In this report, the authors extend a framework to provide the first systematic roadmap for protecting algorithmic insights, the novel techniques, methods, and design know-how that materially improve artificial intelligence (AI) systems, which are among the most strategically valuable assets in the AI ecosystem. With this new framework, organizations can make informed decisions about which insights warrant which security investments.

Securing AI Algorithmic Insights

Asher Brass-Gershovich, Rachel Steratore, Wesley Hurd, Henry Alexander Bradley, Anjay Friedman, Sella Nevo

ResearchPublished Jul 20, 2026

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.

Key Takeaways

The attack surface is broad and human-centered.

  • Algorithmic insights can reside in code, documents, communications, devices, and people’s knowledge. Some can be conveyed through only a few lines of code, a brief conversation, or an observed screen, so cyber controls alone are insufficient.

Compartmentalization is the central organizing principle for insight security.

  • Organizations can reduce the consequences of compromise by limiting where complete insights reside, restricting access to personnel with a need to know, and progressively strengthening separation between insight domains.

Meaningful improvements are possible at every ISL.

  • ISL1 and ISL2 largely extend security fundamentals and established enterprise practices. ISL3 adds formal classification and compartmentalization, insider-risk controls, network segmentation, and supply chain assurance.

ISL4 and ISL5 entail major organizational trade-offs.

  • Protecting insights against highly capable state adversaries could require isolated facilities, intensive personnel vetting, compartmentalized supply chains, and restrictions on remote work, travel, and communication. Some measures might require government support and years of preparation.

The appropriate level depends on the insight and adversary.

  • The framework does not determine which insights should be protected; organizations must weigh each insight’s value and potential for misuse against the costs of protection.

Topics

Document Details

Citation

Chicago Manual of Style

Brass-Gershovich, Asher, Rachel Steratore, Wesley Hurd, Henry Alexander Bradley, Anjay Friedman, and Sella Nevo, Securing AI Algorithmic Insights. Santa Monica, CA: RAND Corporation, 2026. https://www.rand.org/pubs/research_reports/RRA4685-1.html.
BibTeX RIS

Research conducted by

This research was independently initiated and conducted by the Center on AI, Security, and Technology within RAND Global and Emerging Risks using income from operations and gifts and grants from philanthropic supporters.

This publication is part of the RAND research report series. Research reports present research findings and objective analysis that address the challenges facing the public and private sectors. All RAND research reports undergo rigorous peer review to ensure high standards for research quality and objectivity.

This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited; linking directly to this product page is encouraged. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial purposes. For information on reprint and reuse permissions, please visit www.rand.org/pubs/permissions.

RAND is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.