Every organization that’s adopted AI tools over the past few years has also, whether it realized it or not, been signing a new category of vendor contract, one that carries risks your standard software or services agreement template was never built to address. This is Part 1 of a two-part series on modifying vendor contracts to build in real protections against AI risk. Here, we look at why AI vendor relationships are fundamentally different from traditional software vendor relationships and where your existing contract templates are most likely to leave you exposed. Part 2 will walk through the specific clauses to negotiate and how to structure them.

Why an AI Vendor Isn’t Just Another Software Vendor

A traditional software license agreement assumes a relatively simple relationship: the vendor provides a defined product, your data flows into that product for a defined purpose, and the vendor’s obligations around confidentiality and security are relatively static once the contract is signed. AI vendor relationships break that assumption in several important ways, and a legal team relying on a standard software procurement template is going to miss all of them.

First, AI vendors frequently aren’t a single, self-contained product. Many AI tools are built on top of underlying foundation models licensed from another company entirely, meaning your data may pass through multiple corporate hands, the vendor you contracted with, plus one or more underlying model providers, before processing is complete. Your standard vendor agreement almost certainly doesn’t address this multi-layered data flow at all.

Second, AI vendors change their underlying models regularly, sometimes without much notice, and a model swap can materially change the behavior, accuracy, and even the data-handling practices of the product you’re using, without you having signed anything new. A traditional software contract’s change-management provisions, built around version updates and feature releases, generally aren’t built to capture the significance of an underlying model swap.

Third, and most importantly, AI vendors often have a commercial incentive to use customer inputs and outputs to train or improve their models, an incentive that doesn’t exist in the same way for traditional SaaS vendors. Unless your contract explicitly and specifically addresses this, you may be granting a vendor rights to your confidential data, and your customers’ data, that you never intended to grant and that your own confidentiality obligations to clients may not actually permit.

Where Standard Templates Leave You Exposed

If your organization is still running AI vendor relationships through the same procurement template used for a project management tool or an email marketing platform, a few specific gaps are almost certainly present.

Data training rights are usually either silent or dangerously vague. Most legacy vendor templates include a generic confidentiality clause that was never drafted with AI training practices in mind, and “confidential” doesn’t automatically mean “not used for model training.” Without an explicit, affirmative prohibition on using your inputs and outputs to train or fine-tune any model, commercial or otherwise, you may have implicitly granted that right through silence, depending on how the vendor’s own terms of service are structured underneath your negotiated agreement.

Indemnification provisions typically don’t extend to AI-specific harms. A standard indemnification clause protects against claims that the vendor’s software infringes a third party’s intellectual property rights. It rarely, if ever, extends to claims arising from a model’s outputs, meaning if an AI tool generates content that turns out to be defamatory, infringing, or based on improperly sourced training data, your standard indemnification language may not cover that scenario at all. Training data provenance is a related and frequently overlooked gap: if the underlying model was trained on data the vendor didn’t have rights to use, your organization could face exposure simply for having used or commercialized outputs from that model, even without any wrongdoing on your part.

Audit and verification rights are often entirely absent. Even when a contract does include a promise that the vendor won’t train on your data, most legacy templates give you no actual mechanism to verify that promise is being honored. Without audit rights or at least a right to request compliance documentation, your only source of assurance is the vendor’s own self-reporting, which is not a meaningful control in any high-stakes vendor relationship.

Liability caps frequently undercut whatever protections do exist. Even where a contract includes reasonably strong indemnification language, that protection can be effectively hollowed out if the same contract caps the vendor’s total liability at something like the fees paid in the prior twelve months. For a low-cost AI tool handling high-sensitivity data, that cap can be a small fraction of the actual exposure your organization faces if something goes wrong, which means indemnification and liability limitation provisions need to be negotiated together, not treated as separate, unrelated sections of the contract.

Why This Matters Now, Not Eventually

The temptation for many legal and procurement teams is to treat AI-specific contract language as a future project, something to build once AI adoption has matured and the organization has more experience with these tools. That’s backwards. The contract is the one point of leverage your organization has before the relationship begins, before your data has already flowed into the vendor’s systems, before an underlying model has already been trained on your inputs, and before a dispute has already materialized. Once the relationship is live and running on legacy contract terms, renegotiating meaningful AI-specific protections becomes substantially harder, both practically and from a leverage standpoint. Every new AI vendor relationship your organization signs on outdated templates is locking in exposure that will be considerably more difficult to unwind later than it would have been to prevent at signing.