The Rise of AI Vendor Agreements: 7 Clauses Every Business Needs to Get Right in 2025

By Holon Law Partners, LLP
Informational only. Not legal advice.

As organizations rapidly deploy AI to streamline operations, enhance analytics, and accelerate content creation, one reality has become clear: AI vendor agreements are no longer “standard tech contracts.” They now sit at the center of a fast-moving legal landscape that includes Colorado’s Artificial Intelligence Act (SB 24-205), the EU AI Act, New York City’s automated hiring law (Local Law 144), and a growing wave of copyright and discrimination litigation tied to AI systems. 

In 2025, businesses negotiating AI tools—whether enterprise platforms or specialized, domain-specific models—should pay close attention to the following seven contract clauses. Getting these terms right can materially reduce operational, compliance, and litigation risk.

1. Data Rights & Ownership

AI systems rely on access to data, which means contracts must clearly define:

• What customer data the vendor can access
• How it can be used
• Whether derivative data—embeddings, logs, fine-tuning outputs—belongs to the customer or the vendor

This is no longer a purely commercial question. Copyright and data-use disputes over training sets—such as Getty Images v. Stability AI, Andersen v. Stability AI, and author class actions against AI companies like OpenAI—underscore how contested training data and downstream uses have become. 

Ambiguity in data rights can lead to unintentional model training on proprietary or regulated data, or allow vendors to generate lasting value from your inputs. Agreements should impose clear data-use limitations, including “no training,” “no commingling,” or “no retention” provisions where appropriate, especially for confidential, regulated, or high-risk data sets.

2. Model Training, Fine-Tuning & Improvement Restrictions

Most AI vendors default to using customer data to improve their models unless the contract says otherwise. Key questions include:

• Can the vendor use your data to train its general-purpose or commercial models?
• Are fine-tuned models siloed to your environment?
• Does the vendor retain rights to “learn” from your use and apply those learnings elsewhere?

Litigation over training data—such as Thomson Reuters v. Ross Intelligence, which involves use of legal content to train an AI research tool, and author lawsuits like Tremblay/Silverman v. OpenAI—highlights the risk when training practices are opaque or insufficiently documented. 

Clear contractual boundaries help protect trade secrets, customer information, and regulatory compliance (particularly in finance, healthcare, and employment) and make it easier to demonstrate “reasonable care” under emerging AI governance regimes, such as Colorado’s SB 24-205. 

3. Output Rights & IP Allocation

AI output creates complex questions around:

• Who owns the output
• Whether it is licensed or assigned
• Whether the vendor can reuse or analyze outputs
• What happens if outputs trigger third-party IP claims

Recent decisions, including the UK ruling in Getty Images v. Stability AI, show courts grappling with whether models “store” copyrighted works and how trademarks may be implicated when AI outputs recreate branding elements like watermarks. 

In vendor agreements, businesses should:

• Secure broad, exclusive commercial rights to their outputs where feasible.
• Restrict vendors from reusing or publishing outputs except for narrow security or safety-related purposes.
• Require the vendor to indemnify against certain IP claims tied to the model or training corpus, balanced with realistic caps and exclusions.

4. Liability Caps & AI-Specific Risk Carve-Outs

Traditional SaaS contracts often impose tight liability caps and broad disclaimers. AI, however, introduces new categories of risk:

• Inaccurate or “hallucinated” outputs used in operational decisions
• IP claims stemming from training data or generated content
• Regulatory violations (privacy, employment, consumer protection) tied to automated decisions
• Data breaches and model-specific vulnerabilities
• Discriminatory or biased outputs, particularly in hiring, lending, and insurance

Cases like Mobley v. Workday, where an AI vendor faces discrimination allegations under federal and state law for its applicant-screening tools, illustrate how AI-driven decisions can become a focal point of liability for both vendors and customers. 

