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.
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. Power Your Business with Kanerika’s AI Expertise At Kanerika, we help businesses tap into the full potential of AI to solve real problems, improve operations, and stay ahead of the competition. Our AI solutions are built to drive efficiency, reduce costs, and unlock smarter decision-making across industries.
We also offer advanced capabilities in AI-powered surveillance, enabling real-time behavior detection, cross-camera tracking, 3D scene reconstruction, and facial analytics. These solutions support clients across sectors like public safety, education, retail, and manufacturing—transforming traditional surveillance into proactive, intelligent security.
From process automation and predictive analytics to intelligent data governance , our services are designed to deliver impact—whether you’re streamlining internal workflows or building cutting-edge customer experiences. We also support full-scale data migration , helping businesses modernize their infrastructure without friction.
With Kanerika as your AI partner, you’re not just adopting new tech—you’re building smarter, faster, and more resilient operations that scale with your goals.
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FAQs How is AI used in public safety? AI in public safety enables real-time threat detection, predictive policing, emergency response optimization, and intelligent surveillance systems. Law enforcement agencies deploy machine learning algorithms to analyze crime patterns, while emergency services use AI-powered dispatch systems to reduce response times. Smart city infrastructure leverages computer vision for traffic monitoring and crowd management during large events. Public safety analytics platforms also help identify potential hazards before incidents occur. Kanerika’s AI and data analytics solutions help government agencies build intelligent public safety systems that protect communities effectively—connect with our team to explore implementation strategies.
What are the risks of using AI in public safety? The primary risks of AI in public safety include algorithmic bias, privacy violations, false positives in threat detection, and lack of transparency in automated decision-making. Facial recognition systems have shown higher error rates for certain demographic groups, potentially leading to wrongful detentions. Data security vulnerabilities can expose sensitive citizen information, while over-reliance on AI predictions may reduce human oversight in critical situations. Accountability gaps also emerge when automated systems make consequential errors. Kanerika builds AI solutions with robust governance frameworks and bias mitigation protocols—reach out to discuss responsible AI implementation for your public safety initiatives.
What is AI-powered surveillance? AI-powered surveillance combines video analytics, facial recognition, and machine learning to monitor public spaces and detect suspicious activities automatically. These intelligent monitoring systems analyze live camera feeds to identify weapons, track individuals, recognize license plates, and flag unusual behavior patterns without constant human observation. Modern surveillance AI can process thousands of video streams simultaneously, alerting security personnel only when potential threats are detected. Cities worldwide use these technologies for crime prevention and emergency response coordination. Kanerika delivers enterprise AI solutions with built-in privacy controls and compliance features—let us help you implement responsible surveillance technologies.
Can AI reduce crime rates? AI can contribute to measurable crime reduction when deployed strategically within broader public safety frameworks. Predictive policing algorithms help departments allocate patrol resources to high-risk areas, while AI-driven video analytics enable faster identification of suspects. Cities implementing comprehensive AI crime prevention systems have reported decreases in property crimes and improved case clearance rates. However, effectiveness depends on data quality, proper implementation, and integration with community policing strategies. Technology alone cannot solve systemic issues driving criminal behavior. Kanerika helps law enforcement agencies deploy AI analytics that deliver actionable intelligence—schedule a consultation to explore data-driven crime reduction approaches.
Is AI surveillance legal? AI surveillance legality varies significantly by jurisdiction, with regulations governing facial recognition, data retention, and automated monitoring differing across countries and states. In the United States, no comprehensive federal law exists, though cities like San Francisco have banned government facial recognition use. The European Union’s GDPR and proposed AI Act impose strict requirements on biometric surveillance in public spaces. Organizations must navigate local privacy laws, consent requirements, and civil liberties protections when deploying AI surveillance systems. Compliance frameworks should address data handling, algorithmic transparency, and citizen rights. Kanerika’s data governance expertise ensures your AI implementations meet regulatory requirements—contact us for a compliance assessment.
What is an example of AI in safety? A prominent example of AI in safety is gunshot detection systems deployed across major cities. These acoustic sensors use machine learning to distinguish gunfire from other loud noises, instantly triangulating shooting locations and alerting police within seconds—far faster than 911 calls. Another example includes AI-powered wildfire detection cameras that identify smoke plumes early, enabling rapid firefighter deployment. Industrial facilities use computer vision to detect worker safety violations in real-time, preventing accidents before they occur. These AI safety applications demonstrate technology’s potential to save lives through faster response. Kanerika develops custom AI solutions for safety-critical environments—explore how we can address your specific challenges.
How is AI used in road safety? AI transforms road safety through intelligent traffic management, autonomous vehicle systems, and predictive accident prevention. Computer vision algorithms analyze traffic camera feeds to detect dangerous driving behaviors, wrong-way vehicles, and pedestrians in crosswalks. AI-powered traffic signal optimization reduces congestion and accident-prone situations at intersections. Fleet management systems use machine learning to identify driver fatigue patterns and provide real-time alerts. Smart infrastructure can detect road hazards like ice or debris and warn approaching vehicles automatically. These AI road safety technologies significantly reduce collision rates and traffic fatalities. Kanerika builds intelligent automation solutions for transportation agencies—connect with us to modernize your road safety infrastructure.
Will AI ever replace police? AI will not replace police officers but will increasingly augment their capabilities and transform law enforcement operations. Human judgment remains essential for de-escalation, community engagement, constitutional rights protection, and complex investigative decisions that require empathy and contextual understanding. AI excels at processing vast data volumes, pattern recognition, and automating administrative tasks—freeing officers for community-focused work. Future policing will likely feature AI handling routine analysis while humans make critical decisions and maintain public trust. The goal is human-AI collaboration, not replacement. Kanerika helps public safety organizations implement AI that empowers their workforce—reach out to design your human-centered AI strategy.
