Systems and methods for securing artificial intelligence systems for edge computing systems

This invention describes a method to make artificial intelligence (AI) models smaller and more secure, particularly for use on small, local devices. It works by repeatedly trimming down a pre-existing AI model. After each trimming step, the system tests the smaller model against various cyberattack scenarios, like attempts to steal its data or trick its decisions, to see how vulnerable it is. Based on these security test results, the trimming strategy is adjusted, and the process continues until the model is both compact and adequately protected against threats.

Why it matters: Filed before the widespread deployment of complex AI models on edge devices made security-aware compression a critical need. The increasing sophistication of AI-specific cyber threats, like those mentioned in the claims, makes this approach more relevant now for robust AI system deployment.

Status: Not in forceListed as no longer active. The specific reason is not in the record we hold.
How hard to build: SpecializedSoftware logic, ML models, cybersecurity expertise

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