Key Takeaways:
- In AI contracting, the audit clause becomes your tool for monitoring how model behavior evolves to ensure continuity across model lifecycles and verifying that operational performance matches contractual promises.
- SaaS audit rights and procedures don’t align with the realities of AI-powered solutions.
- Be sure agreements with AI vendors include applicable audit rights, procedures, and clauses.
Many SaaS contracts include standard audit clauses. It’s often boilerplate language, tucked between limitation of liability and miscellaneous clauses. You know the one: “Customer may, upon reasonable notice and no more than once per year, audit Provider’s compliance with this Agreement or request certification of Provider’s compliance with this Agreement.”
But drop that same paragraph into a contract involving AI-powered solutions and it’s woefully insufficient.
Why? Because audit rights were built for static systems and AI is anything but. Models evolve, outputs shift, and systems update. In this dynamic terrain, the audit clause is more than a formality. It’s a frontline control.
Why AI Breaks the Mold
Unlike traditional software, which produces consistent results given the same input, AI models generate outputs based on probabilities. The same input can (and does) yield different outputs, even under identical conditions.
This variability makes it difficult to define and rely on a stable “baseline performance.” A model that performs well during onboarding may behave differently in production. Variability can affect accuracy, fairness, and compliance, particularly in regulated or high-stakes environments.
What to include in the contract:
- Benchmarking data: Access to metrics like accuracy, latency, and reliability over time.
- Output testing results: Evidence of how the model performs in real-world scenarios, not just controlled testing environments.
- Evaluation methodology: Transparency into what’s being tested, how, and under what assumptions (e.g., prompt types, data quality).
- Confidence scores and fallback logic: Mechanisms for signaling uncertainty (e.g., confidence thresholds) and fallback procedures when the model is likely to produce low-confidence outputs.
Learn More: For guidance on evaluating AI tools, refer to my Contract Nerds article on key considerations when onboarding an AI tool.
Rethinking Audit Rights for AI Unpredictability
Most SaaS audit clauses focus on infrastructure compliance like uptime, security protocols, and data handling. But AI tools which, by their nature, behave unpredictably demand a rethinking of audit expectations.
AI changes constantly. Your audit rights need to keep up.
AI models evolve after deployment. Vendors often retrain or fine-tune models, update parameters or switch architectures, all of which can alter performance and risk without customer notice.
Even minor updates can degrade performance, introduce bias or invalidate earlier testing.
What to include in the contract: Rights to
- Model change logs
- Retraining documentation
- Validation reports, and
- Version histories.
These allow you to track how the system evolves and whether it still meets your standards.
Models are frequently deprecated or replaced.
Model providers move quickly from one version to another (e.g., GPT 3.5 to GPT-4) and vendors incorporating LLMs into their products or services may change the underlying models based on cost, availability or performance, often without disclosure.
If there is a change to the model, you may be relying on a tool with different limitations, risk profiles or hallucination rates than the one you signed off on.
What to include in the contract:
- Advance notice of any substitution
- Review rights for new model documentation and limitations, and
- A non-degradation clause such as: “Replacement models shall not materially degrade performance or increase risk exposure.”
Model performance can drift over time.
Even without updates or retraining, model performance can decline. This phenomenon, called model drift, happens when the real-world data the model sees diverges from its training data or as it encounters new use cases.
Drift is gradual and hard to detect. If audit rights are limited to annual reviews, you may miss the opportunity to catch and correct serious issues.
What to include in the contract: Audit rights should explicitly cover performance evaluation over time, not just initial compliance. Strengthen your audit clause to allow for the following:
- “For cause” audit triggers (e.g., if there’s a drop in output quality, increased user-reported errors or results inconsistent with past behavior);
- Ongoing audit rights tied to model performance benchmarks, not calendar cycles;
- Access to live or recent outputs and evaluation data to verify continued quality; and
- Notification obligations if internal monitoring detects drift or risk signals.
You may also want to link model drift to SLAs or termination rights if performance degradation impacts contract-critical outcomes.
Don’t Let API Layers Block Your Audit Rights
You may not be contracting with OpenAI, Meta or Anthropic directly, but rather with a vendor integrating their models. Those vendors may disclaim responsibility for model behavior because they didn’t build it.
But if model output affects your obligations, decisions or workflows, you bear the consequences regardless of whether the vendor built the AI or incorporated it through an API call.
You need contractual protections that ensure transparency and traceability regardless of who owns the underlying technology. To preserve your audit rights, consider adding a clause that reaches through the vendor to the underlying model provider like:
“If Provider integrates a third-party AI model into the Products or Services, Provider shall ensure that such model is subject to a level of auditability and documentation consistent with this Agreement. Provider shall promptly disclose to Customer any material changes, performance degradation or risk factors communicated by the third-party AI provider that could impact the quality, behavior or reliability of the Products or Services. Upon Customer’s request, Provider shall make available any documentation, testing results or model summaries received from the third-party AI provider relevant to Customer’s use of the Products or Services.”
Without this, you risk losing visibility into model swaps, drift, or sudden degradation from upstream changes. This clause ensures the vendor who is monetizing the model takes responsibility for maintaining transparency across the full tech stack.
If You Can’t Audit Directly, Ask for What Matters
For many AI contracts, direct audit access may not be realistic. The LLM provider may reject the request (and you have little leverage to negotiate), a vendor using a third-party LLM may lack rights to inspect or disclose the model’s internals, or your team may not have the technical capacity for meaningful review.
Still, you don’t have to operate blindly. In the absence of audit rights (or in addition to), negotiate for documentation that gives you meaningful visibility into model behavior—how it performs, where it struggles, and whether it’s safe to rely on in your use case.
Request model-specific documentation such as:
- Model cards summarizing the model’s capabilities, limitations, intended use cases, and known risks (e.g., hallucinations, bias, misuse);
- AI impact assessments covering fairness, bias, and compliance risks;
- Performance test results relevant to your use case (e.g., clause detection, summarization fidelity); and
- Explainability summaries offering plain-language insights into how the model works, when it might fail, and any human-in-the-loop safeguards.
These aren’t a substitute for direct access, but they create a foundation for assessing risk and compliance.
Visibility is Risk Mitigation
In AI contracting, the audit clause becomes your tool for monitoring how model behavior evolves, ensuring continuity across model lifecycles, and verifying that operational performance matches contractual promises.
Don’t treat it as boilerplate. Build it to reflect how AI actually works.
For more on AI and Contracts, check out my full column here.
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