Artificial intelligence (AI) has extensive applications. One of the most impactful of these potential use cases is its ability to transform security solutions.
AI-powered surveillance systems come in many forms. Some use machine vision to identify people and objects in live video feeds from security cameras. Others combine inputs from multiple sensors to sense break-ins or detect suspicious activity. Regardless of the specifics, this field has several promising advantages over conventional approaches to building security.
Real-Time Alerts
One of AI’s greatest strengths is its real-time functionality. A machine vision system can recognize people in a restricted area or detect unusual behavior as soon as it occurs and alert relevant employees accordingly. That way, security teams can respond to the issue as quickly as possible.
In addition to enabling more effective responses, this efficiency is key in responding to labor shortages. The Bureau of Labor Statistics predicts there will be 162,000 security guard job openings annually over the next 10 years as many current workers leave the industry. Consequently, buildings must do more with fewer professionals, and automated footage monitoring with real-time alerts lets them do so.
AI can also automatically save videos or audio recordings of interest when detecting events. That way, teams don’t need to spend lengthy amounts of time going through data to follow up on an incident.
Greater Accuracy
AI surveillance technology also makes building security systems more reliable. Older alarm solutions like motion sensors are prone to false positives. They can’t distinguish between normal and suspicious movement, leading to alert fatigue and unnecessarily high workloads for security teams. AI is more robust.
Today’s machine vision systems minimize resource waste by ignoring benign movement, as they can differentiate between a person and other objects. Some models can even offer confidence scores, telling personnel the estimated accuracy of their alerts, not just what they are. Such insight makes it easier to know which events to prioritize and what may be a false alarm.
Similarly, AI can go deeper than simply identifying a person or sensing movement. Some can pinpoint the outline of weapons under clothing or recognize aggressive action. This detail makes building security automation more reliable in the absence of a larger human workforce.
Predictive Insights
Predictive analytics is another powerful use case for AI in building security. By predicting likely future events, machine learning makes it easier to allocate resources effectively.
Many security-affecting trends, such as foot traffic spikes, are predictable when enough attention is paid to historical data. AI excels at identifying these factors where humans may miss them, letting it predict the future with remarkable accuracy. Security teams can use these insights to ensure they maintain appropriate staffing or pay extra attention when the need arises.
Early tests show machine learning models can predict security events with 95% accuracy with fewer false positives than conventional methods. They could also recognize changing situations and alert stakeholders in just 250 milliseconds on average.
Concerns Over AI-Powered Surveillance
AI’s benefits for building security are impressive, but this technology has some notable downsides, too. Property managers should be aware of the concerns around this technology before implementing it.
Some AI surveillance applications — most notably, facial recognition — raise privacy and ethics questions. Some states limit the use of biometric data, including face geometry, requiring companies to get informed consent first and follow extensive cybersecurity measures. Even without these laws, AI’s tendency to amplify human biases may lead to errors and unfair treatment of people of color.
Over-reliance on AI can also create problems. As accurate as machine learning is, it’s imperfect, but its general precision can lead people to trust it without question. Consequently, security teams may become complacent and never verify AI results, potentially leading to missed incidents or unnoticed technical errors.
AI also requires a considerable amount of data to be reliable. Collecting enough information to train AI systems could introduce privacy concerns over the people this data reflects. Similarly, if an AI system stores enough, a data breach could expose many people’s sensitive information.
Using AI Security Monitoring Safely and Effectively
In light of such downsides, building managers should approach AI surveillance cautiously. It can be a valuable tool, but only if users follow a few best practices.
Security professionals should review local laws before introducing any AI surveillance software. In general, it’s safest from both a legal and ethical standpoint to only collect what data is necessary for safe operation. Not all CCTV cameras need to recognize faces, audio recordings may be superfluous and some facilities don’t need to worry about weapon detection. Leaving out any unneeded features will ensure AI’s data collection doesn’t breach privacy beyond reason.
Similarly, teams can minimize privacy risks in AI training by using synthetic data. Although this information does not reflect real-world people, machine vision models trained on it are even more accurate than conventional alternatives, making it an ideal training resource.
Education and awareness are likewise crucial. Security professionals should monitor for signs of bias in their AI solutions and always verify AI insights with a human expert before acting on them. All employees should know how and why the technology can falter to avoid over-reliance on it.
AI Can Revolutionize Building Security but Requires Care
AI’s potential in building surveillance is vast, but it’s not without its risks. Property owners and managers should recognize both the advantages and disadvantages of the technology before investing in it. Thorough research and careful implementation will help AI-powered security reach its full potential.
