Summary
Welcome to the first chapter of our artificial intelligence (AI) for public safety blog series. In this blog, we will be covering:
- Why missing persons investigations need to improve traditional investigative methods
- How AI can help investigations by detecting biomarkers, geospatial analysis, and predictive analysis
- Ways that AI, including Veritone Track, helps combat human trafficking while balancing privacy concerns and more
Every year, hundreds of thousands of individuals in the U.S. are reported missing, a scale that highlights just how challenging these investigations can be. According to federal data, about 600,000 people go missing in the United States annually, though most are found within a short time frame. At any given moment, there are still tens of thousands of open cases — with over 25,500 unresolved missing person cases in the NamUs database as of early 2025.
Traditionally, investigators rely on interviews, forensic evidence, eyewitness accounts, surveillance footage, and other manual methods to locate missing individuals. While these methods remain foundational, they are time- and labor-intensive, and they often struggle to keep pace with the volume of reports and the complexity of modern digital evidence environments.
However, with the advancement of AI technology, new solutions are emerging that could greatly enhance both the efficiency and effectiveness of missing persons investigations. The potential of AI in solving these cases is immense, from helping investigators rapidly sift through massive datasets and recognize patterns to improving biometric identification and predictive modeling.
By augmenting traditional methods with AI’s ability to analyze data at scale and surface meaningful insights, agencies can improve search outcomes, reduce investigation timelines, and ultimately bring closure to families and loved ones while more effectively identifying perpetrators or other persons of interest.
The current state of missing persons investigations
Traditional methods for missing persons investigations involve physical search and rescue (SAR) operations, such as ground, aerial, and water-based searches. Investigators also rely on investigative techniques such as witness interviews, surveillance footage analysis, evidence collection, and at times, DNA analysis, to locate missing individuals.
These methods, while effective, have limitations such as time, resources, and human error, making it difficult to find missing persons in a timely manner. And, as most of us know, timeliness is paramount in these types of investigations.
There is a strong need for new solutions as the number of missing person cases continues to grow worldwide — fortunately, AI is revolutionizing public safety with each passing day.
Modernizing investigations with technology can address these limitations and help locate missing persons more efficiently. Advancements in AI can analyze large amounts of data, identify patterns, and process facial recognition and human-like objects (HLOs), improving the accuracy and speed of investigations.
How AI is transforming the search for missing persons
The search for missing persons is a complex and challenging task that has traditionally relied on manual investigation techniques. However, AI is transforming the search for missing persons by enhancing traditional methods with advanced algorithms, data analysis, and predictive modeling, especially in the areas of big data and predictive analysis, geospatial analysis, and biometric recognition.
Big data and predictive analytics
Big data and predictive analysis are also critical areas where AI is transforming search capabilities:
- Large datasets, including social media and public records, are now being used to predict probable locations and patterns of missing persons.
- Predictive modeling helps investigators narrow down search areas and focus resources where they are most likely to be effective.
- Natural language processing (NLP) is also used to analyze social media posts and gather valuable insights that can aid in the search for missing persons.
Combining non-biometric tools with biometric recognition
When investigations first start, the goal is to narrow down, pinpoint, and identify persons of interest as quickly as possible. Solutions have now moved beyond facial recognition, enabling investigators to track persons of interest using other identifiers.
For example, Veritone Track can use markers outside of personally identifiable information (PII) to define and track individuals and build timelines that can assist with identifying persons of interest, finding missing people, and preventing human trafficking.
Once an investigation gets to a certain point, AI-based biometric recognition algorithms offer another tool for them to drill down further. Offering greater accuracy and efficiency compared to manual biometric tools of the past, AI-based solutions can help identify individuals using facial recognition capabilities. For instance, Veritone IDentify, investigators can determine if a person of interest becomes a suspect if they show up in the existing arrest record database.
By combining non-biometric tracking with AI-powered biometric recognition, investigators gain a layered approach: first narrowing the field using contextual markers and activity patterns, and then confirming identities with greater precision once potential persons of interest are identified. This seamless integration allows law enforcement to move efficiently from initial identification to actionable leads, particularly in sensitive investigations like human trafficking.
AI in combating human trafficking
Human trafficking is a worldwide issue that often involves missing person cases. Because of its ability to identify victims and track perpetrators through pattern recognition, data analysis, and machine learning, AI is playing a crucial role in combating human trafficking.
