As the integration of artificial intelligence (AI) into enterprise workflows continues, organizations are increasingly tasked with reviewing vendor agreements in respect of such applications. Unfortunately, organizations may be tempted to accept such agreements without fully considering their terms. Driving any such decision may be a variety of reasons, including:
- these agreements may be presented as a standard-form, "take it or leave it" agreement;
- they resemble conventional software-as-a-service agreements (sometimes with no reference to AI);
- their provisions may appear benign at first glance; or
- the associated licensing fees often fall below an organization's materiality threshold.
As this blog explores, the failure to adapt the organization's procurement controls and systems to identify and manage the emerging AI issues, and as a result ensure the careful review of these agreements may pose significant risks to an organization.
Data Rights and Confidentiality
Many agreements grant vendors sweeping licenses to use, reproduce and exploit user content (raw and processed) without meaningful restriction. These rights frequently extend to the collection and processing of confidential, personal or sensitive information entered into the application, enabling vendors to leverage such data for purposes including training and refining their AI models, developing derivative works or creating data lakes enabling industry benchmarking.
Where an organization's data is incorporated into model training, the implications can be profound: the resulting model may subsequently be deployed for other clients, including direct competitors, thereby eroding an organization's competitive advantage and unintentionally strengthening theirs.
Further, such agreements often authorize vendors to aggregate and anonymize an organization's data for virtually any purpose, including commercial exploitation and industry benchmarking. In many cases, vendors reserve the right to retain copies of such data indefinitely—even after the termination of the agreement.
Finally, it is not atypical for these agreements to remain silent with respect to how an organization's data will be protected and the jurisdiction(s) in which it will be processed, accessed or stored.
The cumulative effect of these provisions is that any information submitted through the application may be repurposed to retrain models or even resurface in outputs delivered to other users.
Absent robust contractual safeguards, organizations risk waiving privilege, compromising privacy, confidentiality and relinquishing control over their confidential, personal or sensitive data. These risks underscore the critical importance of negotiating tailored protections that address data usage, residency, retention, security and confidentiality in AI vendor agreements.
Intellectual Property: Ownership and Infringement
AI-generated content introduces evolving challenges related to intellectual property (IP) ownership and infringement. Most enterprise AI agreements almost always provide that the vendor will retain exclusive ownership to the underlying models, algorithms and training datasets powering the application.
Organizations, on the other hand, typically retain ownership of the outputs generated by the tool; however, ownership and usage rights can vary; in some cases, the agreement includes restrictions on an organization's commercial use or redistribution of the output. And, as noted above, vendor agreements often grant the vendor licenses to use such outputs.
As many AI tools are built upon a series of third-party inputs, tools and data sets, it is not uncommon to find that the vendor does not know the full provenance of its product nor the outputs of same.
For example, many AI systems are trained on vast, heterogeneous datasets that may incorporate protected works, including copyrighted material, proprietary code, or other IP-sensitive content. Such datasets may engage the laws of many jurisdictions. If the application produces outputs that resemble or replicate such protected works, organizations may inadvertently commit infringement, exposing themselves to claims for damages, injunctions, or reputational harm.
Accordingly, to mitigate their own exposure, vendors frequently include broad disclaimers regarding such outputs, effectively shifting the risk to the user. For example, these disclaimers often state that the vendor assumes no liability for outputs that may infringe third-party rights.
In addition, it is important to note that intellectual property rights may not arise in the context of AI generated content, and that the laws in this area are complex and evolving, varying between jurisdictions.
Representations and Warranties, Disclaimers, Limitations of Liability and Indemnities
Vendor agreements frequently provide minimal assurances regarding the performance or reliability of the application. In most cases, vendors expressly disclaim any representations or warranties as to the accuracy, completeness, or suitability of the application’s outputs—delivering the service strictly on an “as is” basis. In such cases, organizations are given no assurances whatsoever that the application will actually work, be available, or meet any of the organization's requirements.
