What Should Frontier AI Developers Disclose About Internal Deployments?

• URL: http://arxiv.org/abs/2604.23065v1
• Date: Fri, 24 Apr 2026 23:28:38 GMT
• Title: What Should Frontier AI Developers Disclose About Internal Deployments?
• Abstract summary: It is essential that developers provide evidence that internally deployed models are safe.<n>We identify key information that companies should disclose about internally deployed models across four categories.<n>Our framework could be used by developers to inform both public transparency documents, such as model system cards, and private periodic reports required under emerging frontier AI regulation.
• Score: 0.6113106953880909
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Frontier AI developers are increasingly deploying highly capable models internally to automate AI R&D, but these deployments currently face limited external oversight. It is essential, therefore, that developers provide evidence that internally deployed models are safe. While recent work has highlighted the risks of internal deployments and proposed broad approaches to transparency and governance, there remains little guidance on the specific information developers should disclose about them. We address this gap by identifying key information that companies should disclose about internally deployed models across four categories: capabilities, usage, safety mitigations, and governance. For each category, we analyse the key benefits and limitations of disclosure and consider how disclosure-related risks can be mitigated. Our framework could be used by developers to inform both public transparency documents, such as model system cards, and private periodic reports required under emerging frontier AI regulation.

Related papers

• Risk Reporting for Developers' Internal AI Model Use [0.0]
  This guide provides a harmonized standard for companies to produce internal use risk reports.<n>It is addressed primarily to evaluation and safety teams at frontier AI developers.<n>We structure the reporting framework around two threat vectors -- autonomous AI misbehavior and insider threats.
  arXiv  Detail & Related papers  (2026-04-27T20:07:09Z)
• Beyond the Binary: A nuanced path for open-weight advanced AI [0.0]
  Open-weight advanced AI models are systems whose parameters are freely available for download and adaptation.<n>This report moves beyond the binary framing of open'' versus closed'' AI.<n>We find significant disparities in safety practices across developers and jurisdictions.<n>We propose a tiered, safety-anchored approach to model release, where openness is determined by rigorous risk assessment and demonstrated safety.
  arXiv  Detail & Related papers  (2026-02-23T10:26:23Z)
• Frontier AI Risk Management Framework in Practice: A Risk Analysis Technical Report v1.5 [61.787178868669265]
  This technical report presents an updated and granular assessment of five critical dimensions: cyber offense, persuasion and manipulation, strategic deception, uncontrolled AI R&D, and self-replication.<n>This work reflects our current understanding of AI frontier risks and urges collective action to mitigate these challenges.
  arXiv  Detail & Related papers  (2026-02-16T04:30:06Z)
• Aegis: Towards Governance, Integrity, and Security of AI Voice Agents [52.7512082818639]
  We propose Aegis, a framework for the governance, integrity, and security of voice agents.<n>We evaluate the framework through case studies in banking call centers, IT Support, and logistics.<n>We observe systematic differences across model families, with open-weight models exhibiting higher susceptibility.
  arXiv  Detail & Related papers  (2026-02-07T05:51:36Z)
• Frontier AI Auditing: Toward Rigorous Third-Party Assessment of Safety and Security Practices at Leading AI Companies [57.521647436515785]
  We define frontier AI auditing as rigorous third-party verification of frontier AI developers' safety and security claims.<n>We introduce AI Assurance Levels (AAL-1 to AAL-4), ranging from time-bounded system audits to continuous, deception-resilient verification.
  arXiv  Detail & Related papers  (2026-01-16T18:44:09Z)
• International AI Safety Report 2025: Second Key Update: Technical Safeguards and Risk Management [115.92752850425272]
  Second update to the 2025 International AI Safety Report assesses new developments in general-purpose AI risk management over the past year.<n> examines how researchers, public institutions, and AI developers are approaching risk management for general-purpose AI.
  arXiv  Detail & Related papers  (2025-11-25T03:12:56Z)
• "We are not Future-ready": Understanding AI Privacy Risks and Existing Mitigation Strategies from the Perspective of AI Developers in Europe [56.1653658714305]
  We interviewed 25 AI developers based in Europe to understand which privacy threats they believe pose the greatest risk to users, developers, and businesses.<n>We find that there is little consensus among AI developers on the relative ranking of privacy risks.<n>While AI developers are aware of proposed mitigation strategies for addressing these risks, they reported minimal real-world adoption.
  arXiv  Detail & Related papers  (2025-10-01T13:51:33Z)
• AI Behind Closed Doors: a Primer on The Governance of Internal Deployment [33.99253912746621]
  Internal deployment is a key source of benefits and risks from frontier AI systems.<n>This report aims to address this absence by priming a conversation around the governance of internal deployment.<n>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.
  arXiv  Detail & Related papers  (2025-04-16T15:21:13Z)
• AgentDAM: Privacy Leakage Evaluation for Autonomous Web Agents [66.29263282311258]
  We introduce a new benchmark AgentDAM that measures if AI web-navigation agents follow the privacy principle of data minimization''<n>Our benchmark simulates realistic web interaction scenarios end-to-end and is adaptable to all existing web navigation agents.
  arXiv  Detail & Related papers  (2025-03-12T19:30:31Z)
• Risks and Opportunities of Open-Source Generative AI [64.86989162783648]
  Applications of Generative AI (Gen AI) are expected to revolutionize a number of different areas, ranging from science & medicine to education. The potential for these seismic changes has triggered a lively debate about the potential risks of the technology, and resulted in calls for tighter regulation. This regulation is likely to put at risk the budding field of open-source generative AI.
  arXiv  Detail & Related papers  (2024-05-14T13:37:36Z)
• Deployment Corrections: An incident response framework for frontier AI models [0.0]
  This paper explores contingency plans for cases where pre-deployment risk management falls short. We describe a toolkit of deployment corrections that AI developers can use to respond to dangerous capabilities. We recommend frontier AI developers, standard-setting organizations, and regulators should collaborate to define a standardized industry-wide approach.
  arXiv  Detail & Related papers  (2023-09-30T10:07:39Z)
• Frontier AI Regulation: Managing Emerging Risks to Public Safety [15.85618115026625]
  "Frontier AI" models could possess dangerous capabilities sufficient to pose severe risks to public safety. Industry self-regulation is an important first step. We propose an initial set of safety standards.
  arXiv  Detail & Related papers  (2023-07-06T17:03:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.