A recent study offered important insight into user interactions with AI chatbots. According to Stanford, AI companies might be pulling conversations for model training by default. Such revelations reiterate the need for companies offering or engaging with AI-driven chatbots to eliminate any doubts regarding the usage of their conversation data, including sensitive information like health reports, biometrics, children’s chats, and more.  

Ambiguous responses to such issues not only raise legal concerns but also hit the brand value by calling the business’s motivations into scrutiny. Therefore, let us explore the different regulatory frameworks governing chatbot data usage to understand how businesses should ensure compliance and caution while pushing their AI projects. 

Around the World in Chatbot Laws 

Naturally, the global consensus around the usage of conversational data and chat inputs demands strict governance. Even though many governments don’t yet target chatbots specifically, they are applying broader data-privacy and AI-transparency laws that implicate this domain. 

California: The Senate Bill 53 (“Transparency in Frontier Artificial Intelligence Act”) that was signed into law on September 29, 2025, requires developers of “frontier” (large-capacity) models to publish transparency reports and manage “catastrophic risk.” The California Consumer Privacy Act / California Privacy Rights Act (CCPA/CPRA) also offer broad consumer-data laws requiring transparency, data deletion, access rights. These are applicable where conversation data may identify or be tied to individuals. 

European Union: The EU Artificial Intelligence Act (AI Act) and the General Data Protection Regulation (GDPR) broadly cover data use, transparency, profiling, and high-risk systems, which can include chatbot models.  

Italy: Italy’s DPA Garante has been aggressive on chatbots (e.g., 2023 halt + subsequent reinstatement; €15m fine in 2024 for GDPR violations), underscoring that training on personal data without adequate legal basis/notice is sanctionable. 

France: CNIL has issued specific recommendations for AI systems and generative AI, clarifying GDPR duties (lawful basis, security, data minimization, annotation conditions). The SREN Law criminalizes non-consensual deepfakes and tightens platform duties and makes its relevant where chatbot outputs or training data involve manipulated media.  

China: Interim Measures for Generative AI (2023) and Deep Synthesis Provisions (2023) impose content labeling, security assessments, and provider accountability.  Personal data processing remains governed by PIPL principles. 

India: India’s DPDP Act and 2025 draft rules (to operationalize the Act) will govern consent, purpose limitation, and data principal rights; industry has already asked for clarity/exemptions for AI training, signaling scrutiny for chatbot data. 

Managing the Responsible Advantage of Chatbots 

AI-driven chatbots deliver measurable value by cutting support costs, improving customer satisfaction, and generating data that can refine marketing, operations, and product design. However, the same data that makes chatbots smart can also be sensitive, especially when it includes personal, financial, or health-related information.  

• Negotiate “no-training” clauses: For sensitive industries like healthcare, finance, or government, insist on service tiers where data isn’t used to retrain the model. Ensure this is contractually binding. 
• Review data localization and transfer practices: Know where your chatbot’s data centers are located and whether any cross-border transfers occur. This is critical under GDPR, DPDP, and China’s data security laws. 
• Vendor risk management framework: Evaluate the AI provider like any critical third party, security certifications, incident history, compliance with ISO 42001/27001, and independent audits. 
• Implement API-level controls: If the chatbot connects with your CRM or HR platforms, ensure those integrations use secure tokens and limited permissions. Audit API calls to prevent unauthorized data extraction. 
• Monitor output for compliance: Chatbots can inadvertently generate discriminatory or inaccurate responses. Deploy monitoring tools or human review for regulated interactions such as credit, hiring, or health advice. 

A large number of businesses also leverage third-party AI chatbots. These are convenient solutions for modernizing customer engagement. They’re fast to deploy, integrate easily with CRM and HR systems, and promise round-the-clock service at a fraction of the cost of live support.  

• Map your chatbot data flows: Identify what data is being captured, where it is stored, how long it’s retained, and whether it’s being shared or used for training. Treat chatbot transcripts as structured data assets subject to privacy and security controls. 
• Define “allowable data”: Establish clear internal rules for what can and cannot be entered into a chatbot for both employees and customers. Use data loss prevention (DLP) tools and filters to prevent entry of PII, health data, or financial identifiers. 
• Segment and anonymize conversation logs: If you analyze chat data for performance insights, anonymize or pseudonymize it before storage. Apply role-based access controls so only authorized teams can view sensitive content. 
• Embed privacy into chatbot design. Default to minimal data collection and implement visible notices explaining how conversations are used. Ensure users can opt out or delete their data where applicable. 
• Conduct regular audits and risk assessments: Test for unintended data retention, shadow storage, or integrations that may bypass compliance systems. Review against GDPR, CCPA, or sector-specific requirements. 

Consent in Data Training

Earlier this year, Peloton faced a class-action lawsuit alleging that its use of customer data to train AI models violated user privacy and consent agreements. The case underscores a fundamental principle that applies to any AI-driven system. Just because data is lawfully obtained doesn’t mean it can be reused freely for AI training. This is particularly relevant for conversational AI, where chat transcripts and uploaded files can contain personal or even sensitive information. 

If you are a company offering AI chatbot services, you have a direct responsibility to ensure that users explicitly understand and agree to how their conversations are used. It’s equally important to separate consent flows, one for essential service delivery and another for optional product improvement, so users can access your chatbot without having their data repurposed for training. 

If you are a company leveraging third-party chatbots, you must review and document the vendors’ consent mechanisms, ensuring that data usage terms are explicit and protective of users. Beyond upstream controls, you also need downstream consent: if your employees or customers interact through your chatbot interface, your own privacy notice should disclose any data sharing or reuse risks.  

Scramble: Truyo’s Privacy-First Solution for Safe AI Training 

All said and done, it is one of the biggest challenges in AI governance today to train models on real-world data without exposing sensitive or regulated information. Truyo’s Scramble capability addresses this head-on. Scramble anonymizes and obfuscates personal data before it’s used for model development or analytics, allowing organizations to train and test AI responsibly. 

• Data anonymization at scale: Scramble masks identifiers such as names, emails, and biometrics while preserving data integrity, so models can still learn from the structure and patterns without compromising privacy. 
• Context-aware redaction: It uses intelligent detection to identify and remove PII, health data, and other sensitive fields across structured and unstructured datasets, including conversational text. 

The Future of Responsible Innovation 

The global rise of chatbots reflects the anticipation towards the next stage in how businesses interact, transact, and learn from their audiences. At the same time, this momentum indicates a necessary tightening of laws as well. Governments and regulators cannot ignore a technology that collects and interprets human language at scale, especially when it carries personal, behavioral, or biometric data. The businesses that thrive in this new era will be those that innovate boldly while embedding transparency and governance into every deployment.