Disclosing sensitive personal data during interaction with neural networks often raises concerns about privacy. In an interview with Reedus, Artem Kiryanov, Deputy Chairman of the State Duma Committee on Economic Policy, highlighted that the risk of exposing private information persists even as authorities pursue stronger data protection measures. He noted that a previous incident involved the leakage of ChatGPT users’ personal details, including full names, bank card numbers, email addresses, and billing addresses—information that should never be in circulation. The takeaway is clear: while digital services advance, safeguarding personal data remains a shared responsibility for users and service providers alike.
Kiryanov emphasized that Russian authorities are actively developing new data-protection mechanisms. Yet he cautioned that absolute risk elimination is not feasible. Users should be mindful not to reveal vital data when engaging with digital platforms, particularly with new applications that have not yet undergone thorough testing. He stressed that, when considering a service like ChatGPT, it functions as an external platform beyond direct control, making user vigilance essential. This perspective frames data protection as a broad societal effort rather than a single technical fix.
Beyond the risk of data leakage, the conversation touched on how AI systems can respond to user prompts in unintended ways. Kiryanov pointed out that the ChatGPT model can generate outputs that feel authentic but may mislead users or produce counterfeit results under certain requests. This underscores the importance of critical thinking and verification when relying on AI-generated information, especially for decisions with real-world consequences. It also illustrates the broader challenge of aligning automated systems with user expectations and safety standards.
Meanwhile, developments within Russia include the emergence of a local analogue to ChatGPT. A domestic company, Sistemma, has launched SistemmaGPT, which is already available for business testing. This domestic option represents an effort to provide a locally governed AI tool that adheres to national policies and data-handling practices. In parallel with international AI tools, such initiatives reflect the growing emphasis on building trusted AI ecosystems that balance innovation with privacy and security considerations. Stakeholders in the public, private, and research sectors are watching how these options perform under real-world workloads and regulatory frameworks, as they shape norms for data protection, transparency, and accountability in AI usage.