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Thorn

Nonprofit tech to defend children online

Overview

Thorn is a nonprofit that develops technology and conducts research to protect children from sexual abuse and exploitation in the digital world. It supports platforms, caregivers, and communities by providing tools, resources, and partnerships to prevent online harm. Thorn’s solutions combine software, original research, and collaboration with other organizations to help detect and block exploitative content and dangerous behavior, and to teach safer online practices. The company differentiates itself by focusing on building practical tech tools and partnerships, rather than just services, to empower others to safeguard children across online spaces. Thorn’s goal is to defend children in the digital age and ensure every kid can enjoy childhood safely.

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About Thorn

Simplify's Rating
Why Thorn is rated
B-
Rated B on Competitive Edge
Rated B on Growth Potential
Rated C on Differentiation

Industries

Company Size

51-200

Company Stage

Grant

Total Funding

$350K

Headquarters

Manhattan Beach, CA

Founded

2012

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What believers are saying

  • July 2026 Context Labels sharpen Safer prioritization as platform CSAM volumes keep rising.
  • Veritone integrated Thorn Detect on April 2, 2026, expanding law-enforcement distribution.
  • 2026 mid-year update showed 86 companies using Thorn products and 1,399,479 suspected files found.

What critics are saying

  • Thorn still needs $30 million philanthropy through 2031 after the $18 million Audacious commitment.
  • EU CSAM detection law expiry on April 3, 2026 threatens platform scanning across Europe.
  • Thorn's mission depends on trust; public backlash can choke donations and platform adoption quickly.

What makes Thorn unique

  • Thorn's Safer processed 50 billion files monthly and 15,000 suspected CSAM daily in 2026.
  • Thorn Detect serves 1,046 agencies with investigator workflows and victim-identification tooling.
  • Context Labels align with INHOPE's Universal Classification Schema, standardizing triage across platforms and agencies.

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Funding

Total Funding

$350k

Above

Industry Average

Funded Over

1 Rounds

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Company News

Thorn
Sep 22nd, 2026
When every moment matters, Context Labels help accelerate child safety.

When every moment matters, Context Labels help accelerate child safety. September 21, 2026 A child sexual abuse investigator might face upwards of 30,000 cases in a year. With each of those cases potentially involving thousands of electronic files, identifying abusive material is a true needle-in-a-haystack problem. The same is true for trust and safety teams at tech platforms. Their tools might flag thousands of suspected child sexual abuse images a day. How can they ensure their reporting provides sufficient information to enable investigators to more quickly triage reports and focus on the most severe cases to help children in harm's way? This dilemma is why Thorn developed Context Labels: to give investigators and trust & safety professionals additional useful information as they review suspected child sexual abuse material (CSAM). How Context Labels strengthen detection. Advanced tools, like CSAM classifiers, identify files that may contain child sexual abuse. That first signal gives investigators and trust and safety teams a critical starting point. But Context Labels go further, adding another layer of information so professionals can evaluate the severity and relevance of what the files may show. Thorn's Context Labels use specialized machine learning models trained on confirmed abuse material. These models analyze images and label them based on apparent maturity level, nudity, and sexual content. This lets platform moderators and investigators focus their attention based on those additional indicators. Context Labels help high-priority cases move faster. Digital files don't often arrive in a neat order. Investigators may receive hard drives, phones, or cloud accounts seized during an operation. A single case can create days or weeks of review. First, the CSAM classifier predicts which files may contain child sexual abuse material, which helps narrow down the large number of files for the investigator. Next, the Context Labels help organize the material. Investigators can use that information to assess which files may need the fastest response. With the Context Labels and CSAM Classifier in place, an investigator may discover a critical image earlier. A social media moderation team may identify patterns emerging across their platform. A report may reach the right agency with more precise details about what the image contains. Each improvement creates room for experts to focus on the child behind the material. Thorn Detect brings this ability into investigative workflows. Thorn Detect helps investigators identify suspected CSAM within digital materials. Context Labels give investigators more information as they decide what steps to initiate for a case. Safer, Thorn's purpose-built platform safety tool, brings the same capability to technology companies. Moderation teams use Safer to detect, review, and report suspected CSAM on their platforms. Context Labels help those teams understand the material they encounter and prepare more nuanced information for their reports. The two products serve professionals at different points in the response. Together, they show how one technical advance can strengthen the wider child protection system. Context Labels support collaboration across organizations. Child sexual abuse can cross regions without interruption in today's digital age. A platform may serve one country while a child being abused lives in another. If files pass through several layers - platform detection, a reporting clearinghouse like NCMEC, a referral to law enforcement - before reaching the investigators who can help, shared language can make these handoffs smoother. Thorn's Context Labels follow the Universal Classification Schema developed by INHOPE*. These are uniform categories agreed by INHOPE and its partners, with the goal of using a shared language across the child safety ecosystem. This creates greater consistency when platforms, nonprofits, and investigative agencies describe suspected abuse material. A report with consistent categories gives the next team universally understood information. Investigators can understand what a platform found. Agencies in different regions can interpret the material through the same framework. Partners can compare patterns and decide where resources may have the greatest impact. How does Thorn innovate for child safety? Thorn develops technology in close partnership with the professionals who use it. In fact, the apparent maturity level, nudity, and sexual content classifiers were developed with the support of the INTERPOL DevOps community. The technology began with a need for more information to prioritize large volumes of suspected CSAM. Thorn built the models and tested them with experts, gathering feedback on their usefulness, clarity, and practical application. Those insights helped shape the technology before Thorn integrated it into Safer and Thorn Detect. Thorn then worked iteratively with experts to determine if the Context Labels were understandable across regions, whether they surfaced useful distinctions within large CSAM datasets, and how they could support prioritization or triage. Child protection depends on this kind of continuous innovative development. Perpetrators change their methods. New technologies create new risks. The volume of digital material continues to grow. The solutions need to scale alongside the threat. Donor support enables Thorn to build innovative tools, conduct research, and form partnerships that help child safety. It supports the technical innovation behind products like Safer and Thorn Detect. Every critical file may carry information about a child. And giving investigators more information can help them find that child faster and move them closer to safety. *Thorn's use of the Universal Classification Schema is provisioned through a license with INHOPE. INHOPE exclusively holds all intellectual property rights in and to the Schema, and any use of the Schema outside of Safer is subject to INHOPE's consent.

