AI in Policing: Benefits, Risks, and Real-World Use Cases

In August 2025, an AI-powered drone surveyed 452 acres of steep, inaccessible terrain in Italy’s Cottian Alps. Within hours, it had analyzed more than 2,600 images and flagged a single red helmet—the clue that led rescue teams to a mountaineer missing for nearly a year. Repeated ground searches had come up empty, and humans would have needed weeks to comb the same terrain. The AI did it in an afternoon. 

That’s the power of using AI in policing and public safety. This tech can process evidence faster, find what humans miss, and give investigators more time to focus on tasks that require human judgement. However, it’s far from perfect. AI has also created serious legal challenges, documented wrongful arrests, and a growing debate about oversight, bias, and civil liberties. 

The reality of AI in law enforcement sits somewhere in between a miracle and a catastrophe. Bottom line: it’s complicated, evolving fast, and worth understanding in full.

How Police Departments Use AI Now

Police departments across the country have been implementing AI for quite some time now, and according to the data, the usage is only expected to grow over the next few years. In the U.S., the surveillance and law enforcement AI market is estimated to grow from its current size of $1.32 billion to over $34.72 billion by 2035.

The speed at which AI adoption happens is in direct correlation to a real-world problem: law enforcement agencies are understaffed and overwhelmed with digital evidence. 68% of investigators say the time required to review all the digital evidence they collect is preventing cases from moving forward and adding pressure to already heavy caseloads. To close that gap, many departments are turning to a variety of AI tools.

Here’s a look at where AI is actually being used today in policing, what’s working, and what’s raising red flags.

AI-Assisted Police Report Writing

Officers spend up to 40% of their work hours writing reports. That’s time not spent patrolling, investigating, or engaging with their communities. AI tools use body-worn camera audio to generate a police report in seconds, which officers can then review, edit, and sign off on before submission. The main tools currently deployed across U.S. departments include:

• Axon Draft One: Generates report narratives directly from BWC audio.
• Truleo Field Notes + AI Assistant: Takes BWC footage and generates accurate transcripts of the event as well as draft reports with built-in prompts for incident-specific details.
• CopEntry and PoliceNarratives.ai: Tools that take structured field notes or call data (rather than BWC recordings) and transform them into narrative text.
• Caseify: Enables officers to create reports using audio recordings, scanned documents, and images, converting multi-source input into a structured narrative.

While many tools are available that offer similar services, results have varied significantly by tool and even by department. Take Axon as an example. Fort Collins’ PD in Colorado found a 67% reduction in report-writing time using Draft One. However, Anchorage PD also tested the same tech and decided not to use it, given that it found no significant time savings for their officers.

Time savings aside, there are some concerns everyone can agree on. Most current technology doesn’t allow defense attorneys to compare what the AI wrote to what was ultimately submitted—a transparency gap that the industry is actively working to address.

Facial Recognition Technology

Facial recognition can match a still image from surveillance footage against a database of previously photographed individuals—a capability that has helped break cases that would otherwise go cold, particularly in identifying suspects from low-quality security camera footage.

However, this technology is not without its faults. Several Americans have been wrongfully arrested following misidentifications from AI, and some systems have been known to misidentify Asian and Black individuals at rates up to 100x higher than white men.

The root cause is consistent: officers treating AI output as confirmed without independent verification. 15 states now require corroborating evidence before an arrest can be made on a facial recognition match alone, and seven more are considering similar legislation. The NJ Supreme Court even recently ruled that prosecutors must disclose how law enforcement utilized facial recognition software in their investigations.

The bottom line: when used correctly (as a lead to investigate, not a conclusion to act on), the technology remains a legitimate investigative tool. But when it’s not used correctly, it can accelerate already present bias.

Predictive Policing Software

Predictive tools analyze historical crime data to help commanders allocate patrol resources more effectively. Several are in use today:

• SoundThinking: Builds patrol plans and flags areas at high crime risk based on historical data, among other features.
• Palantir: Pattern analysis across large datasets, used by departments including LAPD and New Orleans PD to surface connections across cases.
• IBM public safety platform: Aggregates and predicts crime patterns in real time.

The key implementation question is data quality. Models trained on historically skewed enforcement data can reinforce patterns instead of predicting them—which is why independent auditing and community transparency are increasingly standard requirements for responsible deployment.

Automated License Plate Readers (ALPRs)

Automated license plate readers scan passing vehicles and cross-reference plates against hotlists of stolen cars, wanted individuals, and other flags. Big names like Flock Safety and Motorola Solutions claim to have cameras active in almost 10,000 communities combined.

ALPRs have enabled real results—like the Cobb County police department recovering a stolen vehicle in 32 minutes using an AI-powered real-time crime center platform. But federal agencies have also accessed this data for immigration enforcement in cities with laws prohibiting that usage. 