AI algorithms can analyze large amounts of data from multiple sources (including CCTV, social media, and online platforms) to identify patterns and potential victims as well as build a timeline. This technology enables law enforcement to investigate and apprehend perpetrators, increasing public safety and potentially preventing future cases. Fortunately, this can all be done while maintaining privacy laws and protecting the PII of victims, perpetrators, and witnesses.
Balancing privacy concerns and public safety
As AI becomes more prevalent in law enforcement, balancing privacy concerns and public safety is a critical issue. While AI has the potential to enhance public safety, it can also lead to privacy violations and abuse of power.
Needless to say, it’s essential to establish ethical and legal frameworks to regulate AI usage and protect privacy rights. This includes developing legislative measures and guidelines to ensure transparency, accountability, and oversight of AI-based systems.
Additionally, implementing best practices such as data anonymization,security measures, and having human-in-the-loop processes can mitigate the risks associated with AI. Overall, ensuring privacy is a crucial component of police reform and promoting public trust in law enforcement and justice agencies.
The role of AI in police reform and missing person cases
AI is transforming police reform and law enforcement practices by enabling improved resource allocation and decision-making. Data-driven insights and automation help police departments allocate resources more effectively, optimize patrol routes, and reduce response times. AI algorithms also aid in decision-making processes by providing real-time information, analyzing data patterns, and predicting potential outcomes.
These tools can help law enforcement to identify areas of improvement, allocate resources more effectively, and ultimately enhance public safety. By utilizing the correct AI-powered tools and integrating a strong framework for how AI should be used, law enforcement agencies (LEAs) and justice agencies can ensure greater accuracy and speed in active investigations, uncompromised confidentiality and PII for improved case handling, and increased public safety and prevention of future incidents.
AI as a force multiplier in modern investigations
Modern investigations generate unprecedented volumes of digital evidence, from body-worn camera footage, CCTV video, and 911 call recordings to interview files, dispatch logs, and digital media from multiple agencies. According to industry research, digital evidence is now a factor in about 90 % of criminal cases, meaning the vast majority of investigations today include some form of video, audio, or device data that must be processed and analyzed.
The sheer scale of this data is overwhelming agencies. One recent report from a district attorney’s office in Colorado found that video and audio evidence handled by prosecutors increased from about 36,000 individual recordings (24,000+ hours) in 2022 to over 67,700 recordings (41,000+ hours) in 2025, a 600 % increase in evidence volume compared with five years earlier.
While this data is invaluable for uncovering leads and building cases, it often exists in silos across disconnected systems, from separate storage repositories and agency databases to disparate formats and devices. Investigators frequently need to locate, request, and manually consolidate evidence before analysis can even begin. One benchmarking study of investigative workflows found that officers and detectives often spend 4 to 6 hours or more on repetitive tasks like logging into multiple systems and gathering basic evidence before substantive analysis can start.
Key investigative benefits of AI include:
- Automated evidence discovery across large volumes of video, audio, and documents
- Rapid correlation of events across time, location, and media types
- Advanced search and analytics, including object, speech, and pattern recognition
- Faster case timelines, enabling investigators to move from collection to insight more quickly
By reducing manual review and enabling investigators to focus on decision-making rather than data wrangling, AI helps agencies accelerate investigations while improving accuracy and consistency.
This shift toward AI-driven investigation management is laying the foundation for platforms designed to centralize digital evidence, streamline workflows, and provide actionable insights at scale.
AI as a force multiplier in modern investigations
The future of AI in public safety and missing person cases will likely involve collaboration between LEAs and tech companies. By working together, we can develop more effective and efficient AI-driven tools to enhance search and rescue operations, as well as other applicable use cases. One potential application is in addressing bullying and preventing disappearances through early identification and intervention strategies using AI-powered monitoring and analysis.
As technology advances, we can expect new AI-powered tools and techniques to emerge, such as more sophisticated biometric recognition and predictive modeling. For public safety agencies, having access to the right tools is paramount.
AI continues to offer new solutions and directions for missing persons investigations. With Veritone Track, public safety teams can identify and locate persons of interest across several separate video files without the need for using PII — enabling faster investigations that maintain privacy laws and protect individuals’ personal information.
To learn more about how Veritone’s AI-powered Track solution can revolutionize missing persons investigations and ensure a safer future, reach out to a team member who can help answer questions or schedule a free demo.
References:
https://www.statista.com/topics/13808/missing-persons-in-the-united-states/
National Missing and Unidentified Persons System (NamUs)
https://pmc.ncbi.nlm.nih.gov/articles/PMC10311201/
https://www.cpr.org/2026/01/02/overwhelming-digital-evidence-body-cam-footage/