At the same time, regulators such as the FTC have made clear that AI tools do not enjoy any exemption from existing consumer-protection and advertising laws, as demonstrated by enforcement initiatives targeting deceptive AI marketing and “robot lawyer” claims. 

Businesses should consider:

• Adjusting liability caps for AI-specific harms
• Carving out certain categories (e.g., data breaches, IP infringement, willful misconduct, regulatory fines) from limitations on liability or disclaimers
• Aligning indemnity obligations with where the risk is actually created (e.g., training corpus vs. customer prompts vs. deployment choices)

5. Security Controls, Technical Safeguards & Model Access

AI tools raise distinctive security concerns—prompt injection, data leakage through logs, model-extraction attacks, and more. Regulators and policymakers are explicitly referencing AI and algorithmic security in new frameworks, including the NIST AI Risk Management Framework, which calls for documentation, monitoring, and robust controls across the AI lifecycle. 

Vendor contracts should address:

• Encryption, access control, and environment isolation
• Logging and monitoring of model access and abuse
• Incident response timelines and cooperation duties
• Restrictions on subcontractors and infrastructure providers
• Controls around how test, support, and debugging environments handle real data

In certain sectors, these controls must also harmonize with existing regimes (GLBA, HIPAA, state privacy laws), as well as broader AI governance duties like those in Colorado’s AI Act and the EU AI Act’s obligations for “high-risk” systems. 

6. Transparency, Documentation & Explainability

As AI becomes embedded in consequential decision-making—such as hiring, lending, education, and critical infrastructure—laws increasingly require documentation, transparency, and human oversight.

Examples include:

• New York City Local Law 144, which mandates bias audits, public summaries of audit results, and candidate notice for automated employment decision tools. 
• Colorado’s SB 24-205, which imposes a duty of “reasonable care” on developers and deployers of high-risk AI systems, combined with documentation, disclosure, and impact assessment obligations. 
• The EU AI Act, which establishes layered requirements for “high-risk” systems around data governance, logging, transparency, and human oversight. 

Vendor agreements increasingly need to require:

• Access to model documentation, including known limitations and intended use cases
• Notice of material updates to models or training data that may affect performance or risk
• Cooperation in supporting bias audits, impact assessments, and regulatory reporting
• Clear descriptions of how the model was trained and evaluated, to the extent commercially and legally feasible

These transparency obligations help enterprises show that they exercised diligence in selecting and overseeing AI vendors—critical under both emerging AI laws and traditional negligence, consumer-protection, and discrimination frameworks.

7. Monitoring, Audit & Ongoing Compliance

AI systems are dynamic: models drift, data shifts, and new legal standards emerge. Laws like Colorado’s AI Act and the EU AI Act explicitly contemplate ongoing monitoring and risk management for high-risk systems, not just one-time assessments.

In parallel, enforcement and litigation trends—especially in employment—are pushing organizations toward continuous oversight. The Mobley v. Workday litigation and related state and federal enforcement activity underline the expectation that employers and vendors will monitor AI-driven hiring tools for disparate impact and other discriminatory outcomes over time. 

To reflect this reality, AI vendor agreements should:

• Provide rights to periodic audits or third-party assessments
• Require vendors to notify customers of material performance, security, or compliance issues
• Define human-in-the-loop expectations for consequential decisions
• Permit suspension or termination where continued use would be noncompliant or unsafe
• Allocate responsibility for monitoring metrics (e.g., false positives, error rates, disparate outcomes) and remedial actions

AI vendor agreements are quickly becoming a primary vehicle for operationalizing AI governance. As laws like Colorado’s AI Act, NYC Local Law 144, and the EU AI Act roll out—and as courts continue to test the boundaries of copyright, privacy, and discrimination in AI systems—contract terms around data, training, liability, security, and monitoring are moving from “nice to have” to central risk controls. 

Holon Law Partners assists organizations in navigating these emerging contract frameworks with a focus on clarity, compliance, and strategic risk mitigation.