Leveraging Technology
Leveraging Technology: Bridging the Data Gap with Missing Persons and Human Trafficking
Author: Melody Peace
October 17, 2025
Human trafficking and missing persons cases are inherently complex and time-sensitive. Yet, fragmented reporting and frequent changes in data policies make it difficult to measure progress accurately. These inconsistencies obscure the true scope of the problem and hinder effective resource allocation. Compounding this challenge, current AI applications often prioritize efficiency in reporting rather than focusing on solving cases. By shifting AI innovation toward actionable solutions, tools that help detect, coordinate, and prevent incidents, investigators can achieve more meaningful outcomes and respond more effectively.
Between 2005 and 2024, millions of missing person cases were recorded in the FBI’s National Crime Information Center (NCIC). At first glance, the numbers appear to show improvement: in 2006, there were 836,131 entries with 96,183 active cases at year’s end (Federal Bureau of Investigation, 2007). By 2024, annual entries had fallen to 533,936, with 93,447 active cases (Office of Juvenile Justice and Delinquency Prevention & FBI, 2024). However, these figures do not tell the full story. Changes in reporting procedures, policy updates, and data integration efforts have redistributed cases across multiple systems rather than eliminated them. The apparent decline reflects administrative evolution, not a straightforward reduction in missing persons.
Several major milestones shaped missing person reporting during this period. Suzanne’s Law (2003) required mandatory reporting for individuals under 21, immediately increasing case counts (FBI, 2007). The National Missing and Unidentified Persons System (NamUs) launched its Unidentified Persons Database in 2007 and its Missing Persons Database in 2008, followed by automated matching in 2009. NamUs 2.0 in 2018 modernized cross-agency integration, and legislation like Savanna’s Act and the Not Invisible Act (2020) enhanced accountability for Indigenous populations. These procedural shifts underscore an important point: fluctuations in reported cases often reflect evolving definitions, reporting obligations, and inter-agency coordination, rather than actual changes in the number of missing persons.
Understanding these contextual factors is essential when considering how technology can support investigations. Tools like AI, data integration, and predictive analytics can fill the gaps created by inconsistent records, but only if the underlying data is accurate, standardized, and ethically managed. Otherwise, automated systems risk reflecting administrative quirks instead of real-world conditions. Conceptually, a modern framework would integrate anonymized data from law enforcement, NGOs, shelters, social media, CCTV, telecom metadata, and open-source intelligence. This information would be de-identified, mapped to standardized schemas, and stored securely with clear consent metadata.
Advanced analytics, including natural language processing, risk scoring, anomaly detection, facial and object recognition, and geospatial-temporal clustering, can help identify patterns, hotspots, and high-priority leads. Decision-support dashboards would visualize these findings with ranked leads, heatmaps, timelines, and relational graphs. Human investigators would validate leads, coordinate field operations, and provide feedback to retrain AI models, ensuring that automated outputs complement, rather than replace, expert judgment. Governance protocols, including access logs, ethical oversight, and explainability reports, maintain accountability throughout the process.
In today’s world, cameras and sensors are everywhere, from smartphones and webcams to traffic lights, security systems, and social media platforms. Almost every public and semi-public space is under some form of recording, creating a massive, untapped network of observational data. By integrating this existing technology into a coordinated system for public safety, law enforcement could receive real-time alerts about missing persons or potential trafficking situations without ever revealing the identity of individuals until verification is complete. This approach leverages devices and networks already in place, turning passive recordings into actionable intelligence while protecting privacy and minimizing additional costs. The result is a smarter, faster, and more proactive way to respond to cases before they escalate.
Private companies are increasingly leveraging facial recognition technology (FRT) to enhance security, streamline operations, and boost profitability. The global facial recognition market was valued at approximately $8.09 billion in 2024 and is projected to reach around $32.53 billion by 2034, growing at a compound annual growth rate (CAGR) of 14.93% during the forecast period Precedence Research. This rapid growth underscores the increasing adoption of FRT across various sectors, including retail, healthcare, and finance.
In the retail industry, for instance, companies like Wesfarmers are integrating FRT to combat rising retail crime. The company's CEO has advocated for the responsible use of facial recognition technology to focus on known offenders, aiming to enhance security and reduce losses. Similarly, AI-powered surveillance systems, such as those developed by Veesion, are being employed in over 4,000 stores across 25 countries to significantly reduce shoplifting incidents Safe and Sound Security.
These implementations not only bolster security but also contribute to cost savings by reducing theft-related losses and administrative burdens. As the market continues to expand, the financial benefits for private companies utilizing FRT are becoming increasingly evident.
