Stephenie Slahor, Ph.D.
Artificial Intelligence (AI) is no longer a distant concept. Once confined to research laboratories and science fiction novels, AI is beginning to shape and promises to further transform how law enforcement agencies patrol, investigate and engage with communities.
Across the United States, law enforcement leaders are grappling with both the opportunities and the risks presented by this technology.
The pace of innovation is rapid. From predictive policing to automated video analysis, AI offers new ways to anticipate threats, allocate resources and uncover evidence hidden in mountains of digital data. Yet, with great capability comes new risks: bias, privacy concerns, legal uncertainty, and public trust challenges.
For police chiefs, commanders and policymakers, the discussion is not whether AI will transform policing, but how to ensure it does so responsibly, ethically and effectively.
Human and Machine Intelligence: Different Strengths
Before diving into applications, it is important to understand what AI is – and what it is not.
Human intelligence is flexible, intuitive and deeply contextual. Officers draw on years of training, lived experience and situational awareness when making decisions. Each individual brings a unique perspective shaped by education, cultural background and professional exposure. The ability to adapt on the fly, weigh competing priorities and exercise discretion is a human strength which machines cannot replicate.
AI, by contrast, excels in processing vast datasets, spotting patterns invisible to the naked eye and executing repetitive tasks with precision. It can scan thousands of hours of video in minutes, cross-check license plate data against millions of records and generate predictive models of crime patterns.
Neither intelligence replaces the other. Instead, the future of policing lies in combining human judgment with machine-driven insights – many minds working together with many machines, producing vantage points and strategies which neither could achieve alone.
CURRENT APPLICATIONS OF AI IN LAW ENFORCEMENT
Predictive Policing and Crime Forecasting
Predictive policing has emerged as one of the most visible uses of AI. In the past, systems such as HunchLab, developed by Azavea, and PredPol, created through a collaboration between UCLA researchers and the Los Angeles Police Department, analyzed past crime reports, service calls and environmental data to forecast where and when crime is most likely to occur.
The aim was not to predict specific crimes or individuals, but to identify “hot spots” where police presence may deter offenses. In practice, these systems have been used to plan patrol routes; adjust staffing levels during high-risk hours; and allocate resources to neighborhoods facing spikes in burglaries, assaults or thefts.
While early efforts faced criticism – particularly, concerns that predictive models concentrated enforcement in minority communities – newer approaches emphasize transparency and community engagement. For example, some departments now use predictive analytics not only for patrol allocation, but also for prioritizing community meetings, outreach programs and preventive services in areas identified as vulnerable.
In the future, predictive tools are expected to evolve beyond geography. They may incorporate social dynamics, environmental changes and even real-time sensor inputs, offering commanders a much more comprehensive picture of emerging threats.
AI Powered Video Analytics and Virtual Guarding
Surveillance has long been a cornerstone of public safety, but the sheer volume of footage now available – millions of hours from public, private and police cameras – makes human review nearly impossible. AI steps in by scanning video feeds for anomalies: sudden crowd surges, abandoned packages, vehicles circling suspiciously, or individuals loitering in sensitive areas.
Systems like IntelliSee integrate with existing cameras rather than requiring new hardware, lowering costs and simplifying adoption. They provide real-time alerts which allow officers to respond immediately. Unlike passive video systems, which merely record incidents for later review, AI-powered analytics actively monitor, detect and flag threats as they happen.
Applications include monitoring crowd density at parades, concerts or sporting events; detecting noncompliant visitors or vendors at secured facilities; tracking environmental conditions such as smoke or heat which may pose risks; and generating recommendations for safer site design based on detected vulnerabilities.
This shift from reactive to proactive monitoring could fundamentally change how law enforcement and private security collaborate in safeguarding public spaces.
Facial Recognition Technology
Few technologies generate as much debate as facial recognition. The ability of AI to measure, digitize and compare facial features offers enormous investigative value –but also raises profound ethical concerns.
