The technological trajectory is clear: Hash-based systems anchored in the National Center for Missing and Exploited Children (“NCMEC”) database remain highly effective for identifying known CSAM, but they are structurally incapable of addressing synthetic, modified, or previously unseen material. Machine learning systems—trained on large corpora of images—offer the only plausible path forward for detecting novel CSAM, including AI-generated and “hybrid” images.

That technological necessity, however, forces a harder legal question: Can a private entity, acting under contract with law enforcement, lawfully train an AI model on CSAM without consent of the depicted individuals, and then deploy or license that model to law enforcement, internet service providers, and platforms for blocking, filtering, or reporting?

The answer is not categorical. It depends on how one characterizes the private actor, the scope of statutory authorization, and whether the downstream use remains within a law enforcement or public interest framework—or drifts into generalized commercial processing.

Under U.S. law, CSAM is contraband per se. See 18 U.S.C. § 2252A(a)(5)(B). Possession, even for ostensibly benign purposes, is criminalized absent a recognized exception.

Law enforcement agencies and entities like NCMEC operate under statutory carve-outs that allow them to receive, review, and process CSAM for investigative purposes. See 18 U.S.C. § 2258A(c).

The complication arises when a private contractor performs those functions. The legal question becomes whether the contractor is effectively standing in the shoes of law enforcement—or acting as an independent entity engaging in otherwise prohibited conduct.

If a contractor is acting under close direction and control of law enforcement—processing CSAM solely for investigative purposes—there is a strong argument that its possession and use of the material is implicitly authorized as part of the governmental function. This is consistent with how digital forensics vendors operate when analyzing seized devices containing contraband.

However, that argument weakens considerably when the contractor’s activities extend beyond discrete forensic analysis into the creation of generalized training datasets and reusable AI models. At that point, the contractor is no longer merely assisting in a specific investigation; it is building a product. That distinction matters because the statutory framework does not clearly authorize private entities to possess CSAM for product development, even if the product has law enforcement applications.

The legal risk crystallizes at the moment of “productization.” Training a model requires aggregation, retention, and iterative use of CSAM images. Unlike case-specific forensic analysis, which is bounded in scope, training datasets are persistent and often repurposed. The resulting model may then be deployed across multiple contexts, including private-sector content moderation. This raises two interrelated concerns.

First, the continued possession of CSAM for training purposes may exceed the scope of any implied authorization tied to a specific law enforcement objective. Courts have historically construed possession broadly. See United States v. Kuchinski, 469 F.3d 853, 861 (9th Cir. 2006). There is no clear statutory safe harbor for retaining contraband as part of an ongoing machine learning pipeline.

Second, once the trained model is made available to third parties—ISPs, platforms, or other private actors—the nexus to law enforcement becomes attenuated. The model is no longer a tool used by the government; it is a distributed capability embedded in private systems. At that point, the original justification for accessing the underlying data—law enforcement necessity—may no longer apply.

Under the General Data Protection Regulation, the analysis is more explicit and less forgiving. Training an AI model on CSAM is unquestionably “processing” of personal data, and likely special category data under Article 9. Regulation (EU) 2016/679, arts. 4(1), 9(1).
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The Law Enforcement Directive (Directive (EU) 2016/680) permits such processing by competent authorities, and potentially by processors acting on their behalf. See Directive (EU) 2016/680, art. 22. Thus, a private contractor operating strictly as a “processor” for law enforcement—under documented instructions, with no independent use of the data—may lawfully participate in training.

But that status is fragile. The moment the contractor determines the purposes or means of processing—e.g., by deciding to build a generalized model for broader deployment—it becomes a “controller” or joint controller under GDPR. At that point, it must independently satisfy the requirements of Articles 6 and 9, including identifying a lawful basis and complying with data subject rights.

Consent, as previously discussed, is not viable. Legitimate interests are unlikely to override the fundamental rights of the data subjects, given the nature of the data. Public interest grounds may not extend to private entities absent specific statutory authorization. In short, what is permissible as delegated law enforcement processing becomes unlawful when transformed into a commercial or generalized service.

The downstream use of the trained model further complicates the analysis. If the model is deployed by law enforcement agencies, it remains within the Law Enforcement Directive framework (in the EU) or within statutory authorization (in the U.S.).

If it is deployed by platforms or ISPs for blocking or filtering, it may fall within content moderation obligations, such as those imposed by the Digital Services Act. But the legality of the underlying training process does not automatically carry over. Moreover, if the model is used to scan private communications, it raises additional concerns under the ePrivacy Directive and, in the U.S., under statutes like the Wiretap Act, 18 U.S.C. § 2511, depending on how the scanning is implemented.

The European regulatory crisis following the expiration of the ePrivacy derogation underscores this point. Platforms remain obligated to remove CSAM, yet may lack a clear legal basis to conduct the scanning necessary to detect it. Thus, even if the training is lawful, the deployment may not be.

Finally, the reliability of the trained model becomes critical when it is deployed beyond law enforcement. If ISPs or platforms rely on AI classifications to block content or report users to NCMEC, the consequences can be severe. False positives may trigger mandatory reporting under 18 U.S.C. § 2258A, leading to law enforcement investigations.

From a due process perspective, the opacity of machine learning models raises concerns. Under Daubert v. Merrell Dow Pharmaceuticals, Inc., 509 U.S. 579 (1993), 
courts require that scientific evidence be reliable and subject to scrutiny. AI models trained on sensitive and legally restricted datasets may be difficult to audit or explain, particularly if the training data itself cannot be disclosed.

This creates a feedback loop: The more effective the model, the more likely it is to be used; the more it is used, the more its outputs must withstand legal scrutiny; and the more scrutiny is applied, the more its opacity becomes a liability.

Undoubtedly, social media outlets, ISPs and corporate users do not want CSAM on or through their networks. Filtering, however, is a double-edged sword – if an entity can block some, then must it effectively block all, and is the failure to block cause for waiver of immunity under the CDA? Probably not, but there’s always a risk. Human-moderated “abuse” teams suffer from burnout and error. The use of private contractors to train AI models on CSAM sits at the edge of existing legal frameworks. When the contractor acts strictly as an agent of law enforcement—processing data under direct control and for a defined investigative purpose—the activity can likely be justified under existing statutory and regulatory schemes.

But when that activity evolves into the creation of reusable models deployed across public and private sectors, the legal foundation becomes unstable. The contractor risks stepping outside the scope of delegated authority and into independent processing of contraband and sensitive personal data without a clear lawful basis.

The law, in both the United States and Europe, has not yet reconciled this transition. It continues to treat CSAM as contraband and personal data as protected, while simultaneously demanding more effective detection mechanisms.

The result is a narrow and precarious path: AI training on CSAM may be permissible when tightly confined within law enforcement functions, but becomes increasingly difficult to justify as it is abstracted, generalized, and commercialized.

That is not a technological limitation. It is a boundary imposed by the legal system—one that will have to be reexamined as the technology continues to evolve.

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