PR Newswire
Jul 14th, 2026
Thorn launches AI context labels to help platforms prioritise severe child abuse content faster

Thorn, a nonprofit focused on child protection, has launched context labels for its Safer platform to help detect child sexual abuse material (CSAM). The new predictive classifiers provide additional signals around nudity, apparent maturity level, and sexual content, enabling trust and safety teams to better prioritise severe cases. In 2025, Safer classified over 3.8 million files as suspected novel CSAM using predictive AI and detected nearly 1.5 million known CSAM files through hash matching. The context labels were developed with support from the INTERPOL DevOps community and align with the Universal Classification Schema V2, developed by INHOPE. The new tools aim to help moderation teams manage large volumes of flagged content more effectively and identify emerging platform-level trends.

Associated Press
Apr 2nd, 2026
Veritone integrates Thorn's AI-powered child abuse detection into law enforcement tools

Veritone has integrated Thorn Detect, a child sexual abuse material detection tool, into its AI-powered Intelligent Digital Evidence Management System. The partnership combines Veritone's enterprise AI solutions with Thorn's non-profit technology to help law enforcement identify potential abuse images and videos on seized devices faster. Thorn Detect uses machine learning models to help investigators prioritise critical files and accelerate case resolution. The integration aims to shorten investigation cycles and enable faster victim identification. "Behind every case file is a child waiting to be found," said Brian Herrick, Vice President of Victim Identification at Thorn. The partnership is designed to help investigators cut through overwhelming caseloads whilst protecting their wellness. Veritone's iDEMS suite serves both commercial and public sectors with AI-driven data analysis capabilities.

Investing News Network
Apr 2nd, 2026
Veritone and Thorn partner to safeguard children against abuse faster.