Because of this, Congress opened an investigation into Flock Safety in late 2025, and several cities have cancelled their contracts. The public is making it clear that data-sharing policies and access controls are as important as the technology itself.

AI-Powered Evidence Analysis And Digital Forensics

This is where AI’s impact on investigative work is most direct. A single complex case can involve hundreds of hours of body-worn camera footage, jail calls, and recorded interviews. No investigator has time to review all of it manually.

AI tools purpose-built for this work can transcribe hours of audio and video, flag key conversations like confessions or contradictions, and make the content searchable across multiple files simultaneously. AI tools that make that material reviewable at scale include:

• Rev: Upload hundreds of files at once and analyze your entire case directly in the platform. Find witness inconsistencies, build your case chronology, and let our AI automatically surface moments that manual review might have missed.
• Longeye: Detectives can search hours of jail call audio to surface confessions, witness mentions, and timeline discrepancies in minutes.
• Cellebrite: Digital forensics across messages, emails, and call logs to map relationships between suspects and evidence.
• Axon Evidence AI: Transcript search, object identification in video, and automated footage prioritization for review.

For evidence that may end up in court, verifiability is everything. Rev’s investigative intelligence platform ties every output to a timestamp and source file, so findings can be cited and verified—not just generated. It’s a crucial best practice for police evidence management.

Drones And Autonomous Aerial Systems

Drone-as-First-Responder (DFR) programs are now standard tools at a growing number of U.S. agencies. Drones can reach a scene faster than patrol units, provide aerial situational awareness, scan hard-to-access terrain, and autonomously search for contraband or missing persons.

The rescue in Italy’s Alps illustrated the ceiling of what’s possible—AI analyzing thousands of aerial images to find a single clue in terrain too dangerous for ground teams. In law enforcement contexts, DFR programs are being used for traffic accident analysis, search-and-rescue operations, and active-scene monitoring. 

As of 2026, agencies procuring drone systems must verify compliance with FY2025 NDAA requirements, which restrict procurement of certain foreign-manufactured systems.

Balancing Technological Advancement And Public Concern

The public reaction to AI in policing is not uniformly negative—but it is conditional. A recent study in Police Practice and Research found that support is directly tied to perceived fairness and whether communities feel they have input. Residents who felt they had some voice in how these tools were used were significantly more likely to support them.

Axon’s survey of public safety professionals found that three in four officers believe AI will boost their productivity and investigative accuracy. But those same officers acknowledged a growing demand for AI ethics training and clearer guardrails on how tools are used. The technology is moving faster than the governance frameworks designed to oversee it—and that gap is where public trust erodes.

Other Challenges With Law Enforcement AI

Beyond public concern, agencies deploying AI face a few serious operational and legal challenges that aren’t always visible from the outside.

AI systems are only as good as the data they’re trained on. If an agency’s historical records contain inconsistencies, gaps, or systemic biases, the AI will reflect and amplify them. There’s also what researchers call “automation bias”— the tendency to defer to AI’s output rather than applying independent judgment. After all, good tech with poor protocols produces worse outcomes than no technology at all.

Finally, many agencies lack the technical infrastructure and trained personnel to deploy AI responsibly. Smaller departments in particular may have trouble getting up to speed on complex tech while prioritizing their day-to-day responsibilities. Responsible adoption requires investment in training, governance frameworks, and ongoing auditing—not just a software subscription.

For a broader look at the challenges facing modern law enforcement and how technology intersects with them, read our deep dives into challenges for law enforcement agencies and current trends in law enforcement.

Rules And Regulations Around AI In Law Enforcement

The regulatory landscape is maturing. A December 2025 executive order (Executive Order 14365) established a federal AI framework while explicitly preserving state authority over law enforcement AI procurement. States are exercising it:

• 15 states restrict arrests based solely on facial recognition, requiring corroborating evidence first.
• Colorado’s AI Act (June 30, 2026) requires risk assessment documentation for high-risk AI, including law enforcement tools.
• California’s Transparency in Frontier AI Act adds accountability requirements for large AI model developers.
• Utah introduced a bill requiring disclosure when AI drafts a police report—a transparency step that builds credibility with courts and communities.
• 46 states have enacted deepfake legislation; a California judge issued a terminating sanction after deepfake videos were submitted as evidence in September 2025.

Learn more about recent AI rulings in Texas, New York Rule 161, and Florida AI citation guidelines on our blog.

The Investigator’s Playbook For Integrating AI Responsibly

Investigators and detective units adopting AI tools face a different set of considerations than patrol operations. The goal isn’t just efficiency—it’s building cases that hold up under legal scrutiny, preserving the chain of custody, and maintaining the accuracy standards courts and prosecutors require. Here’s a framework for doing that responsibly.

1. Audit Before You Adopt

Not all AI tools are built for investigative environments. Before deploying any platform for evidence analysis, transcription, or case management, agencies should require independent validation data. Ask specifically about accuracy in difficult audio conditions (background noise, overlapping speech, non-studio recordings), bias testing across demographic groups, and chain-of-custody protocols.

