Export Auditors as Market-Powered Export Enforcement
Proposes BIS-accredited, for-profit export auditors that inspect shipments to detect AI chip diversion, arguing this would scale US export enforcement beyond the agency's own capacity.
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IAPS
Policy research on advanced AI security and governance. www.iaps.ai
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Building AI Surge Capacity: Mobilizing Technical Talent into Government for AI-Related National Security Crises
Report arguing the US government lacks AI security talent and fast hiring mechanisms for AI-related national security crises, outlining plausible scenarios and a typology of needed expertise.
Policy Options for Preserving Chain of Thought Monitorability
A policy report arguing that competitive pressure could push AI developers away from human-readable chain-of-thought reasoning, undermining monitoring, and proposing governance options to preserve CoT monitorability.
Accelerating AI Data Center Security
Report argues that AI data centers housing frontier compute are attractive targets for nation-state adversaries and recommends building security into them from the outset, especially for facilities outside the West.
Managing Risks from Internal AI Systems
Argues that internal frontier AI systems face theft and sabotage risks plus internal rogue deployment, self-exfiltration and successor sabotage, and recommends security practices, expanded evaluations and government visibility.
Countering AI Chip Smuggling Has Become a National Security Priority: An Updated Playbook for Preventing AI Chip Smuggling to the PRC
Catalogues evidence of large-scale AI chip smuggling into China and recommends policy measures, including a whistleblower program, notification rules, and location verification, to strengthen export-control enforcement.
Asymmetry by Design: Boosting Cyber Defenders with Differential Access to AI
This IAPS report proposes three differential access approaches that let frontier AI developers tilt dual-use cyber capabilities toward defenders, with a framework for choosing among them based on risk.
AI Agent Governance: A Field Guide
A field guide to AI agent governance that maps the risks of more autonomous agents, open governance challenges, and possible interventions.
Chinese AI Safety Institute Counterparts
Identifies Chinese government-linked institutions that could serve as counterparts to the US and UK AI Safety Institutes, based on a systematic review of open sources.
Coordinated Disclosure of Dual-Use Capabilities: An Early Warning System for Advanced AI
Proposes a Coordinated Disclosure of Dual-Use Capabilities process, with a government clearinghouse receiving reports of dangerous AI capabilities so defenders get early warning and time to respond.
AI-Relevant Regulatory Precedents: A Systematic Search Across All Federal Agencies
A systematic search of US federal agencies identifies regulatory case studies, using measures like expertise, risk assessment and enforcement, that could inform how advanced AI should be regulated.
Responsible Scaling: Comparing Government Guidance and Company Policy
Evaluates Anthropic's Responsible Scaling Policy against UK DSIT guidance on responsible capability scaling, arguing its risk thresholds should be lower and grounded in societal risk tolerances from other industries.
Response to the NIST RFI on Auditing, Evaluating, and Red-Teaming AI Systems
IAPS's response to NIST recommends that external actors get access and resources for scrutiny of dual-use foundation models, with incident response plans for dangerous capabilities and defense-in-depth.
Secure, Governable Chips
Proposes on-chip governance: security features on AI chips to mitigate national security risks from dual-use AI while protecting privacy, with concrete U.S. policy recommendations.
Catching Bugs: The Federal Select Agent Program and Lessons for AI Regulation
Examines the US Federal Select Agent Program as a biosecurity precedent and draws lessons for AI regulation, covering development-phase licensing, regulatory expertise, and risk-based versus list-based approaches.
Introduction to AI Chip Making in China
A primer on Chinese AI chip manufacturing, covering the semiconductor industry and US export controls, to help forecast China's indigenous AI compute progress under compute governance.
Safeguarding the Safeguards: How Best to Promote Alignment in the Public Interest
Argues that public-interest institutions should fund alignment work reducing accident and misuse risks, identifies four ways alignment efforts could backfire, and proposes mitigations.
Towards Publicly Accountable Frontier LLMs: Building an External Scrutiny Ecosystem under the ASPIRE Framework
Argues that external scrutiny, including third-party auditing, red-teaming and researcher access, is needed for public accountability over frontier LLM decisions, and sets out six requirements under the ASPIRE framework with policy recommendations.
Preventing AI Chip Smuggling to China
Overview of AI chip smuggling into China, its current and future trajectories, and four recommendations for US policymakers to understand and mitigate large-scale smuggling of AI chips.
International AI Safety Dialogues: Benefits, Risks, and Best Practices
A report offering best practices for international AI safety dialogues to reduce large-scale risks from advanced AI, including harmful outcomes to avoid and promising safety interventions to discuss.
Managing AI Risks in an Era of Rapid Progress
A consensus paper from many researchers outlining risks from advanced AI, including loss of human control, and proposing urgent priorities for AI R&D and governance.
Adapting Cybersecurity Frameworks to Manage Frontier AI Risks: a Defense-in-Depth Approach
Proposes adapting cybersecurity frameworks (functional, lifecycle, and threat-based approaches) to help frontier AI developers and policymakers identify gaps in managing AI risks.
AI Chip Smuggling into China: Potential Paths, Quantities, and Countermeasures
Examines how AI chips could be smuggled into China at scale, estimates likely quantities, and proposes six countermeasures to reduce large-scale export-control evasion.
Open-Sourcing Highly Capable Foundation Models
Argues that open-sourcing some highly capable foundation models may pose extreme risks outweighing benefits, and surveys alternative model-sharing strategies with policy recommendations for developers, standards bodies and governments.
Deployment Corrections: An Incident Response Framework for Frontier AI Models
Proposes an incident response framework and toolkit of deployment corrections that frontier AI developers can use to handle dangerous capabilities or behaviours discovered after model deployment, with recommendations for standardisation.