Want to learn more about drafting, negotiating, and understanding intellectual property and technology contracts and have 10 minutes to spare? Grab your morning coffee or afternoon tea and dig into our Tech Contract Quick Bytes—small servings of technical contract insights expertly prepared by our seasoned attorneys. This month we are talking about Software as a Service contracts.
Artificial intelligence has quickly shifted from an innovative experiment to a core operational tool across industries. As business teams explore new AI service providers—ranging from automated analytics engines to generative-AI copilots—the contracts have grown more complex.
AI service contracts sit at the intersection of traditional software as a service (SaaS) agreements, data-centric licensing terms, and emerging regulatory frameworks that are still taking shape. While many AI service contracts look like standard SaaS arrangements, they contain nuances that can create outsized legal and operational risk if overlooked. A well-structured review process helps counsel spot the clauses that require negotiation early, align terms with internal risk tolerances, and prevent downstream disruption.
Below are key considerations and practical tips to keep in mind during AI service contract review for agreements styled as "AI as a Service" or using a SaaS format to provide an AI tool or software.
If you or your company would like to discuss drafting strategies regarding IP ownership terms, please contact A.J. Zottola.
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Clarify the Scope of the AI Services
- Ensure the agreement defines what the AI system actually does, including use cases, functionality, and any limitations
- Look for ambiguous descriptions like "predictive" or "autonomous," which can inflate expectations and complicate performance disputes
- Verify whether the provider uses third-party models, APIs, or datasets, as this may affect risk allocation and licensing rights
Evaluate Data Rights and Data Flows Thoroughly
- Map out what customer data is ingested, how it is processed, where it is stored, and who can access it
- Require clear delineation between what constitutes the following terms and create definitions: Customer Data, Output Data, Model Training Data, and Derived Data
- If the provider uses customer data to train or improve models, confirm opt-in requirements, anonymization standards, and the right to revoke use
Address Confidentiality and AI Output Handling
- Ensure that AI outputs—especially those containing or derived from confidential data—are expressly protected under the confidentiality clause
- Require the provider to implement technical and organizational safeguards to prevent unintended disclosure or model leakage
- Consider contract terms limiting the provider's ability to use or reuse outputs for other customers, where feasible
Confirm IP Ownership and License Rights
- Establish who owns outputs, prompts, and fine-tuned models developed during the engagement
- Validate that the customer receives the necessary license rights to use outputs commercially, including rights to derivative works
- Protect against claims that output may infringe third-party IP and ensure indemnity coverage where appropriate
Scrutinize Provider Representations and Disclaimers
- Expect broad disclaimers around accuracy, bias, and reliability; negotiate them where the AI tool materially affects business decisions
- Push for performance standards, uptime commitments, and fault-handling processes where the AI service is operationally critical
- Seek assurances relating to dataset provenance, model quality, and compliance with applicable AI, privacy, and cybersecurity laws
Review Indemnification for AI-Specific Risks
- Request indemnification for:
- Avoid indemnities limited solely to "software"—ensure they extend to model outputs and predictions
Assess Reliability, Bias, and Model-Performance Obligations
- Where AI outputs feed into high-impact decisions (e.g., employment, healthcare, finance), consider contract terms requiring the provider to:
Strengthen Data Security Requirements
- Align security measures with internal policies and industry standards
- Mandate prompt notification for security incidents involving model or data integrity, not just traditional data breaches
- Confirm restrictions on subcontractor use and cross-border data transfers
Consider Regulatory and Compliance Obligations
- Ensure the provider will assist with compliance obligations under AI-specific legislation (e.g., the EU AI Act), privacy laws, and sector rules
- Include obligations to support impact assessments, record-keeping, and responding to regulatory inquiries
Watch for Model Updates, Roadmap Changes, and Exit Rights
- Require advance notice of model deprecations, significant changes, or feature removals
- Confirm continuity protections, such as transition assistance, for offboarding or internal migration
- Ensure the customer can retrieve all data and outputs in usable formats upon termination