Vijay Raina is a distinguished expert in enterprise SaaS technology and a leading voice in software architecture and design. With years of experience navigating the intersection of complex commercial contracts and emerging technologies, Raina has become a key advisor for organizations grappling with the rapid integration of artificial intelligence into their existing software ecosystems. In a landscape where generative AI is often deployed through routine updates rather than new agreements, he provides a critical perspective on how legacy contract language—once considered standard and safe—now carries unprecedented risks and consequences. Our discussion focuses on the hidden dangers within five-year-old agreements and the necessary evolution of data definitions, usage rights, and liability frameworks as we navigate the complexities of 2026.
When a vendor activates a new AI assistant via a routine product update, as many CRM providers are doing today, what are the primary legal and operational risks that organizations overlook during this “silent” deployment?
The most immediate risk is the complete bypass of the traditional procurement and legal review process. In the scenario where a CRM vendor releases an AI tool to draft communications or summarize tickets as part of a routine update, they are often relying on “service update” provisions negotiated years ago. Overnight, a foundation model provider may start processing your company data without a fresh security review, and your internal teams may be interacting with prompts and outputs that fall entirely outside existing data governance policies. We see organizations discovering that their legacy agreements, which were drafted when software was largely deterministic, now inadvertently authorize a vendor to use data in ways that were never contemplated, such as sending information to a third-party model for processing. The subprocessor notice might have been sent, but if it didn’t trigger a review of what activating that feature actually means for data flow, the organization is effectively operating in a legal blind spot.
You have highlighted the concept of “same clause, new consequence.” Could you elaborate on how a standard update provision that seemed harmless years ago can now create significant liability in an AI-driven environment?
Historically, a provision allowing a vendor to “modify or update the services” was intended to cover interface improvements, minor bug fixes, or the addition of a new reporting tool. It was a mechanical update to a predictable system. Today, however, that same clause acts as a legal bridge for the deployment of AI functionality that actively reads customer data and influences external-facing communications. The consequence has shifted from a cosmetic change to a fundamental shift in how the software interacts with your proprietary information. If the clause allows for “modifications to improve the service,” the vendor might interpret this as permission to deploy an AI that monitors every user interaction to fine-tune its responses, creating a level of data surveillance and utilization that was never the intent of the original signatory.
As organizations move beyond basic SaaS usage, why is it becoming critical to move away from the umbrella term “Customer Data” and toward more granular definitions like prompts, outputs, and usage data?
The traditional definition of “Customer Data” is simply too narrow to catch the various artifacts created during an AI session. If your contract only protects “Customer Data” and provides for its return or deletion, you might find significant gaps regarding prompts—the specific instructions your employees give the AI—and the resulting outputs, which might be classified by the vendor as “service data” or “usage data.” We are seeing sophisticated agreements now separating information into at least six categories: customer data, inputs, outputs, usage data, service data, and de-identified data. Without these distinctions, a vendor could argue that while they must delete your raw files, they have a right to keep the prompts and the information derived from them, which often contain more sensitive strategic insights than the original data itself. The question isn’t just about what the vendor calls the information, but which specific protections and permissions follow the data as it moves through the AI pipeline.
A common strategy for legal teams is to insist on a “no training” restriction, but why is this often an insufficient safeguard against modern AI data-use practices?
A “no training” restriction is a great starting point, but it often fails to address the multiple, distinct ways data and AI systems interact in 2026. Beyond base-model training, vendors frequently use data for fine-tuning, evaluation, safety reviews, and retrieval. For instance, Retrieval-Augmented Generation, or RAG, allows an AI system to draw on stored information to generate a contextually accurate response; while this isn’t technically “training” the model, it involves indexing and embedding representations of your data into a secondary store. If your contract only prohibits “training,” the vendor might still be fine-tuning the model or retaining evaluation artifacts that could theoretically be accessed or reconstructed. Effective drafting requires us to identify precisely which of these activities—retrieval, evaluation, or fine-tuning—are permitted and what the specific retention and deletion obligations are for each of those separate technical processes.
How is the “de-identified data” clause, which many have viewed as a routine provision for years, being used as a “back door” for vendors to capitalize on customer information?
The de-identification clause is perhaps the most scrutinized legacy provision in current negotiations because it often lacks a clear technical standard for what “de-identified” actually means. In many older agreements, vendors were granted ownership of de-identified data to improve their products and services—a term that felt harmless when it meant simple analytics or benchmarking. In the AI era, this clause becomes the contractual basis for long-term retention of information that is then used to train and improve the vendor’s proprietary models. Because this data is no longer classified as “Customer Data,” it frequently escapes the obligation to be deleted at the end of the contract term. This allows the vendor to build a massive, permanent asset based on your data, potentially creating a “back door” where they own the intellectual fruits of your information long after the business relationship has ended.
Given the asymmetry of risk in current SaaS terms, how should customers navigate the tension between providing broad warranties for their inputs while receiving almost no assurances about AI-generated outputs?
This is one of the most difficult contractual hurdles we face today. Customers are frequently asked to provide comprehensive warranties for everything they submit to an AI—effectively promising that they have all necessary rights and that the input is non-infringing. However, the vendor often refuses to provide a comparable assurance for the output, sometimes even disclaiming that the output is protectable intellectual property at all. This creates a scenario where the customer carries all the diligence obligations and the legal risk if the output is problematic. Organizations must push for more balanced risk allocation, especially since outputs may not be unique; the same prompt could generate similar results for different customers, leading to complex IP disputes. We have to resolve this tension by negotiating for better vendor indemnities on outputs, or at the very least, limiting the scope of the customer’s input warranties to what they can reasonably control.
What is your forecast for the future of SaaS and AI contracting?
I believe that by the end of 2026, the era of the “all-in-one” SaaS agreement will be largely over, replaced by modular governance frameworks that treat AI as a distinct layer of risk. We will see a shift where data rights are no longer binary—meaning you either own it or you don’t—but are instead based on “permissible utility.” This means contracts will focus less on who “owns” a piece of data and more on exactly what “intelligence” can be extracted from it and for how long. I also expect the development of standardized technical benchmarks for de-identification and retrieval-data deletion, as the legal industry realizes that purely linguistic protections are insufficient without technical verification. Ultimately, the most successful organizations will be those that treat their legacy contracts as living documents, proactively auditing them to ensure that the “same clauses” don’t lead to disastrous “new consequences” as AI continues to evolve.
