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What best practices should law enforcement and tech platforms follow when handling suspected AI-generated CSAM?
Executive summary
Tech platforms and law enforcement should combine rapid reporting, rigorous preservation, and advanced detection while supporting victims and ensuring legal clarity; Thorn and industry guidance call for stress‑testing models, documented trust-and-safety procedures, and use of hash/databases to identify CSAM [1] [2] [3]. The Internet Watch Foundation and other bodies report sharp increases in AI‑generated CSAM reports, straining investigators and making provenance and cross‑sector cooperation essential [4] [5].
1. Treat suspected AI‑CSAM as a high‑priority public‑safety issue
Platforms must make clear zero‑tolerance CSAM policies and report material to authorities such as NCMEC’s CyberTipline when it appears on their systems, because federal reporting obligations already apply to interactive service providers [3]. Government bulletins frame all AI‑created CSAM as illegal and urge industry collaboration with NGOs and law enforcement to prevent, identify, and investigate misuse [6].
2. Preserve evidence and document chain‑of‑custody from first contact
Industry guidance recommends platforms document trust‑and‑safety procedures for reporting and preserving suspected CSAM so law enforcement can triage and investigate without losing critical metadata or context [2]. Legal analysts urge proactive dialogue with prosecutors and calls for clearer retention and preservation rules so reports remain usable for investigations [7] [8].
3. Use a layered technical approach: hash‑matching, provenance, and novel detection
Traditional hash‑matching to known CSAM databases remains useful for modified material, and platforms should consider training detection models on established hash lists to remove and report known content quickly [3]. But many sources note hash methods fail on novel AI imagery; safety-by‑design advocates push for provenance markers, content provenance systems, and next‑generation detection that can flag novel synthetic material [1] [9] [10].
4. Stress‑test models and bake safety into AI development
Thorn and allied groups call for structured, scalable red‑teaming and stress testing throughout model development to measure a model’s capability to produce child sexual exploitation imagery and to integrate findings back into training and safety controls [1]. Contracts with AI vendors should include warranties, indemnities, and audit rights to ensure vendors maintain guardrails against CSAM generation [3].
5. Equip investigators with specialized forensic tools and training
Forensics firms and law‑enforcement partners recommend new image‑forensics techniques and investigator training to distinguish photorealistic fakes from real victim material and to identify victims where possible; collaboration between tech vendors and police can produce tailored tools for complex cases [11] [12]. Agencies should also choose investigative AI tools that reduce investigator exposure to traumatic content via automated triage [13].
6. Prioritize victim wellbeing and safeguarding protocols
Guidance from UK agencies stresses that the method of creation is irrelevant for safeguarding: whether fabricated or edited, images depicting minors sexually are treated as CSAM, and wellbeing support should be offered to any identified child, with special protocols when an alleged perpetrator is a child [4]. Platforms and police should coordinate removal and support pathways rather than leaving victims to navigate takedowns alone [5].
7. Share intelligence, but mind bias and privacy risks
Cross‑sector information sharing—through coalitions like INHOPE, the Tech Coalition, and national hotlines—is advocated to scale detection and enforcement, but safety frameworks also warn about biased automated moderation (e.g., demographic disparities) and recommend evaluating detection performance across populations [10] [2]. Procedural safeguards and transparency about methods are necessary to balance effectiveness and civil‑liberties concerns [10] [2].
8. Push for legislative clarity and operational support
Several analysts and practitioners argue that clearer laws and narrow safe‑harbors would let companies red‑team models for CSAM without fear of prosecution and would help law enforcement by extending data retention for CyberTipline reports—both practical measures to improve investigations [8] [7]. Meanwhile, national laws like the UK’s Online Safety Act have increased platform responsibilities and border‑force powers, raising resource and implementation questions for police [5].
9. Operational checklist for immediate best practices
Platforms: publish CSAM reporting contacts, document preservation workflows, implement hash and provenance detection, contractually require vendor safeguards and red‑teaming [2] [3] [1]. Law enforcement: secure preserved evidence and metadata, adopt image‑forensics tools, prioritize victim support, and coordinate internationally and with NGOs [11] [12] [6].
Limitations and open questions
Reporting highlights consensus on urgent cooperation, detection innovation, and victim support, but available sources do not provide a single, unified operational standard or law applicable worldwide; national variances and evolving tech mean practices will continue to shift (not found in current reporting).