BIS Summit

Natalia Kaspersky Questions Trustworthiness Criteria for Generative AI

Natalia Kaspersky has expressed serious doubts about applying traditional trust criteria to generative AI systems. She explained that a trusted system must operate within predefined parameters and deliver predictable, repeatable results. Generative AI fails this standard because it produces varying outputs for the same inputs. The enormous scale of modern models makes comprehensive verification practically impossible. Selective testing of individual responses provides no assurance of overall reliability. Kaspersky stressed that creating trusted AI requires joint efforts from AI specialists, information security experts, methodologists, and standards developers rather than discussions alone.

Russian Officials Call for Embedding Fear and Conscience Mechanisms into Generative AI

At the BIS Summit conference on business information security, Deputy Minister of Digital Development Alexander Shoytov argued that generative AI lacks an essential sense of fear toward errors. He proposed building in a technical mechanism that forces models to evaluate consequences, recognize insufficient data, and halt actions when risks are too high. This would address current issues where AI confidently produces hallucinations or executes dangerous commands without human-like risk awareness. Nikolay Lishin, Deputy Head of Russia's FMBA, went further by suggesting models should also incorporate a form of conscience to assess the ethical acceptability of actions. The discussion highlighted risks for AI agents with access to corporate systems, where unchecked behavior could lead to data leaks, file deletions, or infrastructure disruptions. Officials framed these ideas as necessary to create reliable AI that is intelligent yet cautious and morally constrained.