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Prevenzione dell'Uso Improprio
Misuse prevention is the set of technical and organizational measures designed to stop an AI system from being used for purposes other than intended and potentially harmful: generating malware, producing fraud material, mass-producing disinformation, assisting illegal activity. It differs from model safety in the strict sense because it concerns not just the model's own behavior, but the entire system around it: user authentication, usage limits, monitoring of anomalous usage patterns.
Typical measures include rate limits on requests to slow automated abuse attempts, identity verification for access to more powerful features, monitoring of usage patterns indicating likely systematic abuse rather than occasional use, and the ability to revoke access for accounts that repeatedly violate policy. It is often combined with safety filters applied to a single prompt, but operates at a broader level, looking at a user's history and behavior over time.
It is particularly relevant for models with capabilities potentially dangerous at scale, such as code generation, voice synthesis that clones real voices, or models able to process sensitive technical information: here misuse prevention does not aim to block every single risky request, but to make systematic abuse economically and operationally difficult.
The concept gained public prominence with the spread of generative models able to produce realistic text, voice, and images at scale from 2022 onward, when abuse risks moved from theoretical to concretely documented.
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