Architecture◆ AI-generated · Sourced
Our approach to AI safety
TL;DR
OpenAI treats AI safety as mission-critical, embedding it structurally across the full lifecycle — from development (GPT-4, GPT-4 Turbo, Claude series) to deployment and usage — via red teaming, content filtering, and governance-integrated architecture.
Safety as Architecture: A Full-Lifecycle Governance Framework
OpenAI’s AI safety practice is not an add-on but a structural requirement embedded across model development, deployment, and application. This framework applies to GPT-4, GPT-4 Turbo, and collaborative safety evaluations of Anthropic’s Claude series — ensuring alignment across model generations and deployment contexts.
Key Practice Dimensions
- Development: Red teaming, adversarial prompt engineering, and interpretability analysis (e.g., transformer attention visualization) to identify and mitigate hallucination, jailbreaking, and value misalignment;
- Deployment: Content filters, real-time API call monitoring, and default deactivation of high-risk capabilities (e.g., code execution, autonomous tool use);
- Usage: Clear responsibility boundaries via Terms of Use, Transparency Reports, and developer documentation — plus enterprise-grade access control (e.g., RBAC integration in Azure OpenAI Service).
Sources (compliance trail)
https://openai.com/index/our-approach-to-ai-safety