Businesses deploying artificial intelligence face growing legal exposure when their descriptions of data practices fail to match how their systems operate.

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The analysis identified a shared expectation across the Federal Trade Commission, Securities and Exchange Commission and European authorities. Companies must accurately explain their AI practices to users. For businesses, that means connecting disclosures to actual data flows, vendor arrangements and model development, while addressing changes before they take effect.

The FTC’s concern centers on companies collecting information under one set of promises and subsequently expanding its use without adequate transparency, the analysis said. A business that pledged not to share customer information without consent, for example, could face exposure if it later sends chat transcripts to an outside AI provider for optimization.

The analysis cited FTC guidance warning that adopting more permissive data practices through a “surreptitious, retroactive amendment” to contractual terms or privacy policies may be unfair or deceptive. Adding language after processing practices have changed does not resolve the underlying mismatch between the original promise and subsequent conduct.

Timing and consent, in other words, matter alongside the substance of disclosures. The analysis cautioned against assuming that a routine terms-update email, a website footer link or continued product use establishes adequate acceptance of a material change. Companies should address how customer inputs will be used before collecting them, particularly when those inputs could train generative AI models.

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