AI facial recognition can identify suspects from surveillance footage; verify the identity of individuals in custody; assist in locating missing persons; generate accuracy scores; or suggest multiple potential matches.
However, limitations persist. Poor lighting, obstructions like masks or sunglasses, and aging can all reduce accuracy. Studies have shown higher error rates for individuals with darker skin tones, fueling criticism that the technology may disproportionately harm minority communities.
Some jurisdictions, including San Francisco and Portland, have banned real-time use, citing privacy concerns. Others, such as Florida and Virginia, continue to expand its use under strict oversight. Most agencies employ facial recognition as an investigative aid; leads are confirmed by human investigators before action is taken.
It is worth noting that human recognition is not flawless either. Roughly two to three percent of people suffer from prosopagnosia, or “face blindness,” while others are exceptionally skilled “super recognizers.” AI is evolving toward the latter end of the spectrum – capable of identifying faces even at odd angles or low resolutions. Still, errors remain possible, reinforcing the need for human judgment and clear policy boundaries.
Automated License Plate Recognition (ALPR)
AI-enabled ALPRs have become common on highways and patrol vehicles. They capture plates at high speeds, across multiple lanes and in low light. Partial plate captures can still generate leads, thanks to pattern recognition algorithms.
Benefits include identifying stolen vehicles in real time; tracking suspect movements after crimes; providing evidence in court through time-stamped footage; and supporting Amber Alerts and other urgent investigations.
Privacy concerns focus on data retention. Many departments now adopt policies limiting how long non-hit data is stored, balancing investigative value with civil liberties.
Digital Forensics and Cybercrime
As society moves online, so does crime. AI is now indispensable in digital forensics. Investigators use AI to extract relevant files from seized phones, laptops and cloud accounts; scan massive volumes of images and videos for illicit content, such as child exploitation material; map communications across encrypted apps and platforms; and detect patterns in ransomware campaigns, phishing operations and financial fraud.
The scale of digital evidence is staggering – often terabytes per case. Without AI, sorting through this mountain of data would be nearly impossible. Cybercrime units now rely on AI not only to solve cases, but also to defend critical infrastructure, businesses and individual citizens against online threats.
Gunshot Detection and Acoustic AI
Cities plagued by gun violence increasingly deploy AI-powered acoustic sensors. Arrays of microphones triangulate the origin of gunfire and notify officers within seconds. This technology has reduced response times; enabled faster medical aid to victims; and has helped identify patterns of illegal gun use in neighborhoods.
Next-generation systems integrate with video analytics, automatically directing cameras toward the sound source. These integrations provide real-time situational awareness, giving officers a tactical advantage before they arrive on scene.
Traffic Safety and Enforcement
Traffic enforcement is another area where AI makes a difference. Automated systems can detect speeding, distracted driving and red light violations without requiring officers to be physically present.
Some jurisdictions take it further by using AI to predict high-risk intersections, allowing agencies to proactively deploy resources. Others employ systems which analyze driving behaviors – hard braking, sudden lane changes, tailgating – to flag aggressive drivers.
The overarching goal is reducing accidents and fatalities. With many departments facing staff shortages, AI provides a force multiplier, supplementing traditional traffic enforcement and freeing officers to handle emergencies.
Open-source Intelligence (OSINT)
AI now powers advanced monitoring of open-source platforms, from mainstream social media to niche forums and dark web markets. Applications include detecting planned violent protests or school shooting discussions; monitoring disinformation campaigns which could destabilize communities; tracking illegal drug sales or arms trafficking; and identifying emerging extremist movements.
AI systems can cluster conversations, assess sentiment and flag trends long before they escalate. During elections, high-profile trials or major events, OSINT tools allow agencies to prepare for unrest and respond more effectively.