The National Policing Institute recommends creating governance frameworks before adoption, not after. That means defining which use cases are permitted, what human oversight is required at each step, and how the technology’s outputs will be documented and disclosed in legal proceedings.

2. Keep Humans In The Decision Loop

Every credible AI tool in law enforcement is designed with an “officer in the loop” requirement—AI generates a draft or a flag, but a human reviews and approves before any action is taken. This is non-negotiable for high-stakes events like arrest decisions, warrant applications, and case filings.

The facial recognition wrongful arrests mentioned above weren’t just failures of the technology—they were also failures of the protocol, as officers skipped independent verification steps. Training programs should explicitly address automation bias and make human confirmation a must-do.

“The most important safeguards are proper oversight, real-world testing, and tight control over how the system is used. AI tools should be tested in the same conditions they will actually be used in, because accuracy can change depending on lighting, sound quality, camera angles, or the type of people involved,” explains Jack Charman, Private Investigator at National Private Investigators. 

“Ongoing checks for bias and accuracy are also essential, especially as expectations from regulators continue to increase.” 

3. Prioritize Verified And Citable Outputs

For investigative work, one of the most important qualities of an AI tool is verifiability. Summaries and transcripts that can’t be traced back to a specific timestamp in a specific source file create problems down the line: for discovery, cross-examination, and even in suppression hearings.

When evaluating transcription and evidence analysis tools, ask how findings are cited. Are all outputs cited to their source? Is there a clear audit trail? Can it create court-ready documents? Tools built for the general public typically can’t say yes to these, while platforms built for legal and investigative environments can.

Rev’s platform ties every output to a timestamp and source file, so investigators can verify exactly where each finding came from.

4. Address Data Security And Chain Of Custody Upfront

Investigative evidence is sensitive by nature. For criminal justice environments, CJIS compliance is a requirement that not every AI vendor meets. 

Equally important is understanding where your data goes. Some AI platforms train their models on user-submitted data, which can be a huge issue when dealing with sensitive investigation materials. Zero data-sharing architecture, where evidence stays in a closed loop and is never used to train third-party models, is a crucial distinction. But it is regularly becoming required (see New Jersey v. Tybear, New Jersey v. Arteaga).

Rev meets both these requirements. Our Unlimited plans are CJIS, HIPAA, and SOC2 compliant, and we will never sell your data to third parties or use your data to train models.

5. Build Transparency Into The Process

Courts, prosecutors, and defense attorneys are paying close attention to how AI is being used in investigations and report writing. Departments that can document their AI workflows—what tools are used, how outputs are reviewed, how errors are caught—are in a substantially better position than those that can’t.

This includes disclosure. The Utah bill requiring disclosure of AI-drafted reports reflects a broader trend: stakeholders in the justice system want to know when AI was involved in producing evidence or documentation. Getting ahead of disclosure requirements now builds credibility and trust with prosecutors, courts, and the communities they serve.

6. Plan For Staffing Realities

We’ve written about the growing issue of police shortages before, and since then, the problem has only continued to grow. AI is often positioned as a force multiplier in this context, reducing administrative burden so investigators can focus on case work.

That potential is real. But achieving it requires honest internal evaluation on where time is lost, which tools have been independently validated, and what training and guidelines will be needed. Technology adopted to address a staffing problem without adequate implementation planning can create new problems rather than just solving existing ones.

The Future Of Intelligence In Policing

Policing technology in 2026 is defined less by any single tool than by the convergence of multiple AI systems. Think real-time crime centers pulling from drones, ALPRs, and body cameras simultaneously; generative AI drafting reports while investigators are still on scene; advanced analytics identifying patterns across thousands of case files. 

The National Policing Institute identifies AI governance as one of the defining issues of this year—not because the technology is slowing down, but because adoption is consistently outpacing the frameworks designed to govern it.

The next frontier for AI in public safety and investigations is multimodal analysis: combining audio, video, text, and structured data in ways that give investigators a more complete picture earlier in a case. Agencies that build strong documentation practices and clear AI governance frameworks now will be far better positioned for those evidentiary challenges than those that don’t.

What won’t change: the judgment calls at the center of investigative work remain human. The goal of policing technology—when it’s implemented well—isn’t to replace that judgment but to make sure it has the full picture to work from.

Better Evidence. Stronger Cases.

AI in policing is most valuable where it does what humans can’t do at scale: surfacing the moment in hundreds of hours of recordings that cracks a case, turning chaotic scene audio into a searchable, citable record, or giving investigators the full picture before they make the judgment calls only they can make. 

Rev’s platform is built for exactly that—accurate transcription in difficult audio environments, every output tied to a source file, CJIS-compliant, and zero data sharing with third-party models. We can help you work smarter, without sacrificing accuracy.