Securing AI Algorithmic Insights

Algorithmic insights—the proprietary techniques, methods, and design know-how that materially improve artificial intelligence systems—are among the most strategically valuable assets in the AI ecosystem, yet they are uniquely difficult to protect because they cannot be isolated as a single digital artifact. A new RAND Corporation framework provides organizations with a systematic roadmap for matching security investments to the value of their insights and the capabilities of adversaries.

Algorithmic insights comprise the novel techniques, methods, and design know-how that materially improve artificial intelligence (AI) systems. They differ fundamentally from another prized AI asset—model weights:

Unlike model weights, algorithmic insights generally cannot be isolated as a single digital artifact. Instead, they reside across multiple vectors simultaneously:

• Source code
• Documentation
• Communications
• Experimental systems
• Human expertise and tacit knowledge

Some insights 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.

Securing AI Algorithmic Insights

RAND researchers extended the framework from their prior report, Securing AI Model Weights, to cover algorithmic insights specifically. The core contributions:

Attack Taxonomy — 44 attack vectors mapped across nine categories, with an emphasis on the broad and human-centered nature of the attack surface. Cyber controls alone are insufficient because insights can leak through non-technical channels—lines of code, words spoken, or a glance at a screen.

Five Insight Security Levels (ISLs) — Cumulative security postures matched to five levels of adversary operational capacity:

Compartmentalization as the Organizing Principle — Reducing the consequences of compromise by:

• Limiting where complete insights reside
• Restricting access to personnel with a need to know
• Progressively strengthening separation between insight domains

The framework is conditional rather than prescriptive: it describes what security posture would be likely required to protect a specified insight against a specified class of adversary, while leaving organizations to determine which insights warrant protection.

Meaningful improvements are possible at every ISL. Lower levels extend established enterprise controls and need-to-know practices. Higher levels demand increasingly isolated systems and facilities, more-intensive personnel security, and substantial restrictions on ordinary research practices.

Protecting insights against the highest-capability threats entails major organizational trade-offs, including:

• Isolated physical facilities
• Intensive personnel vetting
• Compartmentalized supply chains
• Restrictions on remote work, travel, and communication
• Potential government support for measures that may take years to prepare

The framework does not determine which insights should be protected. Organizations must weigh each insight's:

1. Strategic or commercial value
2. Potential for misuse (including lowering barriers to dangerous capabilities)
3. Cost of protection—which rises sharply at ISL4–5

There is no one-size-fits-all answer. The same framework supports both modest baseline protections for lower-value insights and near-vault-level security for those that truly warrant it.

• The attack surface is broad and human-centered — insights reside in code, documents, devices, communications, and people's knowledge; cyber controls alone are insufficient.
• Compartmentalization is the central organizing principle — reduce compromise impact by limiting where complete insights reside and restricting access on a need-to-know basis.
• Every ISL offers meaningful protection — even ISL1 and ISL2 meaningfully extend security fundamentals without requiring radical organizational changes.
• ISL4 and ISL5 entail major trade-offs — protecting against state-level adversaries requires isolated facilities, intensive vetting, supply chain compartmentalization, and restrictions on normal research practices.
• Organizations must decide what to protect — the framework maps options but leaves the value-versus-cost judgment to organizations themselves.

Source: 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