AI Chatbots in Policing: Navigating the New Frontier of Digital Evidence Analysis

Law enforcement agencies are confronting a data crisis, with digital evidence from phone recordings, online chat logs, and surveillance footage creating an insurmountable volume of information for human analysts to process. A new wave of artificial intelligence tools, including chatbots like Longeye, promises to help police spot clues and patterns within this data deluge¹. While these technologies offer significant time savings, turning hours of work into minutes, they also introduce a complex array of security, accuracy, and civil liberties concerns that security professionals must understand².

For security leaders, the adoption of AI in policing represents a dual-edged sword. On one hand, it can enhance investigative capabilities; on the other, it creates new threat vectors and operational risks. A survey of law enforcement agencies found that 53% struggle to access relevant data, while 46% face challenges in analyzing that data for court-admissible insights². This environment is ripe for AI solutions, but their implementation must be scrutinized with the same rigor as any enterprise system handling sensitive information.

AI-Powered Evidence Analysis in Action

Start-ups like Longeye are deploying AI chatbots specifically designed for police use. These systems can analyze massive datasets, such as 60 hours of jail calls, and answer specific investigative queries like “List any names that come up repeatedly.” The AI provides results with timestamps and links back to the source audio, allowing for verification¹. Police Chief Darrell Lowe of Redmond, Washington, reported that the tool significantly shortened evidence review time and even surfaced a missed detail that broke a cold-case murder investigation. This capability addresses a critical pain point: law enforcement’s inability to process the mountains of potential evidence that lead to under-investigated crimes, according to investor David Ulevitch of Andreessen Horowitz¹.

The technical implementation of these systems involves processing digital evidence stored on secure, FBI-standard cloud servers, with the company assuring that tools always cite source material for human verification. This approach mirrors enterprise security practices where audit trails and data provenance are critical for maintaining integrity. The value proposition is clear for larger agencies with more substantial data resources; 57% of agencies with over 5,000 employees value AI-powered predictive analytics, compared to 42% of smaller agencies with fewer than 1,000 employees².

Administrative Efficiency Through Automated Reporting

Beyond evidence analysis, AI is streamlining police administrative work. Axon, the company known for Tasers and body cameras, has developed Draft One, an AI tool that uses body camera audio to automatically generate the first draft of police reports³. In field tests in Oklahoma City, a report that typically took 30-45 minutes to write was generated in approximately 8 seconds. Officers reported that the drafts were accurate and comprehensive, sometimes capturing details the officer had missed. The technical foundation is built on a tuned version of OpenAI’s technology, with the “creativity dial” turned down to minimize fabrications³.

This application demonstrates how AI can offload repetitive tasks, but it also introduces significant accountability questions. Axon CEO Rick Smith emphasizes that the officer must always be the author and testify to the report’s contents in court. Different jurisdictions have implemented varying safeguards; Oklahoma City restricts use to minor incidents on advice from prosecutors, while other cities like Lafayette, Indiana, have no restrictions on use³. The tool includes a disclaimer that AI was used to generate the draft, creating a transparent record of the process.

Critical Security and Accuracy Concerns

The implementation of AI in law enforcement raises substantial concerns about accuracy and reliability. Legal scholar Andrew Ferguson warns that AI’s tendency to hallucinate could insert convincing falsehoods into police reports, which are fundamental documents for determining “someone’s loss of liberty”³. These errors could have huge consequences in police investigations, particularly when AI tools make mistakes when summarizing complex evidence. The concern extends to how these tools might be manipulated; one expert notes that chatbots could offer police the “ultimate out” to spin reports, though preserving chatbot logs could help courts discover inaccuracies⁷.

Data security presents another critical challenge. Thousands of ChatGPT conversations, including some from law enforcement professionals, were briefly exposed and indexed on Google due to an OpenAI configuration error⁵. This incident demonstrates how using public, cloud-based AI tools risks exposing Sensitive Security Information (SSI), potentially compromising investigations and threatening officer safety. Following a separate leak of 300 Grok user chats, experts warn that exposed data from government employees can reveal mission-critical confidential work, operational protocols, and personal affiliations⁶.

AI Policing Tools: Capabilities and Risks
Tool	Function	Benefits	Security Concerns
Longeye	Analyzes digital evidence (calls, chats)	Reduces review time from hours to minutes	Potential data exposure, AI hallucinations
Draft One (Axon)	Generates police reports from body cam audio	Creates reports in seconds instead of minutes	Insertion of false information, accountability gaps

Privacy, Bias, and Regulatory Challenges

The expansion of AI in policing intersects with longstanding concerns about privacy and bias. Michael Price of the National Association of Criminal Defense Lawyers argues that the fundamental problem begins with over-collection of data, questioning “the need for software to interpret data that arguably shouldn’t be handed over in the first place”¹. He draws parallels to unreliable facial recognition that has led to wrongful arrests, suggesting that AI tools could amplify existing biases in the justice system. Community activist aurelius franco calls automated reporting “deeply troubling,” stating it “ease[s] the police’s ability to harass, surveil and inflict violence on community members,” with disproportionate impact on Black and brown people³.

Regulatory scrutiny is increasing as these technologies proliferate. The U.S. Federal Trade Commission has launched an inquiry into AI companion chatbots from companies including Alphabet, Character.AI, Meta, and OpenAI, focusing on how these companies develop AI characters, monetize engagement, and protect underage users and their data⁸. This investigation signals growing governmental scrutiny of the entire AI ecosystem, which will inevitably impact the tools available to and used by law enforcement. The collaboration between AI companies and police extends beyond tool provision; OpenAI has acknowledged it scans user conversations for harmful content and may, in extreme cases, refer threatening conversations to law enforcement⁹.

Security Implications and Recommendations

The security implications of AI adoption in policing extend beyond data protection to encompass system integrity and operational security. Police Chief Darrell Lowe concedes that “You can’t take the human out of the loop, and this is where sloppy police work will jeopardize technological advancements”¹. This human-in-the-loop necessity mirrors security operations center principles where automation supports but doesn’t replace human judgment. The technological arms race extends to criminal use of AI; law enforcement identifies AI chatbots (55%) and deepfakes (38%) as top tech tools fueling crime, creating a dynamic where police feel compelled to adopt AI to combat criminals who are already using it².

Security professionals should consider several key recommendations when evaluating similar AI systems. Experts advise using local AI models for sensitive work and never inputting confidential information into public AI systems, as “private” conversations with major AI platforms are often used as training data, creating permanent digital footprints¹⁰. Implementation should include strict access controls, comprehensive audit logging, and regular security assessments of AI providers. Organizations should develop clear policies governing AI use, including classification of data that can and cannot be processed through these systems, and establish protocols for verifying AI-generated outputs before action.

The integration of AI chatbots into police work represents a significant shift in how law enforcement processes digital evidence. While these tools offer compelling efficiency benefits, they introduce complex security challenges that require careful management. The balance between operational efficiency and security risk will define the successful implementation of these technologies. As police agencies continue to adopt AI solutions, maintaining robust security protocols, ensuring human oversight, and addressing ethical concerns will be essential for protecting both investigative integrity and civil liberties.