By Steve Lindsey
Law enforcement agencies face mounting pressure to enhance public safety while facing unprecedented staffing challenges. A new approach using agentic artificial intelligence — AI that autonomously detects, validates and responds to security threats — is transforming how agencies address crime prevention.
While law enforcement has traditionally been reactive to incidents, agentic AI introduces proactive capabilities that complement existing approaches using behavioral data. AI agents can help alert agencies about potentially risky behavior before an incident happens, bridging the gap between private sector security concerns and public safety operations.
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Understanding Agentic AI for crime prevention
When we think about the role of law enforcement in the public sector, officers are usually called on a reactive basis after a crime has been committed or an incident gets out of hand. The private sector, which accounts for a significant portion of law enforcement’s time, is more interested in preventing something from happening instead of reacting and catching a criminal afterwards. There’s a disconnect currently between the proactive response businesses want, and the reactive response that agencies are staffed to provide.
Agentic AI differs from conventional surveillance by understanding behavioral context through visual large language models. Rather than simply identifying a person or vehicle, these systems interpret behaviors within their environment to determine potential threats.
Just because someone is laying down or running doesn’t mean they’re doing anything wrong. We need to understand the context of why they’re doing that. Laying down next to a car might indicate a health condition, someone relaxing, or someone stealing a catalytic converter — context matters in understanding behavior, and now, AI technology is becoming sophisticated enough to tell the difference between someone casing a vehicle, versus someone casually strolling through a parking lot.
Deterrence through intelligent response
When potential threats are detected, agentic AI systems can deliver customized audio messages. Unlike predictable motion-activated recordings, these systems generate dynamic responses that reference specific details about the individual’s appearance or location.
For example, the system might address “the individual in the red jacket by the loading dock” or “the person with the blue backpack in the crosswalk.” Pre-recorded warnings are very effective for making potential criminals aware of surveillance, but when the “voice from above” provides this level of specific detail, it really changes the reaction of someone being monitored and can dramatically improve deterrence.
Importantly, these systems focus on behaviors rather than demographics — how a person is moving, where they’re standing, how long they’ve been in one spot — the concerning behavior we want to monitor. We can use specific characteristics to call someone out, so they know we’re talking about them, but the AI detection is looking at behavior to trigger the alert.
Streamlining evidence collection
Beyond prevention, agentic AI can also transform how video evidence is collected and processed — a traditionally time and resource-intensive process. Not only finding digital evidence, but extracting, preparing and presenting evidence for a court can take weeks if not months.
Agentic AI addresses this by automatically labeling, associating and classifying video clips across multiple cameras during an incident. This cataloged evidence package can be instantly retrieved when needed. Humans must review and validate this evidence, but AI can turn five hours of video into five 30-second clips that showcase the most likely evidence of the crime, making it much easier for the officer to find the relevant file.
That said, the best time to collect evidence is while it’s happening. If you can use AI to label events, associate all the video clips from various cameras in one package, classify the violations, tag everything and package it up right after the event, it’s much easier to create the required information for use in the criminal justice process.
Tactical recommendations for agencies
For public agencies considering agentic AI implementation, these are several practical recommendations to prepare for AI:
- Prioritize cloud-based solutions: Information feeding into cloud services makes it much easier to access from the station house, the field, outside a court room, or anywhere it’s needed, much easier than trying to securely access on-premises systems. Storing as much as possible in a secured cloud enables multi-location access and simplified data sharing between jurisdictions as needed.
- Leverage established partnerships: Rather than building proprietary systems, agencies should connect with existing ecosystems. Solution providers can help withimplementation so agencies can stick to what they’re best at — law enforcement — instead of trying to manage IT from scratch.
- Focus on behavior-based detection: Train staff to understand that the best AI systems detect behavioral patterns, not demographic characteristics. This approach enhances both effectiveness and community trust.
- Start with high-priority use cases: Begin implementation in areas with clear return on investment, such as evidence gathering for repeat offenders like car thieves, or real-time alerts for known threats around specific hot spots.
Balancing security and privacy
With advanced surveillance capabilities, privacy concerns are best addressed with secure implementation and proper training. Staff should understand that the accuracy and timeliness of today’s AI systems can be a major force multiplier, and behavior-based agentic AI is much less susceptible to bias than traditional approaches.
Balancing security and privacy encourages focusing detection on behaviors rather than individuals and by limiting human review to only the most relevant incidents. No matter how big an Operations Center a jurisdiction has, nobody can watch every camera in the world. We know that 99.9% of all video recorded is never seen by human eyes. With AI, we can use technology to monitor for anomalies, and only flag relevant incidents for humans to monitor.
By implementing these tactical recommendations, public agencies can enhance their situational awareness while maintaining community trust — optimizing limited resources while improving public safety outcomes.
The path to Agentic AI
As police agencies nationwide continue to face staffing challenges, agentic AI offers a practical force multiplier that enhances capabilities without increasing personnel costs. The technology allows agencies to do more with existing resources while potentially improving outcomes.
Most importantly, agentic AI should augment human decision-making, not replace it. The goal is to automate routine monitoring and basic deterrence while freeing officers to focus on complex situations requiring human judgment and community engagement.
By implementing these recommendations, police agencies can enhance their situational awareness, improve evidence collection, streamline case management, and strengthen community trust — all while making more efficient use of limited resources. As AI technology continues to evolve, the agencies that adapt most effectively will be best prepared to meet the public safety challenges of tomorrow.
About the author
Steve Lindsey is the CTO for LiveView Technologies (LVT).
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