The global AI in security market is projected to grow from $22.4 billion in 2023 to $60.2 billion by 2028, with a compound annual growth rate (CAGR) of 21.9%, according to a report by MarketsandMarkets (2024) . This sharp rise reflects the growing demand for AI in public safety, as governments and organizations increasingly turn to intelligent systems to manage risk, monitor activity, and respond faster in real-time.
A study conducted by McKinsey discovered that more than 75% of smart city projects now embed AI surveillance systems capable of automatic monitoring, that can detect anomalies in real-time and act decisively in emergencies. This increasingly prevalent algorithmic infrastructure for public safety is starting to influence how governments and organizations think about public safety.
With the increasing implementation of artificial intelligence , it is fundamentally reshaping aspects of traditional surveillance policies and upgrading public safety systems This article explores how AI in public safety is transforming key areas like facial recognition, real-time video analytics, predictive policing. Each section will cover a specific way AI is contributing to more proactive, efficient, and scalable public safety solutions.
Top Benefits of AI in Public Safety and Surveillance
1. Faster Response Times
AI-based robotics security systems are able to automatically identify threats and/or suspicious activity and inform the right people immediately.
Whether spotting a weapon, a fight, or the sound of a gunshot, AI can work in real-time — more quickly than a human operator, who would be trying to scan multiple screens simultaneously. This speeds up emergency response and can save lives.
2. Reduced Human Error
Traditional surveillance relies on people watching video feeds, which is prone to fatigue, distraction, or simple mistakes. AI doesn’t get weary or bored. It will be able to scan constantly for anomalous patterns of behavior invisible to the human eye, helping to prevent crucial lapses that could enable crimes to go unreported.
3. 24/7 Monitoring Without Fatigue
AI surveillance tools operate around the clock, seven days a week. Unlike humans, these systems don’t require breaks or sleep. They can keep an eye on hundreds of camera views simultaneously, sending security personnel into action only when something out of the ordinary occurs for a more efficient operation.
4. Smarter Resource Allocation
AI can analyze historical crime data and inform law enforcement decisions about where to deploy officers or install cameras. Rather than patrolling randomly, agencies can pinpoint high-risk areas or periods. This results in more efficient coverage using fewer resources, and it enables departments to make data-driven decisions.
5. Higher Accuracy in Identification
Older facial recognition systems have been prone to false positives. AI has greatly improved that. Today’s systems can match faces more accurately, even under dim lighting or at awkward angles. This means fewer innocent people flagged and better chances of catching the right person, especially in large crowds.
6. Lower Operational Costs Over Time
Although it is costly to implement the systems themselves, they ultimately reduce costs over time. Agencies have saved on their staffing needs, overtime, and manual processing. Cloud systems also minimize reliance on bulky hardware, reducing maintenance costs, and enhancing scalability.
How AI Is Used in Public Safety & Surveillance
Real-Time Video Analytics
AI-powered video analytics, in the first case, allows surveillance systems to analyze video footage in real-time, which means you don’t need to have people watching on screens, to entangle ridiculous suspicious behavior, to find an object, ascertain that it’s illegally left there, and to identify if something is going strangely. All of which require a human body to evaluate those functions. Now, law enforcement can quickly assess if what is happening requires action.
Facial Recognition and Identity Matching
Next, AI has significantly improved the accuracy and reliability of facial recognition systems. Companies like Coram AI are leading the way in transforming ordinary IP cameras into powerful facial recognition cameras . For example, the police can utilize AI to correlate facial representations of suspects, from video footage to the representations of persons in a database. By using AI to match the representation of a person, police can more accurately identify subjects and reduce false positives, which was a huge problem for older systems, as far as accuracy is concerned.
Use of Cloud-Based Surveillance
Moreover, AI systems are increasingly integrated with cloud-based security cameras to facilitate remote monitoring, data storage, and scalability. Many systems provide cloud solutions to facilitate improved real-time responsiveness with reduced infrastructure costs. The AI algorithms run in the cloud to process the footage, allowing agencies to leverage intelligent surveillance manufacturers to deploy faster and cheaper.
Predictive Policing and Crime Forecasting
Furthermore, predictive policing by the police department can be developed by leveraging artificial intelligence to analyze historical crime data to find which areas were at the greatest risk of future crime, which allows law enforcement to better allocate their resources. Many cities have reported crime reductions based on AI enhanced patrolling.
License Plate Recognition (LPR)
AI also aids license plate recognition systems in many ways. First, it helps to improve the accuracy of recognition systems in a range of conditions, including poor light or high speed and a number of other challenging physical environments. Second, police departments around the country have begun using AI-based LPR to track stolen vehicles or catch uninsured drivers and other violations. Last, LPR systems can be incorporated alongside other surveillance networks to broaden coverage or data collection.
Gunshot Detection and Audio Surveillance
Equally crucial, artificial intelligence-based audio sensors can detect both gunshots and unusual audio in real-time. This allows police to locate incidents faster and send first responders more accurately. These systems are used in public places and can also be connected to video feeds to offer a holistic security solution.
