected casually. In cross-border AI contracts, the seat can affect the entire dispute strategy.

25. Enforcement of AI Arbitration Awards

Arbitration is attractive partly because awards may be enforceable internationally under the New York Convention framework. For AI disputes, enforcement strategy should still be considered: where the respondent's assets are; whether the counterparty is solvent; whether assets are in arbitration-friendly jurisdictions; whether public policy objections could arise; whether due process rights were respected; whether the arbitration clause was valid; whether non-signatories were involved; whether emergency relief was enforceable; whether the award requires technical performance; and whether confidentiality orders are enforceable.

Winning an award is not the same as recovery. The dispute clause should be designed with enforcement in mind from the beginning, and the cross-border enforcement of foreign judgments and arbitral awards should be planned as part of the wider strategy rather than treated as an afterthought.

26. AI Investment and M&A Disputes

AI-related disputes may arise after investment or acquisition. A buyer or investor may claim that the target misrepresented ownership of AI technology, training data rights or model performance; customer contracts, regulatory compliance or data protection practices; IP ownership, use of open-source code or cybersecurity posture; vendor dependency, revenue quality or scalability; or EU AI Act exposure and pending complaints. These disputes may involve warranty claims, indemnity claims, fraud allegations, earn-out disputes or shareholder disputes.

AI due diligence findings should be reflected in the transaction documents, and the dispute clause in the share purchase agreement, shareholders' agreement or investment agreement should be aligned with the technical nature of the likely claims. Expert determination may be useful for performance metrics or technical representations, while arbitration may be suitable for warranty and indemnity disputes.

27. AI Disputes in Türkiye, Northern Cyprus and Cross-Border Markets

Companies connected with Türkiye, Northern Cyprus, London and wider international markets may face AI-related disputes in several contexts: Turkish companies procuring AI tools from foreign vendors; AI startups serving international customers; Northern Cyprus-based businesses using AI SaaS tools; UK-connected technology contracts; cross-border data processing; AI vendor disputes involving foreign governing law; IP disputes over AI-generated content; AI-related investment disputes across multiple legal systems; enterprise AI procurement disputes; data breach disputes involving AI tools; and arbitration clauses in technology contracts.

For such companies, the legal question is not only whether AI use is lawful; it is also whether the contract can manage the dispute if the AI system fails. Cross-border AI disputes require coordinated analysis of contract law, data protection, IP, confidentiality, arbitration, enforcement and technical evidence.

28. Practical Drafting Checklist for AI Dispute Clauses

Before finalising an AI contract, parties should consider whether arbitration is appropriate and what law governs the contract; what the seat of arbitration is and which institution or rules apply; what language applies and whether emergency measures are preserved; whether court injunctions are preserved for data, IP and confidentiality, and whether technical disputes are referred to expert determination; whether mediation is required before arbitration and whether time limits are clear; whether digital evidence preservation duties are included and whether logs and model version records are preserved; whether confidentiality protections are strong enough and whether cybersecurity standards apply to the proceedings; whether multi-party disputes are considered and whether vendor subprocessors are covered; whether indemnity claims are included in the same forum and whether regulatory cooperation disputes are covered; whether data deletion disputes are treated as urgent and whether interim access and transition rights are covered; whether costs and fees are addressed and whether enforcement strategy is considered; whether consumer or employment claims are excluded where required and whether mandatory laws are respected; and whether the clause is ultimately aligned with the commercial value of the contract.

Frequently Asked Questions

Are AI disputes suitable for arbitration?

Many B2B AI and technology disputes may be suitable for arbitration, especially where confidentiality, technical expertise, cross-border enforceability and a neutral forum are important. However, consumer, employment or regulatory disputes may require separate analysis.

What types of AI disputes can arise?

AI disputes may involve vendor performance, data use, model training, confidentiality, IP ownership, AI-generated outputs, inaccurate results, bias, cybersecurity, regulatory cooperation, termination, data deletion and liability allocation.

Should AI contracts include expert determination?

For technical issues, yes. Expert determination may be useful for disputes over model performance, service levels, API failures, data deletion, technical benchmarks or system defects.

Why is emergency relief important in AI contracts?

AI disputes may involve urgent risks such as misuse of confidential data, continued model training on customer data, trade secret exposure, IP infringement, loss of access to critical systems or destruction of logs.

Can AI contract disputes involve data protection issues?

Yes. AI systems often process personal data through prompts, uploads, logs, outputs, analytics, training and vendor support. Data protection issues may become central to the dispute.

Who is responsible for harmful AI outputs?

Responsibility depends on the contract, applicable law, vendor control, customer use, human review, disclaimers, permitted use, warnings and the factual circumstances of the harm.

Why does model versioning matter in AI disputes?

A dispute may depend on which model version generated an output or caused a failure. Without model version records, logs and change control, it may be difficult to prove what happened.

Should AI contracts preserve the right to go to court?

Often yes, at least for urgent interim relief involving confidentiality, IP, data misuse, evidence preservation or critical system access.

Conclusion

AI contracts should not treat dispute resolution as an afterthought. The same features that make AI commercially powerful also make AI disputes difficult: data dependency, model opacity, probabilistic outputs, vendor chains, IP uncertainty, regulatory exposure, confidentiality risks and rapid technical change. A standard arbitration clause may be too blunt.

A well-designed AI dispute clause should decide which issues go to negotiation, which go to expert determination, which require emergency relief and which should be resolved by arbitration. It should preserve confidentiality, protect evidence, manage technical complexity, respect mandatory law and support cross-border enforcement. For companies developing, buying or investing in AI systems, legal strategy begins before the dispute — it begins in the contract.

How Terziolu & Partners Can Assist

Terziolu & Partners advises businesses, investors, entrepreneurs and private clients on Türkiye, Northern Cyprus and cross-border legal matters. Our work may include drafting dispute resolution clauses for AI and technology contracts; reviewing arbitration clauses in AI vendor agreements; advising on expert determination mechanisms; advising on AI-related confidentiality, IP and data disputes; supporting AI vendor and SaaS contract disputes; advising on emergency relief for data, IP and trade secret risks; reviewing AI-related liability and indemnity structures; supporting AI due diligence in investments and acquisitions; and coordinating with arbitration counsel, technical experts, data protection advisors and foreign lawyers where required.

Discuss an AI contract, arbitration clause or technology dispute with our team.

This article is provided for general informational purposes only and does not constitute legal advice. AI-related disputes, arbitration clauses, expert determination mechanisms, emergency relief, data protection, IP rights, confidentiality obligations, liability allocation and enforcement issues may vary depending on the contract, parties, jurisdiction, applicable law, seat of arbitration, technology, data involved, regulatory exposure and timing of advice. No action should be taken or withheld solely on the basis of this publication. Specific legal, technical, data protection, intellectual property and arbitration advice should be obtained before drafting, signing, terminating, disputing or enforcing an AI or technology contract. Submission of an enquiry to Terziolu & Partners does not create a lawyer-client relationship unless and until the engagement is formally accepted in writing.