Brivo unveils Anomaly Detection, a revolutionary technology that leverages access data and machine learning to strengthen built-in world security

The patented technology promotes Brivo’s efforts to revolutionize the company’s PropTech through the power of data

BETHESDA, Md., November 17, 2021 / PRNewswire /Brivo – a global leader in cloud-based access control and smart building technologies – today announced the release of Anomaly Detection in their flagship access control solution, Brivo Access. Anomaly Detection is a patented technology that uses advanced analysis with machine learning algorithms to compare vast amounts of user and incident data to identify events that are out of the ordinary or look suspicious, and issues priority alerts for immediate follow-up. With Anomaly Detection, business leaders can gain a nuanced understanding of security vulnerabilities across their facility portfolio and act on early indicators of suspicious user behavior that may otherwise go unnoticed.



“With Anomaly Detection, Brivo incorporates the latest data and machine learning technology in ways never before seen in the field of physical security,” said Steve Van Till, founder and CEO of Brivo. “Together with our newly released Brivo Snapshot capability, Anomaly Detection uses AI to simplify access control by alerting customers to abnormal situations and prioritizing them for further investigation. After training, each customer’s neural network will know more about traffic patterns in their space than the property. “Property managers can stop searching for the needle in the haystack. We’ll identify it and mark it automatically for them.”

Anomaly Detections AI engine learns the unique behavior patterns of each person in each property they use to develop a signature user and spatial profile, which is continuously refined as behavior evolves. This dynamic real-time image of normal activity complements static security protocols, permissions, and schedules. In practice, when someone engages in activity that is a deviation from their previous behavior, Anomaly Detection creates a priority alarm in the Brivo Access Event Tracker that indicates the severity of the aberration. This programmed protocol helps organizations prioritize what to research.

As more companies roll out hybrid work policies for employees, most companies are ready to see a lot of variation in office plans and movements. For human operators, it would take an enormous amount of time to learn these new patterns, especially to analyze beyond the usual behaviors that are technically still within the formal limits of acceptable use. With Anomaly Detection in Brivo Access, security teams can achieve better visibility and understanding, as the underlying technology continuously learns users’ behaviors and patterns as they change over time.

The release of Anomaly Detection continues Brivo’s significant investment in Brivo Adgang and AI over the past year to offer building owners and managers more understandable, actionable insights and save time-consuming legwork. With a comprehensive enterprise-quality user interface, real-time data visualizations, and clear cross-property trends, organizations can secure and manage multiple spaces from one central hub.

Anomaly Detection is now available in the Enterprise Edition of Brivo Access. For more information, visit our Free Access Blog.

About Brivo
Brivo, Inc., created the category of cloud-based access control and smart building technology over 20 years ago and remains a global leader serving commercial real estate, multi-family housing and large distributed businesses. The company’s comprehensive product ecosystem and open API provide companies with powerful digital tools to enhance security automation, enhance employee and tenant experience, and improve the safety of all people and assets in the built environment. Brivo’s building access platform is now the digital foundation for the largest collection of customer facilities in the world, trusted by more than 23 million users, occupying over 300 million square feet in 42 countries. Learn more at



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