Securing Research Infrastructure for Advanced AI: OpenAI’s Commitment to Safety
OpenAI, a leading artificial intelligence (AI) research organization, has outlined its current architecture and operations for securing research infrastructure to support the safe training of frontier models at scale. This includes measures designed to protect sensitive model weights within a secure environment for AI innovation.
Threat Model
Research infrastructure presents a unique security challenge due to the diverse and rapidly evolving nature of workloads required for experimentation. OpenAI has identified several important types of assets that are essential to protect, including unreleased model weights, which represent core intellectual property and need to be safeguarded from unauthorized release or compromise.
Architecture
OpenAI has created a series of research environments dedicated to the development and security of frontier models. The research infrastructure must support the protection of model weights, algorithmic secrets, and other sensitive assets used for developing frontier models by shielding them against unauthorized exfiltration and compromise. At the same time, researchers must have sufficient access to resources and the underlying compute infrastructure in order to be productive and efficient.
Protecting Model Weights
Protecting model weights from exfiltration from the research environment requires a defense-in-depth approach that encompasses multiple layers of security. These bespoke controls are tailored to safeguard OpenAI’s research assets against unauthorized access and theft, while ensuring they remain accessible for research and development purposes. These measures include:
- Authorization: Access grants to research storage accounts containing sensitive model weights require multi-party approvals.
- Access: Storage resources for research model weights are private-linked into OpenAI’s environment to reduce exposure to the Internet and require authentication and authorization through Azure for access.
- Egress Controls: OpenAI’s research environment uses network controls that allow egress traffic only to specific predefined Internet targets. Network traffic to hosts not on the allowlist is denied.
- Detection: OpenAI maintains a mosaic of detective controls to backstop this architecture. Details of these controls are intentionally withheld.
Auditing and Testing
OpenAI uses internal and external red teams to simulate adversaries and test its security controls for the research environment. The organization has had its research environment penetration tested by a leading third-party security consultancy, and its internal red team performs deep assessments against its priorities.
Research and Development on Future Controls
Securing increasingly advanced AI systems will require continuous innovation and adaptation. OpenAI is at the forefront of developing new security controls, as outlined in its “Re-imagining Secure Infrastructure for Advanced AI” blog post. The organization’s commitment to research and development ensures that it stays ahead of emerging threats and continues to enhance the security of its AI infrastructure.
Join the effort
At OpenAI, the work to develop and protect advanced AI continues every day. The organization invites the AI and security communities to join it in this mission. By applying for its Cybersecurity Grant Program or joining its team, your contribution can help shape the future of AI security.
Learn more about OpenAI’s efforts to secure research infrastructure for advanced AI at https://openai.com/index/securing-research-infrastructure-for-advanced-ai/.
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