System Card

A system card is an extension of the model card concept that documents an end-to-end AI system — including the model, safety layers, deployment context, and known risks — rather than just the underlying model. The term was popularized by OpenAI's system cards for GPT-4 (2023), GPT-4V (2023), Sora (2024), and o1 (2024), each running 30-100+ pages of detailed evaluation, red-team findings, and risk analysis. Anthropic publishes similar documentation for Claude releases, Google for Gemini, and Meta for Llama. System cards typically cover capability evaluations (benchmarks, qualitative assessments), risk evaluations (CBRN, cybersecurity, persuasion, autonomy), red-team summaries, mitigations applied, and residual risks. The EU AI Act's documentation requirements for high-risk AI systems are essentially calls for system cards. AI governance teams treat system cards as the primary AI compliance documentation for deployed AI systems, supplementing them with internal evaluations of fitness for the specific enterprise context. System cards have become a key transparency mechanism in the AI industry, allowing comparison across providers.

System-card-documented deployments with Centralpoint: Centralpoint maintains system-level documentation across the LLM stack — covering model selection, safety layers, audit trails, and operational context — for AI compliance readiness. Tokens are metered per skill, prompts stay local, and documented chatbots deploy through one line of JavaScript on any portal.


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