AI Model Security: Threats to the Model Itself, Not Just the Data

AI model security focuses on safeguarding trained models’ weights, architecture, and query behavior in addition to protecting the data used to train them, because models represent valuable intellectual property and can potentially reveal sensitive training information. Key confidentiality threats include model extraction, in which attackers recreate a model through repeated queries; model inversion, which attempts to infer training records from outputs; and model theft, involving direct acquisition of model files through compromised storage, pipelines, or insider access. Risks occur throughout training, storage, deployment, and inference, with distributed and federated learning adding exposure through gradients and model updates that may leak underlying data. Recommended protections include secure aggregation, encrypted gradient exchange, multiparty computation, fully homomorphic encryption, confidential computing, differential privacy, access controls, rate limits, and monitoring for suspicious queries. The text argues that these measures should be combined according to deployment needs, particularly for collaborative and regulated applications, and presents Duality’s platform as using protected hardware environments and privacy-enhancing technologies to secure both data and models across their lifecycle.

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