With the rise of generative artificial intelligence (GenAI), new dimensions of child exploitation have emerged, with greater capacity to do harm and increased enforcement complexity. Just as GenAI tools act for legitimate businesses as resource multipliers for improved efficiency, they can also broaden the reach of those who perpetrate digital child abuse. Chatbots are being deployed to automate grooming interactions with victims, putting more children at risk. Another alarming trend is the use of GenAI to generate child sexual abuse materials (CSAM), flooding the internet with explicit deepfake pictures and media at extraordinary scale.

The scope of these crimes and the damage they impose on children are growing at an unprecedented rate. The National Center for Missing & Exploited Children reports a more than 64 times  increase in GenAI-related offenses from 2024 to 2025 in the United States.¹ In addition to psychological trauma, victims may be subjected to financial extortion, coerced into committing self-harm, or even targeted for human trafficking. The intensity and expansion of this vector demands robust response by law enforcement, including using lawful intelligence to detect and take down CSAM content, then prosecuting those responsible.

Detection Mechanisms for GenAI-Created CSAM

Perpetrators use sophisticated GenAI tools to generate massive amounts of CSAM with little effort. They often exploit their victims without ever directly contacting them, which complicates investigations and deprives law enforcement of crucial evidential footprints. Because the detection of GenAI-based CSAM often leads to extensive troves of images, as well as links to broader criminal enterprises, lawful intelligence countermeasures are rapidly emerging.

A growing body of online solutions directly analyze images—including CSAM—to reveal visual artifacts and inconsistencies that suggest the involvement of GenAI. Such images often include telltale signs such as unnatural or repeated textures, flaws in details such as hands or teeth, and unexpected asymmetries. Additions or modifications to natural images such as pornographic versions of innocuous photos often fail to blend smoothly with the original, sometimes evidenced by lighting, shadows, and reflections that don’t match the scene. Feeding these insights into lawful intelligence frameworks using APIs can identify a substantial proportion of CSAM deepfakes, especially when multiple related images are available for comparison. Reverse image lookup can often match GenAI-created CSAM to original online pictures that were modified using GenAI, making the forensic analysis more effective.

The lawful intelligence platform itself has further ability to reveal GenAI use in photos, including by analyzing the Exchangeable Image File Format (EXIF) metadata embedded in them. EXIF information is automatically attached to images, such as details about the camera and editing software used, as well as the date, time, and location where the picture was created. Where perpetrators fail to insert false information manually, they may be exposed when a GenAI image generator neglects to embed EXIF profiles at all, generates EXIF anomalies and inconsistencies, or provides suspiciously scant profiles. In some cases, GenAI tools even identify themselves explicitly in the metadata.

Technology and Policy to Shut Down AI-Driven Exploitation

Once GenAI CSAM has been detected, broader lawful intelligence measures help investigators learn the extent of the crime, its connection to other offenses, and ultimately, the identities of the criminals.  Advanced cloud-based platforms can provide deep, automated detection and analysis of these materials, making forensics on them a first-order contributor to investigations. Law enforcement personnel analyze images and their underlying information in the context of other investigative data such as lawfully intercepted communications, location services, and open-source intelligence. Thoroughly understanding the provenance of the materials may also identify children at risk who need immediate protection and clarify whether the charges involve production and abuse versus distribution of prohibited synthetic material.

Today, the enforcement environment remains complex. Some jurisdictions treat synthetic CSAM with different legal significance than “real” images, and in some cases, prosecution may be limited to obscenity statutes or the offending materials may even be protected as free speech. The overwhelming global trend is toward explicit criminalization, although rapid evolution of these laws leaves ambiguity that can complicate prosecution, especially in international cases. There is a clear need for legal definitions and statutes to be harmonized globally.

Moreover, GenAI systems must be designed so they cannot produce sexualized depictions of minors, using measures such as clean training data, strict prompt and output filters, age‑detection models, and strong governance. They should also monitor for reportable patterns of abuse and enforce strong child-safety policies across the entire product lifecycle. Future advances in technology and cross-border enforcement strategies are timely and critical across government and commercial entities to protect society’s most vulnerable from GenAI’s malicious misuse. d

Note:

¹Matt Seldon, “Surge in Online Crimes Against Children Driven by AI and Evolving Exploitation Tactics, NCMEC Reports,”  Homeland Security Today, October 10, 2025.

Please cite as

Syed Hussain, “Protecting Children from AI-Based Sexual Exploitation,” Police Chief Online, February 3, 2026.