LegalVision warns firms on single AI provider risks

LegalVision has warned Australian businesses about the legal and commercial risks of relying too heavily on a single AI provider, after recent limits on access to advanced AI models exposed the issue.

Lauren McKee, Practise Leader at LegalVision, said many organisations have woven third-party AI tools into core operations without contracts, governance rules or backup plans. That leaves them exposed if a provider changes product terms, alters model performance or restricts access.

According to the firm's assessment, AI tools are now used well beyond experimental projects in many companies. It identified software development, customer service and marketing automation as areas where businesses increasingly depend on external platforms for routine work.

McKee said that shift means AI should be treated less like an optional software purchase and more like a critical supplier relationship. The legal exposure extends beyond internal disruption if a service becomes unavailable or less effective.

"The main risk with any single provider dependency is losing control when something outside your business changes," said Lauren McKee, Practise Leader at LegalVision.

"If the AI product is updated, the provider's terms shift, or regulation catches up with how the tool works, you may find the platform you've built critical processes around is no longer fit for purpose, with no easy way out."

One concern is how businesses sign supplier agreements for AI tools. McKee said key contract terms are often overlooked, even though they can determine how much risk sits with the customer rather than the vendor.

She said businesses should review several points before adopting a service for important work. These include whether the provider can use prompts or outputs to train models, how confidential information is handled, whether privacy obligations are met, what service levels are promised, and whether the supplier is trying to limit its liability if problems arise.

Exit rights are another issue. Businesses may need to retrieve data and move to another provider if a tool changes or no longer suits their operations, and weak contract language can make that difficult.

"Check whether the provider can use your prompts or outputs to train their models, how they handle confidentiality and data security, whether they actually comply with privacy laws, what service levels they are committing to, and whether they are trying to unreasonably limit their liability if something goes wrong. Exit rights also matter. If the product changes or stops working for your business, you need to be able to retrieve your data and transition without being locked in," McKee said.

Contract gaps

The risk becomes more acute when a business builds its own product or service on top of a third-party AI model. If that underlying access is restricted, interrupted or degraded, the business may still face claims from its own customers for failing to deliver what it promised.

In that scenario, a supplier outage can shift from an operational problem to a contractual and legal one. McKee said many vendor contracts do not provide enough protection against that kind of exposure.

"Useful protections include service level commitments, notice of material changes, restrictions on model changes that affect performance, data portability, prompt and output ownership terms, confidentiality and privacy obligations, cyber incident notification, business continuity commitments and meaningful termination rights. Liability caps should not quietly exclude the very losses most likely to occur, such as privacy breaches, IP claims or service disruption," McKee said.

Governance plans

Beyond contracts, McKee said businesses should put internal controls around AI use before tools become deeply embedded in daily operations. Any AI system used in a core process should be supported by documented fallback arrangements, tested recovery plans and defined escalation procedures.

That also means limiting unsanctioned use by staff. LegalVision recommended a controlled set of approved AI tools, backed by rules on what each tool can and cannot be used for, rather than allowing individual teams to build critical workflows on unapproved services.

McKee also called for a broader governance framework covering policies, registers of approved AI systems, risk assessments for use cases, human review requirements, data handling rules, incident reporting processes and regular testing.

"Organisations can reduce reliance by designing their AI use around business processes, not one provider's product. SMEs should also set approval rules so staff cannot independently build critical workflows on unapproved tools," McKee said.

The warning comes as businesses face a market in which AI access, pricing, performance and terms of use can shift quickly. Those changes can stem from regulation, commercial decisions, safety concerns or geopolitical factors beyond a customer's control.

For companies that rely on AI in customer-facing or operational roles, the central issue is no longer just whether a tool works well today. It is whether the organisation can continue operating if the supplier changes the rules tomorrow.

"AI access can change quickly, and businesses should not build essential operations on assumptions they do not control," McKee said.