Natalia Kaspersky Questions Trustworthiness Criteria for Generative AI
Natalia Kaspersky has cast doubt on whether generative AI can ever meet conventional standards of trustworthiness. Speaking at the BIS Summit in an interview with Anti-Malware.ru correspondent Olesya Afanasyeva, the chairwoman of ARPP Domestic Software described the very notion of trust for such systems as an unresolved puzzle.
According to Kaspersky, a trusted system must function within strictly defined parameters and produce consistent, predictable outcomes. Generative AI does not satisfy this requirement because it cannot guarantee identical results on repeated queries. “The criterion of trustworthiness as applied to generative artificial intelligence remains a mystery to me,” she stated.
The core difficulty lies in the sheer scale of contemporary models. With billions of parameters and vast training datasets, exhaustive validation becomes infeasible. While individual answers or scenarios can be tested, such partial checks do not demonstrate the reliability of the entire system.
This situation creates a practical dilemma for both business and government: how to rely on technology whose behavior cannot be fully verified. Kaspersky noted that the task of building trusted AI has been officially set, yet no concrete methodology currently exists. She called for coordinated work among AI researchers, information security professionals, methodologists, and standards developers to address the gap.
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