# AI Behind Closed Doors: a Primer on The Governance of Internal Deployment

arXiv: https://arxiv.org/abs/2504.12170 · HTML: https://mlsanity.com/paper/2504.12170/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2025.

## Built on by (most cited first, 8 of 8)

- [The 2025 AI Agent Index: Documenting Technical and Safety Features of Deployed Agentic AI Systems](https://mlsanity.com/paper/2602.17753/) (2026)
- [Measuring AI R&D Automation](https://mlsanity.com/paper/2603.03992/) (2026)
- [Technical Requirements for Halting Dangerous AI Activities](https://mlsanity.com/paper/2507.09801/) (2025)
- [Internal Deployment Gaps in AI Regulation](https://mlsanity.com/paper/2601.08005/) (2026)
- [Societal Capacity Assessment Framework: Measuring Resilience to Inform Advanced AI Risk Management](https://mlsanity.com/paper/2509.22742/) (2025)
- [Password-Activated Shutdown Protocols for Misaligned Frontier Agents](https://mlsanity.com/paper/2512.03089/) (2025)
- [Risk Reporting for Developers' Internal AI Model Use](https://mlsanity.com/paper/2604.24966/) (2026)
- [MOLE: Detecting Insider Threats in AI Agents](https://mlsanity.com/paper/2609.06966/) (2026)

## Abstract (arXiv)

The most advanced future AI systems will first be deployed inside the frontier AI companies developing them. According to these companies and independent experts, AI systems may reach or even surpass human intelligence and capabilities by 2030. Internal deployment is, therefore, a key source of benefits and risks from frontier AI systems. Despite this, the governance of the internal deployment of highly advanced frontier AI systems appears absent. This report aims to address this absence by priming a conversation around the governance of internal deployment. It presents a conceptualization of internal deployment, learnings from other sectors, reviews of existing legal frameworks and their applicability, and illustrative examples of the type of scenarios we are most concerned about. Specifically, it discusses the risks correlated to the loss of control via the internal application of a misaligned AI system to the AI research and development pipeline, and unconstrained and undetected power concentration behind closed doors. The report culminates with a small number of targeted recommendations that provide a first blueprint for the governance of internal deployment.