IWF data will strengthen SafetyMode protections against child sexual abuse material, grooming and other online risks.
UK-based child-safety company SafetyMode has joined the Internet Watch Foundation (IWF), gaining access to specialist intelligence designed to help prevent children from encountering child sexual abuse material on smartphones.
SafetyMode says its software uses on-device AI to monitor and block harmful content across apps while allowing children to continue using their phones for communication, education and other everyday activities.
Through its IWF membership, the company will integrate the organisation’s URL List, Non-Photographic Imagery URL List and Keywords List into its services.
The URL lists can help block or disrupt access to webpages containing confirmed child sexual abuse material or prohibited non-photographic depictions. The Keywords List contains terms, phrases and codes used to disguise and circulate illegal content, supporting detection and moderation.
SafetyMode’s software is used on OtherPhone, a smartphone co-designed with Mumsnet using hardware from Nothing. It can also be installed on Android smartphones.
The IWF said its analysts took action on more than 311,000 reports of confirmed child sexual abuse material in 2025. SafetyMode argues that access to the organisation’s intelligence will strengthen protections against harmful content and risks, including grooming and sextortion.
The announcement does not provide independent performance figures, a timetable for completing the integration or details about how potential false positives will be handled.
Why does it matter?
Combining on-device AI with regularly updated intelligence on known illegal content could help prevent children from accessing harmful webpages and identify signals linked to the circulation of abuse material.
The effectiveness of the approach will depend on how the technology is implemented, how frequently its data is updated and whether privacy protections and mechanisms for correcting errors are sufficiently robust.
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