Beyond Hash Matching: AI-Based Detection of Previously Unidentified CSAM and its Legal Implications in the EU

A peer-reviewed paper published November 5, 2026, examines the limits of traditional hash-matching technology, which is designed to identify previously verified child sexual abuse material but cannot detect new, previously unidentified CSAM. Researchers Kokolaki and colleagues review and classify AI-based technologies that could complement hash-based approaches, conducting a comparative analysis of general-purpose computer vision platforms, content moderation services, and specialized CSAM detection tools. The paper addresses the capabilities, limitations, and potential integration of these systems within existing detection workflows, highlighting the technical and legal challenges facing law enforcement authorities, online service providers and child protection organizations in the European Union.

Hash-matching identifies only what is already known; it cannot surface a child in active danger from material not yet in a database. Guardii's detection methodology does not rely on comparing an image to a library of known abuse but instead identifies harmful patterns in the conversation itself: coercion, escalation, sustained targeting, and the introduction of sexual or exploitative content in the context of age-inappropriate contact. The platform's anti-CSAM and anti-grooming modules, deployed in real time across Instagram, Snapchat, Discord and other messaging services, flag new material and new tactics at the moment they appear in a child's direct messages, reporting the shape and seriousness of the threat to the parent or authority without waiting for a hash match or manual review, and enabling rapid intervention in cases where previously unidentified material signals active abuse.