How do other major AI providers handle attempts to generate CSAM and how do their policies compare to OpenAI’s?

1. Industry standards: bans, detection, and “safety by design” are the baseline

Most guidance and legal advice for companies urges explicit prohibitions on CSAM in terms of service, use of hash‑based detection that incorporates NCMEC databases, and embedding child‑safety measures across the AI lifecycle — practices described as “safety by design” and recommended by NGOs and legal firms alike ^{[2] [4] [5]}.

2. Technical mitigations: model curation, dataset vetting, and forensic detection

Researchers and regulators call for separating child-related content from adult sexual content in training datasets and for aggressive vetting because popular model datasets were found to contain known CSAM, which enables generation of illegal imagery if left unchecked ^{[5] [1]}. Legal and industry briefs recommend training moderation systems on CSAM hash databases to detect and remove illegal images rapidly and to preserve evidence for reporting to law enforcement ^{[2] [6]}.

3. Enforcement gaps: policy on paper vs platform reality

Investigations find that policies banning AI‑generated CSAM often exist on paper but are inconsistently enforced by messaging apps, social platforms, and payment systems, enabling circulation and even monetization of synthetic child‑abuse imagery in some ecosystems ^{[1] [7]}. Europol and coordinated law enforcement actions have nonetheless disrupted distribution networks and made arrests tied to AI‑generated CSAM, showing that enforcement can work but requires cross‑platform coordination ^[3].

4. Legal context shapes corporate responses but leaves gray areas

U.S. case law has historically narrowed criminal statutes around “virtual” child pornography to content indistinguishable from real victims, a standard that complicates how companies and courts treat fully synthetic CSAM and has driven legislative attention and proposed reporting laws like the REPORT Act ^[2]. Multiple sources assert that creating or sharing AI‑generated CSAM may be illegal under federal law and that policymakers across jurisdictions are updating frameworks to capture synthetic content ^{[8] [9] [3]}.

5. Regulators and NGOs pushing for stricter developer/deployer obligations

European rules such as the Digital Services Act and proposed CSAR measures are cited as templates for putting due‑diligence obligations on intermediaries to prevent proliferation of AI‑generated CSAM, and national regulators like Ofcom have signalled the Online Safety Act could encompass synthetic CSAM risks ^{[3] [10]}. NGOs and child‑safety organizations advocate for lifecycle protections, threat modelling, and rapid reporting pipelines to law enforcement and hotlines ^{[4] [10]}.

6. Where comparisons to OpenAI are limited by available reporting

Public reporting reviewed here documents industry trends, legal analyses, enforcement cases, and NGO recommendations, but does not provide a comprehensive, source‑by‑source audit that enumerates how each major provider’s internal policies, suppression filters, or incident response play out in practice relative to OpenAI’s documented controls; therefore a definitive, evidence‑backed ranking or tight comparison to OpenAI is not possible from these sources alone ^{[1] [2]}. Existing investigations emphasise systemic enforcement weakness rather than singling out a single company as uniquely permissive or protective ^[1].

7. Bottom line: consensus on duty but disagreement on sufficiency

There is broad agreement among researchers, legal experts, NGOs, and regulators that developers must prevent AI‑generated CSAM through dataset hygiene, detection, reporting, and regulatory compliance, but reporting also shows serious implementation gaps, varied legal interpretations, and inconsistent platform enforcement — all of which complicate any simple comparison between “how other major providers handle CSAM” and “how OpenAI handles CSAM” without more granular, provider‑level audits ^{[5] [4] [1] [3]}.