Real-Time Crime Centers have transformed policing by integrating video, location data, intelligence and incident information. But their fundamental logic remains reactive: an incident occurs, information is verified and resources are deployed. By then, the public has already been exposed to danger and, too often, harm has already been done.
This is no longer sufficient. Police and public authorities must ask not only what is happening now, but what is beginning to develop, which warning signs are accumulating, and how to stop the next incident before it occurs.
Most of the required capabilities already exist. Police hold intelligence and crime data; hospitals see violence-related injuries; municipalities know where infrastructure is failing; schools and social services recognize growing distress; transportation systems identify unusual movement; and residents often sense deterioration before it appears in official statistics.
Yet these fragments remain scattered across separate institutions. Each organization sees part of the picture, but no one consistently sees the whole. Weak signals are ignored because, taken individually, they do not appear serious enough to trigger action.
Artificial intelligence can change that by connecting seemingly unrelated signals, identifying anomalies and revealing patterns that no single agency could detect alone. A series of minor disturbances, recurring business complaints, deteriorating lighting and a rise in emergency-room admissions may look insignificant separately. Together, they may constitute a clear warning that violence is escalating.
That warning must not produce automatic police action or label anyone a future offender. It should trigger human assessment and a proportionate preventive response. The goal is not to predict people, but to identify dangerous conditions before they produce victims.
(Photo: Shutterstock, Illustration: Levia Tushinsky)
Consider an entertainment district where assaults are rising. The traditional response is to add patrols after repeated incidents. A prevention-oriented approach would connect police reports with hospital admissions, traffic patterns, business complaints and environmental conditions. It might reveal that violence peaks when venues close, public transportation stops and lighting is poor. The answer may combine targeted police presence, extended transportation, better lighting, alcohol enforcement, temporary cameras and coordination with venue owners. No single measure is revolutionary. Their synchronized use before the next incident is.
The same logic applies to recurring burglaries. Attempted break-ins, damaged streetlights, vehicles repeatedly observed near several locations and citizen reports may form an early pattern. Connecting those signals can enable authorities to repair vulnerabilities, warn residents, adjust patrols and focus investigative resources before the pattern becomes a crime wave.
The shift is required globally because the main obstacle is not technology; it is mindset. Public-safety organizations and local authorities are still structured to respond within institutional boundaries. Real-time prevention requires them to share information, define joint responsibility and act as one network around a common risk. It is a move from agency-centered operations to citizen-centered security.
A Real-Time Prevention Center (RTPC) may be the organizational expression of this approach, but the center itself is not the point. The point is to create a continuous mechanism that turns scattered information into early warning, early warning into assigned responsibility and responsibility into coordinated action. A sophisticated dashboard that no one is obliged to act upon is not prevention. It is merely a better view of the next failure.
Maj. Gen. (ret.) Boaz Gilad PhotoThe public must also be part of the system. Residents and businesses are essential sensors and partners; trusted reporting channels and feedback can strengthen both prevention and confidence.
The safeguards are clear: defined legal authority, strict access controls, auditable algorithms, human validation, transparent oversight and active testing for bias and false positives. Prevention cannot be built at the expense of civil rights, because without public trust the information and cooperation on which the model depends will disappear.
The price of maintaining the current approach is measured in repeat victims, avoidable escalation, neighborhoods that lose confidence in government and resources repeatedly spent responding to patterns that should have been identified earlier. Continuing to invest only in faster response means accepting that the system will remain one step behind.
The breakthrough is not another camera, sensor or platform. It is the integration of existing capabilities and the use of AI to turn fragmented signals into actionable early warnings. Public safety should be measured not only by response times, arrests or crimes solved, but by risks addressed early, incidents prevented, victims spared and public trust. The next frontier is not simply responding to crime in real time, but preventing it.
- Major General (ret.) Boaz Gilad, a former senior official with the Shin Bet and the Israel Police, is CEO of S.T. Impact Ltd. and a researcher at the Institute for Personal Security and Community Resilience at the Western Galilee Academy.