Algorithmic insights—the techniques, methods, and design know-how that materially improve artificial intelligence (AI) systems—can confer substantial commercial and strategic advantages. Unlike model weights, algorithmic insights generally cannot be isolated as a single digital artifact. They reside across source code, documentation, communications, experimental systems, and human expertise, and some can be conveyed through only a brief conversation or an observed screen. Their unauthorized disclosure could erode technological leads and, in some cases, lower barriers to dangerous AI capabilities.
This report adapts the framework developed in Securing AI Model Weights to algorithmic insights. The authors identify 44 attack vectors across nine categories and propose five cumulative insight security levels (ISLs) matched to five levels of adversary operational capacity. The framework is conditional rather than prescriptive: It describes the security posture likely required to protect a specified insight against a specified class of adversary while leaving organizations to determine which insights warrant protection.
The report identifies compartmentalization as the central organizing principle for insight security. Lower security levels largely extend established enterprise controls and need-to-know practices. Higher levels require increasingly isolated systems and facilities, more-intensive personnel security, and substantial restrictions on ordinary research practices.
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