Get the weekly research letter
Companies change their terms quietly. We read every version and catch what actually changed. One email a week on the changes that matter and what they mean. No account.
The document states that safety evaluation results described across system cards were conducted in an offline setting unless specifically noted otherwise, meaning they do not reflect live or production deployment conditions.
This analysis describes what OpenAI's agreement states, permits, or reserves. It does not constitute a legal determination about enforceability. Regulatory applicability and practical outcomes may vary by jurisdiction, enforcement context, and individual circumstances. Read our methodology
This disclosure establishes that the safety evaluation results described in the system card are based on offline testing rather than production deployment conditions, which is a material methodological limitation for compliance teams assessing the real-world applicability of the stated safety posture.
The document states that evaluation results reflect offline testing conditions, which means the described safety performance has not been fully validated under live deployment scenarios. API users and deployers should account for this methodological scope when assessing the system card's safety disclosures.
Cross-platform context
See how other platforms handle Offline Evaluation Scope Disclosure and similar clauses.
Compare across platforms →Monitoring
OpenAI has changed this document before.
Receive same-day alerts, structured change summaries, and monitoring for up to 25 platforms.
"Except where noted, the results in system cards describe evaluations we ran in an offline setting.Excerpt from OpenAI's GPT-5.5 System Card
(1) REGULATORY LANDSCAPE: The offline evaluation disclosure may engage with EU AI Act post-market monitoring obligations, which require ongoing safety assessment under real-world deployment conditions. Regulators assessing whether pre-deployment evaluations are sufficient may consider the offline testing scope as a relevant limitation. (2) GOVERNANCE EXPOSURE: Medium. The offline evaluation scope is a documented methodological limitation that compliance teams should record in AI risk registers. Where regulatory frameworks require production-validated safety evaluations, this disclosure indicates a gap that may require supplementary post-deployment monitoring. (3) JURISDICTION FLAGS: EU deployers operating under EU AI Act post-market monitoring obligations face the most direct exposure. Organizations in safety-critical sectors should assess whether offline evaluation results are sufficient for their internal risk governance requirements. (4) CONTRACT AND VENDOR IMPLICATIONS: Enterprise agreements that incorporate safety evaluation results by reference should specify whether offline or production evaluation results are intended. Procurement teams should assess whether the offline evaluation scope is consistent with contractual safety representation requirements. (5) COMPLIANCE CONSIDERATIONS: Compliance teams should implement post-deployment monitoring processes to supplement the offline evaluation baseline described in the system card. Documentation of this limitation should be included in AI incident response planning.
This disclosure establishes that the safety evaluation results described in the system card are based on offline testing rather than production deployment conditions, which is a material methodological limitation for compliance teams assessing the real-world applicability of the stated safety posture.
The document states that evaluation results reflect offline testing conditions, which means the described safety performance has not been fully validated under live deployment scenarios. API users and deployers should account for this methodological scope when assessing the system card's safety disclosures.
No. ConductAtlas is an independent monitoring service. We are not affiliated with, endorsed by, or sponsored by OpenAI.