Automated Monitoring of Public Spaces
AI can also monitor public spaces, such as airports, train stations, and stadiums. Machine learning models are developed to track people’s behaviors, and become aware of abnormal behavior, unattended luggage, or crowd dynamics. In these situations, BOAI will notify security to act with less effort.
Integration with IoT and Smart City Initiatives
Moreover, AI is instrumental in building smart city projects that rely on interconnected surveillance systems through IoT devices to enable data sharing across traffic cameras, environmental monitoring technologies and emergency responders that allow cities to better coordinate their responses to their incidents.
Data Privacy and Ethical Considerations
However, with the acceleration of AI and surveillance technology development, both privacy and ethical issues have arisen. Technologies like AI, and more specifically facial recognition and predictive analytics , have highlighted several issues, such as bias, transparency, and data misuse. Regulatory bodies are now attempting to create safeguards for AI systems to use AI in a responsible manner with oversight.
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Real-World Applications of AI in Public Safety
1. Los Angeles: AI-Powered Real-Time Threat Detection
Los Angeles has implemented an AI-powered surveillance system that continuously monitors public spaces using advanced cameras equipped with facial recognition and motion detection.
The system analyzes live video feeds to detect anomalies—such as unattended objects or suspicious gatherings—and automatically alerts law enforcement for rapid response.
2. Chicago: Predictive Policing
Chicago uses AI-driven predictive policing, where machine learning algorithms analyze historical crime data, social media, and urban sensor information to forecast crime hotspots.
Police resources are then strategically deployed to these areas, enabling a proactive approach to crime prevention and more efficient use of law enforcement personnel.
3. Amazon Go: Retail Surveillance
Amazon Go stores utilize AI-powered cameras and sensors to track customer movements and purchases. The “Just Walk Out” technology enables a cashier-less shopping experience, automatically billing customers for items they pick up. This system also helps detect suspicious activities and prevent theft in real-time.
4. Changi Airport, Singapore: Smart Video Analytics
Changi Airport employs AI-powered video analytics for enhanced passenger safety. The system can identify unusual behavior, detect unauthorized access, and optimize crowd management, helping prevent security breaches and improve overall airport safety.
5. Tesla: Smart Vehicle Security
Tesla integrates AI with its vehicles’ cameras and sensors to detect attempted break-ins or suspicious activity. The system can trigger alarms and send alerts to the vehicle owner, enhancing both vehicle and personal security.
Future Prospects and Challenges
It is likely that AI will provide even more impact on fulfilling public safety mandates in the future. That said algorithmic prejudice, cyber security, and data governance must be dealt with concurrently. Investment in ethical AI and trustworthy data governance will be paramount for continued advancements forward.
Conclusion
AI is transforming surveillance and public safety systems from reactive systems to proactive systems, making them more efficient, and scalable. AI helps agencies validate their security challenges from start to finish, be it with real-time video analytics or through predictive policing. While performance limitations related to ethics and regulatory gaps continue to exist, developing new technology in public safety and use by agencies will continue to grow.
Key Takeaways
The AI in the security market is projected to reach $60.2 billion by 2028.
AI enhances real-time monitoring, facial recognition, and crime prediction.
Cloud-based security cameras from platforms like Coram offer scalable AI integration.
Ethical and legal considerations must guide future AI surveillance deployments.
Integration with IoT and smart city systems enables coordinated responses.
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FAQs
What is AI-powered surveillance? AI-powered surveillance refers to the use of artificial intelligence technologies to automatically monitor, analyze, and respond to data from security systems such as video cameras, sensors, and databases.
Is AI surveillance legal? Legal status varies by country and jurisdiction. Many regions require public disclosure, consent, and limitations on how data is collected and used.
Can AI reduce crime rates? AI can help reduce crime rates by improving response times, optimizing patrol routes, and enabling predictive policing. However, its effectiveness also depends on implementation and oversight.
How is AI used in public safety? AI is used in public safety to monitor environments in real time, detect threats, and help law enforcement respond faster. Common uses include facial recognition, behavior analysis, gunshot detection, and predictive policing. These tools help agencies prevent incidents, manage crowds, and improve overall security.
How is AI used in road safety? AI supports road safety through smart traffic cameras, automatic license plate recognition, and real-time traffic management systems. It helps detect violations like speeding or running red lights, predicts accident-prone zones, and enables quicker response to crashes or breakdowns. Some cities also use AI to optimize traffic light timing to reduce congestion and improve pedestrian safety.
What is an example of AI safety? A practical example is an AI system that detects a person leaving a suspicious object unattended in a crowded space. Instead of waiting for a human to notice it on a camera feed, the AI flags it instantly, sends an alert to security, and triggers a predefined safety protocol—all in real time.
Can AI replace police? No, AI cannot replace police. It’s a tool that supports law enforcement by providing faster analysis, better surveillance, and smarter decision-making. AI can handle repetitive monitoring tasks and assist with data-heavy work, but it lacks human judgment, empathy, and legal authority—making it a supplement, not a substitute.
What are the risks of using AI in public safety? Key risks include privacy concerns, bias in AI algorithms, and the potential misuse of data. If not properly regulated, AI systems can lead to unfair targeting or errors in identification. That’s why transparency, oversight, and ethical use are essential when applying AI in public safety.
