Advances in artificial intelligence (AI) are changing the way law enforcement agencies use body-worn cameras. In 2024, Oklahoma Police Sgt. Matt Gilmore experienced the benefits firsthand when an AI system generated a draft report from his body camera footage within seconds after a search for an escaped suspect. “It was a better report than I could have ever written, and it was 100% accurate. It flowed better,” Gilbert said.
Despite the advantages, there are limitations to this technology. Instances have been reported where AI misinterpreted objects or altered images incorrectly, highlighting the need for human oversight. The adoption of AI is seen as essential to balance public safety, efficiency, and civil liberties as the technology evolves.
One significant challenge with body cameras is managing the vast amounts of data they produce. Reviewing and redacting video footage is time-consuming and costly. For example, a recent audit by the Spokane Police Department found that targeted video redaction requires 11 minutes of staff time per minute of footage at a cost of $8.36 per minute, mainly due to labor expenses.
These costs add up across thousands of agencies nationwide, leading to interest in AI tools that can automate tasks like video review and paperwork reduction. This shift is particularly relevant for understaffed departments seeking to improve efficiency without compromising police work.
The market for body-worn camera technology is largely dominated by Axon, an Arizona-based company whose latest product connects cameras directly to the internet. This connectivity allows real-time integration with other surveillance sources such as doorbell cameras and license plate readers. In practical terms, dispatchers can combine live feeds from officers’ cameras with traffic or security footage during incidents like foot chases to provide immediate support.
Some new AI tools focus on officer behavior rather than external threats by offering prompts that encourage adherence to procedures or de-escalation tactics during high-stress situations. While these systems may improve professionalism, concerns remain among police unions about excessive monitoring through digital supervision.
Privacy issues are central as facial recognition becomes more common in policing. Past incidents—such as unauthorized live surveillance operations in New Orleans—have raised concerns about warrantless monitoring and lack of oversight by elected officials.
Currently, over 3,000 law enforcement agencies use Clearview AI’s facial recognition platform to compare images against a large database compiled from various sources online. Although many departments have policies against relying solely on facial recognition for arrests, investigations have revealed wrongful arrests linked to overreliance on these technologies.
As of early 2025, only California and Utah had enacted specific regulations governing how police can use AI with body-worn cameras; both require formal disclosures and clear policies about which tools are permitted for which purposes.
Internationally, Canada’s Edmonton Police Service recently became the first agency known to pilot live facial recognition alerts on body cameras targeting individuals wanted for serious crimes such as murder or aggravated assault.
Looking ahead, developers are testing systems designed not just for identification but also behavioral prediction—analyzing cues like aggressive movements or emotional distress—and even cross-referencing multiple data streams during urgent cases such as Amber Alerts.
“The ultimate question is not whether AI will be integrated into law enforcement, but how it will be used,” states the article’s author. “An algorithm may be able to process a crime scene in milliseconds, but it takes a human to understand it.”