Implementing continuous monitoring for missing person and victims of human trafficking would begin with partnerships, legal agreements, and the establishment of ethical oversight committees. Secure infrastructure with role-based access controls would be deployed, and investigators trained on standardized workflows and escalation procedures. The system could be piloted in select regions for three to six months, comparing system-assisted leads to conventional investigative approaches. A phased scale-up would integrate cross-agency operations using standardized APIs. Legislative and incentive strategies could encourage private-sector participation: limiting social media platforms, gaming apps, webcam services, and mobile device manufacturers explicit use of facial recognition or AI for revenue in exchange for continuous monitoring for missing persons and victims of human trafficking. Embedded point-of-capture alert systems could notify law enforcement immediately when a potential missing person is detected, and secure, anonymized data sharing would protect privacy and civil liberties. Incentives like tax credits, recognition programs, or liability protections could further encourage compliance.
If implemented effectively, this approach could reduce time-to-identification for missing persons, increase investigator efficiency, and improve detection of non-obvious patterns while maintaining low false-positive rates. Limitations remain, including legal constraints on data coverage, potential issues with model generalizability across jurisdictions, the need to protect civil liberties, and reliance on the quality and coverage of input data such as CCTV and social media activity. Nonetheless, by combining data-driven analysis, advanced technology, and thoughtful policy incentives, the system offers a proactive path toward more effective, coordinated responses to human trafficking and missing persons cases.
References:
Business Insider. (2025). CEO of Clearview AI, the startup that scraped billions of online face images, resigns. Retrieved from https://www.businessinsider.com/clearview-ai-ceo-resigns-hal-lambert-richard-schwartz-2025-2
Reports on the leadership change at Clearview AI and its impact on the company’s operations in the facial recognition market.
Federal Bureau of Investigation. (2007). National Crime Information Center (NCIC) 2006 Missing Person File Statistics. Retrieved from https://www.fbi.gov/file-repository/ncic-2006-missing-person-file-statistics.pdf/view
Provides NCIC missing person statistics for 2006, including total entries and active cases, and explains the impact of Suzanne’s Law on reporting.
National Institute of Justice. (2018). Upgrading the National Missing and Unidentified Persons System: Introducing NamUs 2.0. Retrieved from https://nij.ojp.gov/library/publications/upgrading-national-missing-and-unidentified-persons-system-introducing-namus-20
Discusses the launch and evolution of NamUs, its databases, and automated matching features, showing improvements in cross-agency data integration.
National Institute of Justice. (2019). Solving the Missing Indigenous Person Data Crisis: NamUs 2.0. Retrieved from https://nij.ojp.gov/topics/articles/solving-missing-indigenous-person-data-crisis-namus-20
Explains NamUs 2.0’s role in modernizing cross-agency integration and improving case tracking for Indigenous populations.
National Institute of Justice. (2025a). NamUs and DIAMD Integration Coming Soon. Retrieved from https://namus.nij.ojp.gov/namus-and-diamd-integration-coming-soon
Discusses planned integration of NamUs with DIAMD for enhanced data interoperability and analytics.
National Institute of Justice. (2025b). NamUs: Narrowing the Search for Missing Persons. Retrieved from https://domesticpreparedness.com/articles/namus-narrowing-the-search-for-missing-persons
Highlights the use of NamUs data and analytics to assist investigations and improve case resolution.
National Institute of Justice. (2025c). Introducing NamUs 2.0. Retrieved from https://namus.nij.ojp.gov/media/video/131
Provides an overview of NamUs 2.0 features, including modernized cross-agency integration and improved database management.
Office of Juvenile Justice and Delinquency Prevention & Federal Bureau of Investigation. (2024). Missing Person Entries and Active Cases: 2024 Report. Retrieved from https://ojjdp.ojp.gov/sites/g/files/xyckuh176/files/pubs/2024/missing-persons-report-2024.pdf
Provides NCIC statistics for 2024, showing trends in missing person entries and active cases, highlighting challenges in interpreting data over time.
Precedence Research. (2025). Facial Recognition Market Size and Forecast 2025 to 2034. Retrieved from https://www.precedenceresearch.com/facial-recognition-market
Provides projections for the global facial recognition market, highlighting significant growth from 2025 to 2034.
Safe and Sound. (2025). Facial Recognition Trends and Statistics. Retrieved from https://getsafeandsound.com/blog/facial-recognition-trends-and-statistics/
Discusses the widespread adoption of facial recognition technology in various industries and its implications for security and privacy.
U.S. Department of Justice. (2020a). Savanna’s Act. Retrieved from https://www.justice.gov/tribal/mmip/SavannasAct
Provides legislative context for enhanced reporting and data collection for missing Indigenous persons.
U.S. Department of Justice. (2020b). Not Invisible Act. Retrieved from https://www.justice.gov/tribal/not-invisible-act
Outlines legislation improving accountability and investigation of missing Indigenous individuals.
