Generative AI is the Future of Law Enforcement Intelligence Analysis

May 21, 2024

Law enforcement intelligence analysts are tasked with information gathering, analysis and dissemination to help agencies combat criminal activity in their jurisdiction. With the exponential growth of data, analysts face the daunting task of sifting through vast amounts of information to develop meaningful intelligence for investigators and command staff. However, thanks to advancements in Generative AI technology, the landscape of intelligence analysis is rapidly evolving, providing law enforcement agencies with powerful tools to enhance their policing and investigative methods. As this new technology rapidly evolves, there are three main advantages to integrating Generative AI into law enforcement intelligence analysis.

Time Efficiency: Generative AI Saves Intelligence Analysts Time

Time efficiency stands as one of the most significant advantages offered by Generative AI in law enforcement intelligence analysis. Traditional methods of data analysis often involve manual processes across systems that are time-consuming and resource intensive. It also involves finding and analyzing data in disparate systems, often owned by different units and departments. When I was an analyst with East Orange (NJ) Police Department, intelligence and crime data were kept in different applications, making it difficult to analyze the data together to get a complete picture of, for example, a gang or neighborhood set. However, with the advent of generative AI-driven platforms, analytical tasks that once took days or even weeks to complete can now be accomplished in a fraction of the time. This is a force multiplier, especially because of the immense volume of data local and federal intelligence analysts must sift through daily. By automating repetitive queries and streamlining analytics and reporting, analysts can allocate more time to critical thinking, ultimately leading to more effective case support and investigation strategies.

Analyzing Large Data Sets: Generative AI Provides a Single Tool for Intelligence Analysis

One of the immediate benefits of Generative AI is the ability to distill large data sets quickly into actionable and timely intelligence. For example, when analyzing data from search warrants and call detail records, analysts may be faced with tens of thousands of records, in various formats and file types. Without a tool to ingest this data, many are left trying to analyze it manually, using software and tools that do not fully automate these processes. This can cause investigative delays and worse, mistakes in case work. Using Generative AI, analysts now can quickly identify relevant information from volumes of data, quickly and with minimal training in SQL and other query languages. Generative AI models can filter through structured and unstructured data, such as forensic extractions, search warrant data, images, and videos, to find key patterns and trends. This capability enables analysts to uncover hidden connections between individuals, organizations, locations and events, facilitating the detection of criminal activities and the disruption of criminal networks.

Prompt Engineering: Generative AI is Easy to Use for Intelligence Analysis

An advantage of Generative AI lies in its ability to utilize prompt engineering instead of traditional queries. Unlike traditional database queries, which can yield limited or incorrect results due to complex table joins and syntax, prompt engineering allows analysts to provide more nuanced instructions to AI models using real language, guiding it to deliver analysis based on those commands. This also lets the analyst create additional prompts, filtering results to get more granular in the analysis. By crafting tailored prompts, analysts can extract highly relevant intelligence from unstructured and structured data, enhancing the quality, accuracy, and timeliness of their analytical products.

For example, suppose a detective squad is investigating a series of drug-related robberies in a particular district or patrol area. By using a generative AI solution, analysts can formulate prompts that instruct the AI model to identify commonalities among persons of interest, locations, and modus operandi (MO). Going a step further, if an analyst pulls in data from license plate readers (LPRs), surveillance cameras, gunshot detection systems, probation/parole records and online data in the area of the crimes, the AI model can then generate detailed reports highlighting potential links between individuals and locations of interest, empowering investigators with intelligence to develop potential leads in the crime series. Generative AI allows analysts to do this work quickly and under a single pane of glass, moving investigations along quicker than through traditional analytical methods.

Generative AI is the Future of Intelligence Analysis

Generative AI can assist law enforcement agencies in predictive analysis, forecasting potential hot spots for activity based on historical data and emerging trends. By analyzing vast amounts of data, including crime reports, online posts, calls for service, arrest records, etc., AI models can identify patterns and correlations that human analysts may overlook. This predictive capability enables law enforcement agencies to proactively allocate resources, deploy personnel, and implement preventive measures to mitigate potential criminal activity. This is the type of analysis that supports not only investigations, but proactive policing strategies as well.

The future of law enforcement intelligence analysis must involve Generative AI solutions because of the volume of data analysts work with daily.  By enhancing time efficiency, distilling down large data sets, and leveraging prompt engineering techniques, Generative AI helps analysts develop meaningful and timely intelligence, ultimately improving investigative efforts. As technology continues to advance, law enforcement agencies must embrace Generative AI as a vital tool in their investigative toolbox, empowering them to quickly address and mitigate emerging threats and crime trends that can impact the community.

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Allison Sullivan
Director, Intelligence

Allison Sullivan is the Manager of the Analyst Team at Cobwebs Technologies holding over 17 years experience as a crime and intelligence analyst in law enforcement. After graduating from Northeastern University, Allison joined the Cambridge, MA Police Department as their crime analyst for nearly three years before returning to her home state of New Jersey where she joined the NJ Transit Police Department. While with NJTPD, Allison helped to create the Crime Analysis Unit as the Senior Crime Analyst, introduced CompStat to the Command Staff, and won a civilian commendation award for her role in solving a violent robbery series. While with NJTPD, she went back to school to obtain her master’s degree in Human Resources, Training, and Development from Seton Hall University. In 2005, Allison joined the East Orange, NJ Police Department as the crime and intelligence analyst for 12 years, aided in the creation of the Real-Time Crime Center, and played a vital role in the Department’s 75% crime reduction.