OpenAI framework on regulating AI-driveN CSAM threats

The company’s April 2026 blueprint warns that generative AI is reshaping online child sexual exploitation by lowering barriers, increasing scale, and enabling the creation of synthetic abuse material. The document positions its proposals as a roadmap to strengthen US child protection frameworks. It argues that evolving, cross-modal threats are exposing gaps in statutes, reporting systems, and prevention mechanisms. The recommendations are as follows: 

Modernise Child Sexual Abuse Material (CSAM) definitions: States should explicitly include AI-generated and digitally altered CSAM within existing prohibitions and criminalise the knowing possession, production, and distribution of such material. This ensures that liability does not depend on technological form and prevents exploitation of statutory gaps.

Clarify attempt liability, including prompt-based attempts: States should criminalise attempts to produce, solicit, upload, distribute, or traffic CSAM, including through synthetic generation or manipulation. This enables intervention even when safeguards block the final outputs.

Establish a good-faith CSAM prevention safe harbour: States should protect providers undertaking good-faith detection, reporting to the National Center for Missing & Exploited Children (NCMEC), evidence preservation, safety research, and red-teaming, while excluding negligent or unlawful conduct from such protections.

Enable federal alignment: Policymakers should support aligned federal measures to improve reporting quality, preserve evidence and accountability, allow safe testing with the Department of Justice (DoJ), and reduce cross-jurisdiction fragmentation.

Improve CyberTipline report quality with structured data: Providers should submit structured, actionable reports that include identifiers (who), content and modality (what), jurisdiction signals (where), and timelines (when), alongside prioritisation indicators such as imminent-harm flags.

Deploy AI-assisted detection with human-reviewed escalation: Providers should use audited AI systems to flag exploitative signals, ensure human review before reporting or escalation, and prioritise high-risk cases.

Include sufficient context in enticement or trafficking reports: Providers should include a meaningful chat context rather than isolated excerpts, while limiting unnecessary collection of personal data.

Reduce investigative burden through bundling and de-duplication: Providers should bundle related reports by user or incident, combining related files, identifiers, and behavioural patterns to reduce duplication and improve linkage.

Use technical identifiers where available: Providers should include hashes, IP addresses, port numbers, and device identifiers where lawful, to enable cross-case analysis and de-duplication.

Safety-by-Design in GenAI:

• Detect attempts and intent: AI systems should identify high-risk prompts and behavioural patterns, including repeated attempts…