Beyond the absence of any meaningful representations or warranties, these agreements typically include sweeping limitations of liability through which vendors seek to exclude responsibility for virtually any liability associated with: (i) the application; (ii) the use thereof; (iii) any suspension, interruption, or delay of the application; (iv) any loss of an organization's data; or (v) any breach of data or system security. Where liability is accepted, it is commonly capped at a nominal amount, rarely more than the value of the contract, shifting most of the risk onto the organization.
Compounding this imbalance: organizations themselves often have no cap on liability and are frequently required to indemnify the vendor against any claims, losses, or expenses arising from their use of the application, including claims related to the use, distribution or publication of the application outputs.
This one-sided allocation of risk means that even when harm originates from the vendor’s technology (such as infringing content generated by the AI), the organization bears the full burden of defense and liability, with little contractual recourse.
For clarity, the implications of the foregoing extend beyond IP infringement. If the application contributes to a data breach, regulatory non-compliance or generates biased or discriminatory outputs, vendors typically disclaim responsibility, leaving the organization to absorb both the legal and reputational fallout.
In short, the combination of negligible warranties, aggressive liability exclusions and unilateral indemnity obligations shifts the majority of risk squarely onto the organizations.
Regulatory Exposure: The Compliance Gap
Vendor agreements frequently provide only minimal assurances regarding compliance with applicable laws, often shifting the entire regulatory burden onto the customer. This approach is increasingly problematic given the accelerating pace of AI regulation and the heightened scrutiny from global regulators. For organizations, this obligation can be onerous, particularly where they lack visibility into the vendor’s underlying models, training data or governance practices.
The challenge is compounded by the fragmented and evolving nature of global AI regulation. Jurisdictions impose varying requirements. For instance, the European Union’s AI Act introduces rigorous obligations for “high-risk AI systems,” including transparency, risk management and conformity assessments, while Canadian legislation remains in early stages and does not yet incorporate comparable concepts. Multinational organizations must therefore navigate a complex regulatory patchwork, adapting compliance strategies across multiple regimes, each with its own set of definitions, standards and enforcement mechanisms.
Further, many agreements fail to mandate adherence to foundational principles of responsible AI, such as transparency and explainability. In such circumstances, vendors are not required to provide audit reports, documentation of model logic, or disclosures regarding training datasets. This opacity may leave organizations unable to demonstrate compliance or respond effectively to regulatory inquiries, creating significant legal and reputational exposure.
Other considerations
In addition, there may be various provisions that favours vendor protection and flexibility. For example, a common issue is the lack of termination for convenience rights, which can lock organization into multi-year commitments with no ability to exit for strategic or operational reasons. In addition, many agreements provide no transition assistance upon termination and impose no obligation on the vendor to return or securely dispose of an organization's data—creating long-term exposure around data portability and compliance.
Pricing structures can also introduce hidden risks: agreements may lack mechanisms to scale user counts up or down, forcing organization to pay fixed fees even as usage declines and provide no clarity on costs for adding new users—potentially resulting in inflated charges.
Another common provision frequently encountered is the vendor’s unilateral right to amend the agreement, often with minimal notice and no corresponding right for the organization to terminate. This means organizations must accept changes that could materially alter their obligations or risk profile.
Conclusion
Accordingly, organizations should review their procurement controls and systems, move beyond passive acceptance of vendor's terms and adopt a proactive, risk-focused approach. This includes negotiating contractual provisions that fairly allocate operational and intellectual property risks, implementing internal review protocols for AI-generated outputs and considering indemnities or insurance coverage to mitigate potential third-party claims. Without these safeguards, reliance on standard-form agreements can leave enterprises exposed to significant legal, operational and compliance vulnerabilities.
Equally important is the need for heightened scrutiny of liability frameworks and governance structures (including, management reporting). Where appropriate, organizations should insist on balanced liability caps, reciprocal indemnities and explicit obligations around data security, regulatory compliance and ethical AI principles such as transparency and bias mitigation. Organizations need to consider and require the management reporting required to enable effective governance of the AI tool and its vendor. Absent these protections, organizations risk not only financial exposure but also reputational harm and strategic setbacks.
If you have any questions with respect to an AI-related agreement that you are considering entering into, or have already entered into, we invite you to contact one of the authors of this blog post.