Veritone and Thorn partner to safeguard children against abuse faster. April 02, 2026 Partnership integrates Thorn's CSAM detection AI into Veritone's iDEMS suite, empowering law enforcement to accelerate investigations and protect vulnerable children with powerful AI solutions. Veritone, Inc. (NASDAQ: VERI), a leader in building enterprise AI and data solutions, today announced the integration of Thorn Detect a child sexual abuse material (CSAM) detection tool developed by Thorn a non-profit organization dedicated to transforming how children are protected from sexual abuse and exploitation in the digital age into Veritone's AI-powered Intelligent Digital Evidence Management System (iDEMS) solutions suite. Using state-of-the-art machine learning models, Thorn Detect quickly identifies potential child sexual abuse images and videos on a seized digital device, helping investigators prioritize the most critical files, speed up case resolution, and find victims faster and remove them from harm. This integration enables Veritone users to utilize Thorn's technology to detect CSAM, significantly accelerating crucial investigations. "Our partnership with Thorn is a critical step forward in our commitment to providing law enforcement with the most advanced tools to protect our communities," said Jon Gacek, GM of Veritone Public Sector. "The fight against child sexual exploitation requires advanced technology, and by integrating Thorn Detect into our iDEMS suite, we are empowering investigators to more quickly identify and act on critical evidence. This technology will help shorten investigation cycles and bring perpetrators to justice faster, all while helping to protect children." "Behind every case file is a child waiting to be found. Thorn Detect helps investigators cut through overwhelming caseloads and focus on what matters most - identifying child victims and getting them to safety," said Brian Herrick, Vice President of Victim Identification at Thorn. "This partnership is designed to help find more children faster, while preserving investigator resiliency and better protecting their wellness." Veritone (NASDAQ: VERI) is a leader in enterprise artificial intelligence (AI) software and solutions that transform unstructured data into actionable intelligence and dynamic workflows. By empowering organizations in both the commercial and public sectors, Veritone enables users to increase operational efficiency, accelerate decision-making, and drive profitability. The company's proprietary AI operating system, aiWARE(TM), orchestrates a diverse ecosystem of machine learning models and intelligent applications to process and tokenize data - including video, audio, and images - powering sophisticated automation and measurable business outcomes. Committed to the development of ethical AI, Veritone blends human expertise with cutting-edge technology to help customers navigate a complex digital landscape while helping to protect intellectual property and enabling sustainable business growth. For more information, visit Veritone.com. About Thorn Thorn is an innovative technology nonprofit that transforms how children are protected from sexual abuse and exploitation in the digital age. Thorn builds scalable tools to help platforms detect and prevent child sexual abuse and exploitation, supports investigators in finding victims faster, and shares research and technical guidance to shape policy and improve protections for children worldwide. By working within the broader child protection ecosystem, Thorn is creating a digital safety net to protect every child's right to simply be a kid. To learn more about Thorn's mission to protect children in the digital age, visit thorn.org. Safe Harbor Statement This news release contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. Forward-looking statements include, without limitation, statements regarding the anticipated integration of Thorn Detect into Veritone's iDEMS solutions suite; the expected capabilities, performance, and benefits of such integration; anticipated improvements in investigation speed, case resolution, and victim identification outcomes; the expected benefits of the partnership between Veritone and Thorn; and Veritone's business strategy, product development plans, and market position in the public sector. Words such as "anticipates," "believes," "could," "estimates," "expects," "intends," "continue," "can," "may," "plans," "potential," "projects," "seeks," "should," "will," "would" or similar expressions and the negatives of those expressions may identify forward-looking statements. These statements are based on current expectations, estimates, assumptions, and projections and involve known and unknown risks and uncertainties that may cause actual results, performance, or achievements to differ materially from those expressed or implied in such statements. These risks and uncertainties include, but are not limited to, risks related to the successful integration of third-party technology into Veritone's products; the ability to achieve the anticipated benefits of the partnership; market adoption of Veritone's iDEMS solutions; reliance on third-party technology providers; regulatory and legal developments affecting AI-based evidence analysis tools; and those additional risks described in Veritone's filings with the Securities and Exchange Commission, including its most recent Annual Report on Form 10-K and Quarterly Reports on Form 10-Q. Although Veritone believes that the assumptions underlying the forward-looking statements are reasonable, any of the assumptions could prove inaccurate. In light of the significant uncertainties inherent in the forward-looking information included herein, the inclusion of such information should not be regarded as a representation by Veritone or any other person that their objectives or plans will be achieved. Veritone undertakes no obligation to update or revise the forward-looking statements contained herein to reflect events or circumstances after the date hereof or to reflect the occurrence of unanticipated events.

PR Newswire
Mar 3rd, 2025
1 In 8 Teens Know Someone Targeted By Deepfake Nudes, New Report Finds

Despite rising awareness—31% of teens are familiar with deepfake nudes—many remain unclear on the legal and emotional consequences.LOS ANGELES, March 3, 2025 /PRNewswire/ -- New research from Thorn , a nonprofit that builds technology to defend children from sexual abuse and exploitation, reveals deepfake nudes are already violating young people's privacy and safety. In fact, nearly 1 in 3 teens surveyed have heard of the term "deepfake nudes," and 1 in 8 reported personally knowing someone who has been victimized by them.Thorn's new report, Deepfake Nudes & Young People: Navigating a New Frontier in Technology-Facilitated Nonconsensual Sexual Abuse and Exploitation, surveyed 1,200 young people (ages 13-20) to uncover how deepfake nudes are rapidly transforming from a fringe concern into a mainstream threat. Unlike earlier forms of manipulated imagery, today's deepfake technology allows anyone to create hyper-realistic, explicit content in seconds—no technical skill required."No child should wake up to find their face attached to an explicit image circulating online—but for too many young people, this is now a reality," said Melissa Stroebel, Vice President of Research and Insights at Thorn. "With lower barriers to creation and easily accessible apps, the dangers of deepfake nudes continue to escalate, with effects we're only beginning to measure. This research confirms the important role tech companies play in designing and deploying technology conscious of the risks of abuse, while also underscoring the need to educate young people and their communities on how to address this kind of digital abuse and exploitation, and for technology-driven solutions that can stem the creation and spread of deepfake nudes."Other key findings of the report include:Teens overwhelmingly recognize deepfake nudes as harmful. A substantial 41% of teens said deepfake nudes cause harm to the person in them

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