A department head signs up for an AI tool with a company credit card. Employees begin pasting customer emails, draft contracts, patient scheduling details, or internal financial information into it. Months later, the business discovers the provider may retain those inputs, use them to improve its systems, or limit responsibility if something goes wrong. That is how many AI contract risks begin: not with a dramatic dispute, but with routine business use that was never fully reviewed.
AI can create real efficiencies, but the contract governing the tool matters as much as the tool itself. Before your company buys, builds, integrates, or permits the use of AI, it should understand how the agreement handles data, intellectual property, security, compliance, and liability.
What AI contract risks should a business look for first?
Start with the provisions that control your data, the AI provider’s permitted uses of that data, ownership or use rights in outputs, and each party’s responsibility for losses. These terms often appear in a master service agreement, online terms of service, privacy notice, data processing addendum, or an order form. Reading only the pricing page or sales proposal is not enough.
A business should determine exactly what information users will enter into the system. The answer may include confidential business information, customer records, source code, trade secrets, employee data, or regulated information. The more sensitive the input, the more carefully the company should review the provider’s collection, storage, security, retention, and deletion terms.
Many standard AI agreements favor the provider. They may grant broad rights to process or retain submitted content, disclaim that output will be accurate, and place a low cap on the provider’s liability. Those provisions are not automatically unacceptable. A low-risk writing assistant used for public-facing marketing ideas may justify a different approach than an AI platform connected to your customer database or electronic health record.
The key is to match the contract to the business use case. A useful review begins with plain questions: What goes in? What comes out? Where does it go? Who can access it? What is the cost if the system is wrong, unavailable, or compromised?
Can an AI vendor use our business data to train its models?
It depends on the contract and the provider’s current product terms, but businesses should not assume that submitted data is excluded from model training. Some providers offer settings or enterprise terms that limit training use. Others reserve rights to use inputs, prompts, feedback, or de-identified information to operate, improve, or develop their services.
The distinction between using data to provide the service and using it to improve a general model is significant. Your company may be comfortable allowing a provider to process a document solely to generate a requested result. It may be less comfortable allowing that same document to contribute to a model that serves other customers.
Look for clear language addressing whether the provider may train on prompts, files, chat history, and generated outputs. The agreement should also address subcontractors, cloud hosting providers, data location, retention periods, and deletion procedures after termination. If the service has administrative settings that control training or retention, assign someone to configure and periodically verify them. A favorable contract term provides limited protection if employees use a different account type or leave the default settings unchanged.
For companies handling personal information, financial information, or sensitive health data, this review should be coordinated with privacy and compliance obligations. Healthcare businesses, for example, may need to consider whether the vendor relationship and intended use trigger additional requirements beyond the vendor’s standard AI terms.
Who owns AI-generated content and can we safely use it?
A contract should clearly address rights in both the material your business provides and the output the system generates. Do not rely on a broad statement that the customer owns output without reading the limitations that follow it.
Many agreements say the customer receives rights to output only to the extent the law permits. They may also state that outputs are not unique, meaning another user could receive a similar result. That can be a practical concern for marketing copy, product names, software code, design concepts, legal language, and other material a business expects to use exclusively.
Ownership is only one issue. A business also needs to consider whether an output may contain inaccuracies, infringe another party’s rights, reveal sensitive information, or fail to qualify for intellectual property protection. AI-generated text can sound confident while misstating a fact. AI-generated code can introduce licensing or security concerns. AI-generated images can create questions about third-party rights and brand use.
The appropriate safeguards depend on the use. A human review process may be sufficient for internal brainstorming. Higher-stakes uses, such as customer communications, regulated disclosures, employment decisions, clinical workflows, or contract drafting, require more defined review and approval procedures. The agreement should not become an excuse to skip operational controls.
How should a business handle confidentiality and security in an AI agreement?
The provider should be contractually required to protect confidential information using reasonable administrative, technical, and physical safeguards appropriate to the service and the information involved. The agreement should also limit access to those personnel and subcontractors who need the information to provide the service.
Pay close attention to exceptions in the confidentiality clause. A provider may exclude information that becomes public, was already known, or is independently developed. Those are common concepts, but the terms should not create a back door that allows broad reuse of your proprietary information. The contract should also avoid treating all user submissions as nonconfidential by default.
Security terms deserve more than a passing reference. Ask how the provider handles authentication, encryption, access controls, incident response, and vulnerability management. If the platform connects to company systems, determine what data it can retrieve, whether it can take actions automatically, and how access can be revoked. Integration can turn a simple AI subscription into a meaningful vendor-risk relationship.
A data incident provision should specify when the provider will notify your business of a confirmed or suspected security event involving your information and how the parties will cooperate. Exact requirements vary based on the data, industry, contract, and applicable law. Still, vague language that gives the provider complete discretion over notification can leave a business without the information it needs to respond responsibly.
Are AI providers responsible when their tool makes a mistake?
Usually, the provider’s standard contract limits that responsibility substantially. It may state that the service is provided as is, disclaim warranties of accuracy and fitness for a particular purpose, exclude indirect or consequential damages, and cap damages at fees paid during a short period. These clauses can leave the customer carrying most of the financial exposure from a bad output or service failure.
That does not mean every limitation must be rejected. Risk allocation is a business decision, and the provider may be unwilling to negotiate certain provisions. But the business should understand what it is accepting, particularly if the AI tool supports revenue-generating activity, handles sensitive data, or influences important decisions.
Indemnification deserves separate attention. An indemnity is a promise to defend or reimburse another party for certain third-party claims. Businesses should review whether the provider will indemnify them for claims that the service itself infringes intellectual property rights, and whether that protection has broad exclusions. They should also understand any indemnity they give the provider for their inputs or use of the service. A one-sided indemnity may shift more risk to the customer than the sales conversation suggests.
Internal accountability matters as well. A company remains responsible for its own decisions, representations, and compliance obligations. Contract terms should be paired with policies that identify permitted AI tools, prohibited inputs, required human review, and approval procedures for high-risk uses.
Do we need a separate AI policy if we already have vendor contracts?
Yes. Vendor contracts govern the relationship with the provider, while an internal AI policy guides employee behavior. Both are necessary because many AI problems arise from unauthorized use, not from a negotiated enterprise purchase.
A practical policy can identify approved tools and accounts, describe what information employees may and may not submit, require review of AI-generated work, and direct employees to escalate unusual uses. It should also address who may connect AI tools to internal systems or customer data. The policy does not need to be long to be useful, but it should reflect the way your team actually works.
Training is equally important. Employees should understand that an AI tool is not a private scratchpad simply because it is accessed through a browser. Managers should know when to involve legal, information security, privacy, or compliance personnel before launching a new use case. Revisit the policy as tools, features, and business uses change.
AI agreements should support growth, not create hidden obligations that surface after a problem occurs. If your business is considering an AI vendor, deploying AI internally, or responding to a customer request involving AI, timely legal guidance can help you evaluate the contract and put workable controls in place. Oracle Legal Group can help businesses assess the legal terms alongside the operational realities of the proposed use.
