Navigating AI Vendor Contracts: Protecting Your Data and IP Amidst AI Training Concerns
As artificial intelligence (AI) rapidly integrates into a vast array of vendor offerings, from consumer-facing chatbots to enterprise knowledge management solutions, companies face a critical need to scrutinize the contractual terms governing these powerful tools meticulously, among other relevant AI legal considerations.
The proliferation of AI functionality necessitates a heightened focus on how vendor agreements address data usage, particularly concerning the training of AI models by both the vendor and third-party AI model providers like OpenAI, Google, and Amazon. These crucial terms may be embedded within Master Service Agreements (MSAs), Data Processing Agreements (DPAs), Statements of Work (SOWs), or increasingly, AI-specific addendums.
The Criticality of AI Training Clauses
The core issue for companies leveraging AI tools, especially those processing large volumes of consumer data or proprietary information, is ensuring that input data, prompts, and outputs are not used for purposes beyond the provision of the contracted service. A primary concern revolves around whether customer data, including personal information or valuable intellectual property (IP), will be used to train the vendor’s AI models or the foundational models of their AI technology providers.
Failure to secure adequate contractual protections can lead to significant negative ramifications. These include inadvertently “selling” or “sharing” personal information under comprehensive privacy laws like the California Consumer Privacy Act (CCPA), potentially triggering regulatory scrutiny or private rights of action. Claims under statutes such as California’s Invasion of Privacy Act (CIPA) may also arise, particularly if AI tools are deemed to have the “capability” to use data for the provider’s benefit, even without explicit consent or actual use for training. Beyond legal and compliance breaches, the unauthorized use of data for AI training can lead to the leakage of confidential company information, trade secrets, and other protected IP, diminishing competitive advantage and potentially causing significant financial and reputational harm.
Negotiating AI-related contractual terms requires nuance, as vendors vary in their willingness to accept restrictions on data use for training purposes. Some vendors, particularly those with mature compliance and security programs, may proactively provide clear information on their AI training practices and data handling policies, often on their websites. This transparency allows for easier assessment of their posture.
Conversely, other vendors, especially those with nascent or less developed compliance frameworks, may offer limited or no information on their AI training practices and may even be unprepared to address such inquiries. This lack of clarity can be a significant red flag, even if the vendor ultimately agrees to more restrictive contractual language regarding data use. The concern then shifts to whether the contractual commitments can and will be operationalized effectively, as a gap between legal agreements and technical implementation can still expose the customer to risk. Companies should, therefore, actively engage vendors on these points, making it clear that restrictions on training AI models with their data are a priority.
Essential Restrictive Clauses for AI Vendor Contracts
To mitigate risks associated with AI data usage, companies should seek to incorporate specific, restrictive clauses into their vendor agreements. At a minimum, companies will want to ensure that their prompts and other protected data, whether personal information or intellectual property, are not used for any purpose other than providing the specific service in question. Key protective clauses include:
- Prohibition on Vendor Training: A clear statement that the “Vendor will not train its AI and related features using Customer Data”. This directly addresses the use of a company’s proprietary or sensitive information for the vendor’s model improvement.
- Restrictions on AI Model Providers: An affirmation that the “Vendor has and will continue to have contractual agreements in place with its AI Model Providers which prohibit AI Model Providers from utilizing Customer Data to train or improve their AI models, features, and services”. This extends protection to the underlying technology layer.
- Data Security and Deletion Mandates: A requirement that “AI Model Providers are required to ensure Customer Data is encrypted in transit and at rest and delete the Customer Data after processing”. This addresses data security throughout its lifecycle and minimizes long-term exposure. Specific data retention timelines should be adjusted based on use case and risk.
Obtaining explicit, informed consent for any intended use of data in AI training is paramount. Contracts should also commit to data usage limitations, such as prohibiting the sale or sharing of customer data with third parties without consent, and employing anonymization or de-identification techniques where feasible. In most cases, ideally, companies will negotiate for the right to opt out of having their data used for AI training altogether, where data is only used “to provide the service” or where it is beneficial for the company to have their use trained on, the training is only to benefit said company and not other customers.
Ancillary Contractual Considerations for AI
Beyond AI training restrictions, several other contractual elements warrant careful attention when engaging AI vendors. These are crucial for a comprehensive risk management strategy.
Indemnification
Indemnification clauses are critical for allocating risk, especially concerning IP infringement and data privacy violations. Given that AI models are often trained on vast datasets with unclear provenance, the risk of copyright infringement or other IP violations from AI-generated output is a significant concern. While some major AI providers have begun offering IP indemnities, these often come with exceptions, such as if the user knew the output might infringe or if the input data itself contained the infringing material. Only about 33% of AI vendors offer IP infringement indemnification, compared to 58% in broader SaaS agreements. Companies should push for broad indemnification covering claims related to unauthorized training data use, AI-generated outputs, dataset licensing issues, bias-related lawsuits, and regulatory fines.
Intellectual Property Ownership
Clarity regarding the ownership of inputs, outputs, and any “learnings” derived from AI processing is essential. Typically, the customer retains ownership of its input data. However, the ownership of AI-generated outputs and the learnings or improvements to the AI model derived from processing customer data can be contentious. Some vendors, like Microsoft with its consumer services, may acknowledge that the user owns the AI-generated output. Contracts should explicitly define ownership and usage rights for all data and AI-generated content to prevent future disputes and protect valuable IP.
Limitations of Liability
AI vendor contracts will include limitation of liability (LoL) clauses, often capping liability at amounts paid under the contract or disclaiming liability for AI-generated content. Given the potential for significant harm from AI errors, bias, or security incidents, companies should negotiate these clauses carefully. Standard LoLs often waive indirect, consequential, and punitive damages. It is advisable to seek carve-outs from liability caps for critical areas such as indemnification obligations, breaches of confidentiality, data breaches, gross negligence, or willful misconduct. The concept of “super caps” (higher liability limits) for AI-related liabilities is an emerging area of negotiation, particularly for high-risk applications. Aligning liability limits with the vendor’s insurance coverage can also be a strategic approach.
Data Security and Breach Notification
Robust data security provisions are non-negotiable, especially when AI systems process vast amounts of personal information or otherwise sensitive data. Contracts should specify the security measures the vendor must implement, including encryption, access controls, and vulnerability management. Provisions for data breach notification, including timelines and cooperation in investigation and remediation, are also critical. Understanding the vendor’s cybersecurity posture and incident response capabilities is vital because AI tools can be new attack vectors.
Warranties
Securing meaningful warranties for AI systems can be challenging, as vendors may argue that AI models are probabilistic and constantly evolving, making performance guarantees difficult. However, customers should seek warranties that the AI solution will perform according to agreed-upon specifications and comply with applicable laws, including privacy regulations and associated compliance. For high-stakes AI applications, tying warranties to clear performance metrics and functional reliability, with remedies like model retraining or service credits for non-compliance, is advisable.
Regulatory Compliance and Transparency
AI is subject to a rapidly evolving regulatory landscape, including the EU AI Act and various US state-level initiatives. Contracts should require vendors to comply with all applicable laws and regulations and to maintain transparency about their AI systems, particularly regarding data sources, model training, and how automated decisions are made. This is especially important for mitigating risks related to bias in AI decision-making.
AI Rapidly Advances and Brings Great Potential and Risk
As AI technology continues its rapid advancement, the contractual frameworks governing its use must evolve in tandem. Proactively addressing the unique risks posed by AI through carefully negotiated vendor agreements is no longer optional but a fundamental component of responsible AI adoption and corporate governance.