Body-worn Cameras and Evidence Management
Body-worn cameras, dash cams and vehicle-mounted video systems are now standard in American policing. AI improves their utility by automatically tagging use-of-force incidents; detecting potential misconduct markers; creating searchable databases of footage; and offering multiple angles for greater evidentiary accuracy.
These features enhance both accountability and operational efficiency. Supervisors no longer need to review hours of raw footage manually. AI flags the most relevant sections for human review.
Drones, Robotics and Autonomous Systems
Drones and robots already play a role in bomb disposal, tactical entries and search and rescue operations. AI enhances these tools by enabling autonomous navigation, object recognition and real-time analysis.
Examples include drones scanning for traffic accidents or fire outbreaks; autonomous searches for contraband or explosives; and robotic “remote presence” in dangerous environments, reducing officer risk. Another notable example was reported in August of 2025. Rescuers in Italy’s Piedmont region recovered the body of a mountaineer who had been missing for nearly a year in the Cottian Alps. The discovery came after an AI-powered drone surveyed roughly 450 acres from above, capturing more than 2,600 detailed images. Artificial intelligence quickly analyzed the thousands of images and flagged a red helmet belonging to the hiker.
The National Alpine and Speleological Rescue Corps (CNSAS) had previously conducted multiple ground searches, but steep cliffs, loose rock and sudden weather changes made the terrain extremely dangerous. By contrast, the AI system analyzed the images in hours, a process which would have taken human experts weeks and pinpointed the critical clue which led rescuers to the site.
Officials emphasized the success came from combining advanced technology with skilled human judgment. They noted AI-driven drones are increasingly being used worldwide to speed up search efforts, reduce risks to rescuers and improve chances of saving lives.
Challenges remain – battery life, public acceptance and even natural obstacles. At Grand Canyon National Park, for example, native falcons have been known to attack drones midflight. Nevertheless, robotics represents one of the most promising frontiers of AI in policing.
Benefits of AI for Policing
The advantages of AI are extensive:
- Operational Efficiency – Automating administrative work allows officers to focus on core policing tasks.
- Enhanced Investigations – Data analysis reveals hidden connections among suspects, locations and events.
- Crime Prevention – Predictive systems and anomaly detection enable proactive intervention.
- Officer Safety – Real-time alerts about weapons, vehicles or threats prepare officers before arrival.
- Community Service – Chatbots and analytics streamline nonemergency interactions and target outreach.
- Evidence Management – Automated tagging and indexing simplify review, storage and courtroom presentation.
- Resource Optimization – AI helps departments maximize limited manpower and budgets.
Challenges and Risks
Yet, with benefits come risks:
- Bias and Fairness – Algorithms trained on historical data risk perpetuating existing disparities.
- Privacy Concerns – Mass surveillance raises fears of a “Big Brother” state.
- Legal Ambiguity – Courts and legislatures are still defining standards for evidence generated or analyzed by AI.
- Transparency – Lack of public communication fuels distrust.
- Overreliance – Officers must avoid blindly trusting algorithmic outputs.
- Cybersecurity – AI platforms themselves can be hacked or misused.
- Civil Liberties – The FTC has warned that biometric data could expose sensitive personal details, from health conditions to political affiliations.
The Future of AI in Policing
Looking ahead, several trends will define the next decade:
- Generative AI will further assist with drafting reports, summarizing interviews and translating evidence.
- Multimodal Platforms will integrate video, acoustic, biometric, and geospatial data into unified dashboards.
- Behavioral Analytics may recognize aggression, stress or deception during encounters.
- Robotics and Drones will gain autonomy, patrolling public spaces or monitoring high crime areas.
- Community Policing with AI will align resources with community needs, strengthening trust.
- Interoperability will allow data sharing across federal, state and local agencies.
- Ethical Regulation will mandate algorithmic audits, explainability and human oversight.
Stephenie Slahor, Ph.D., J.D., is a writer in the fields of law enforcement and security. She can be reached at drss12@msn.com.