Across multiple jurisdictions, businesses are increasingly expected to inform customers when they are interacting with AI-driven systems clearly. This is particularly important in contexts where the interaction could influence decisions or appear human-like. The EU AI Act and many U.S. state AI laws, such as California, Colorado, Texas, and others, are setting the legal guardrails for these requirements, and stricter policies and regulations are expected to emerge soon.

Lawsuits across the globe are also testing how undisclosed AI conflicts with wiretap and eavesdropping laws. Put simply, the legal grounds for AI disclosure are solidifying, and businesses need to be prepared.

Evolving Landscape for AI Disclosure

Many digital offerings, especially in the finance and healthcare industries, help customers inform critical decisions. Therefore, regulators and policymakers find it necessary for businesses to be transparent to customers if they are interacting with AI. Here’s how the current landscape around AI disclosure looks:

Explicit disclosure requirements

Many laws now directly require businesses to inform users when they are interacting with AI systems. California’s SB 243, effective January 1, 2026, requires “companion chatbot” operators to provide clear and conspicuous notice that the chatbot is AI and not human. The upcoming Colorado AI law also requires disclosure for consequential decision-making.  Even Texas, which has the most business-friendly AI law, still provides meaningful risk mitigation benefits starting January 1 if risks are evaluated and appropriate notice and transparency are provided.

Critical Lawsuits

Hundreds of lawsuits filed under CIPA and, to a lesser degree, under pen register, wiretap laws, and Title VII, are seen as related to AI usage, where data is being used in ways not contemplated by the original consent or disclosure. A related and growing risk vector is the treatment of chatbots or other AI-assisted tools integrated into websites as potential “eavesdroppers” or “wiretaps.” A famous exercise equipment business recently faced a lawsuit for using a third-party AI chatbot, where the plaintiff alleged that user interactions were recorded and used to improve the provider’s services.

Err on the side of disclosure

Providing clear notice that AI is being used, even where not strictly required, is a low-risk, high-value step. It helps reduce exposure to litigation and aligns with any emerging state-level automated decision-making (ADM) requirements. This way, early transparency can serve as both a compliance measure and a trust signal. Here are some other steps businesses should take to ensure their AI engagement is transparent and reliable for the customers:

• Adopting a unified approach: AI disclosure cannot be treated as a standalone legal checkbox. It requires coordination across legal, product, and engineering teams to ensure that disclosures, data practices, and system behavior are aligned. A fragmented approach increases the risk of gaps both in compliance and in defensibility during litigation.
• Revisit how data is being used: A significant portion of current litigation stems from data being repurposed in AI-driven applications in ways that were not contemplated in the original user consent or disclosures. Businesses should audit whether existing privacy notices adequately cover current AI use cases, especially where data is being used for model training, personalization, or third-party processing.
• Address “drive-by” lawsuit: Certain implementation choices, such as embedding third-party chatbots, session replay tools, or analytics scripts without clear disclosure, are increasingly being targeted in high-volume litigation. Businesses should proactively assess where user interactions may be exposed to third parties and ensure that appropriate notice and consent mechanisms are in place.
• Deidentify data responsibly: Along with disclosure, an important step towards maintaining customer trust is deidentifying their data. Where data is used for AI development or improvement, proper deidentification can reduce legal risk. However, this must be done in a way that preserves the usefulness of the data for model performance. Striking this balance requires both legal oversight and technical rigor.
• Technical Insight: Effective risk mitigation requires more than legal interpretation. Legal teams need a clear understanding of how AI systems actually function, what data they use, how outputs are generated, and where third parties are involved. Technical input is essential to design disclosures and controls that are both accurate and defensible.
• Stay current with regulatory developments: The AI regulatory landscape is evolving rapidly, with new laws, rulemaking processes, and enforcement trends emerging across jurisdictions. Businesses should actively track these developments and assess how upcoming requirements, particularly around disclosure, may impact their operations.

Truyo’s Practical Approach

Truyo AI Governance platform is built to assist with both, addressing the AI Governance regulations and mitigating risks for drive-by lawsuits. As we onboard clients, we take them through a checklist designed to avoid conflicts with AI regulations to reduce the chances of burdensome lawsuits. Our experts also provide significant knowledge from a technical perspective to help the legal teams develop an appropriate risk mitigation strategy.

Reduce Risk Through Clear AI Disclosure

The legal landscape around AI disclosure is still evolving, but the direction is increasingly clear. Regulators are beginning to formalize expectations in specific contexts, while courts are actively testing how existing laws apply to modern AI use cases. At the same time, litigation is moving faster than regulation. Many of the current risks do not arise from clearly defined AI-specific violations, but from gaps between what users were told and how their data is actually being used in AI-driven systems. In this environment, a practical approach would be to provide clear, upfront notice about AI involvement. Businesses do not need to overstate or overcomplicate their disclosures. But they do need to ensure that their use of AI is not hidden behind outdated assumptions about consent, data use, or system behavior.