AI and edge computing<\/p>\n
A key concept for AI edge computing is placing the \u201cbrains\u201d (AI running on computers and devices) close to the \u201ceyes\u201d (cameras). Adding \u201cears\u201d (audio sensors) means that AI trained to recognize patterns involved in security incidents can perform that pattern matching at a speed and scale that humans can\u2019t \u2014 starting instantly on-site. The pattern-matching work can be distributed across cameras, video servers, purpose-built local appliances, and powerful cloud-computing platforms.<\/p>\n
This article examines different types of AI-enabled security solutions, focusing on the major and minor roles that AI edge computing plays in each. It does not attempt to describe in detail the features or functions of each solution. Instead, the goal is to explain each solution\u2019s distinct approach and how it incorporates or interacts with AI edge computing. An exception is the Alcatraz section, which gives added attention to privacy regulations and the handling of personally identifiable information (PII) \u2014 topics that remain poorly understood within much of the physical security profession and the industry that serves it. It is also worth noting that all the companies featured have patents pending or awarded related to their AI-enabled devices and platforms.<\/p>\n
Surveillance and monitoring historical shortcomings<\/p>\n
One of the core challenges with earlier generations of in-house and third-party alarm monitoring has been the prevalence of two major weaknesses: false positives (alerts that are not security incidents) and false negatives (no alerts for actual security incidents). The historically high rate of false positives is the root cause of the common operator fatigue in surveillance and monitoring operation centers.<\/p>\n
Smarter and faster security response<\/p>\n
Most of the solutions below focus on massively reducing false positives and achieving virtually no missed threats. They support diverse monitoring and response approaches, covering today\u2019s varied surveillance needs. Large organizations may apply multiple solutions at some sites to address all security risks. \u00a0<\/p>\n
All the solutions mentioned below can be applied to existing security system deployments. The use cases explored include:<\/p>\n
Large-scale third-party of central alarm monitoring operations
\nAI edge readers for multi-sensor access control authentication\u00a0
\nEnterprise-grade full-site real-time situational awareness
\nVideo intelligence AI for scenario-specific needs
\nSmall and medium-sized business protection during regular and off-hours<\/p>\n
Actuate: Going beyond object classification for extreme accuracy and response<\/p>\n
Central station alarm monitoring, serving small businesses and homes for over 150 years, supports over 13,000 U.S. stations with intrusion detection, fire monitoring, video surveillance, access control, and environmental monitoring. Historical shortcomings push remote monitoring toward proactive solutions.<\/p>\n
Actuate\u2019s cloud-based AI video analytics transforms monitoring centers into high-tech command centers and remote guarding into AI-augmented services. It detects intruders, weapons (99% accuracy), fires (earlier than sensors), and critical crowd formations, integrating with most cameras, network video recorders\/video management systems (NVR\/VMS), and monitoring platforms.<\/p>\n
Measurably better false alarm reduction<\/p>\n
Typical object classification analytics (detecting motion or people\/vehicles) prioritize avoiding missed threats, causing high false positives. Actuate\u2019s AI models and scenario-focused AI training excel in context-aware processing, reducing false positives by 95%+ while maintaining virtually no missed threats, backed by a $10,000 reimbursement policy for missed detections.<\/p>\n
This reliability allows staff to manage more sites with less stress, with one center reporting a 57% per-site alert reduction (over 400,000 fewer monthly).<\/p>\n
Uniquely for a monitoring platform, Actuate accommodates both live video streaming from cameras and (NVR\/VMS) setups, as well as workflows where edge AI in cameras or recorders sends images via email \u2014 using the simple mail transfer protocol (SMTP) \u2014 based on scene or object motion detection. Supporting both streaming and edge AI email alerts is crucial because some sites cannot stream video due to bandwidth limitations, and some have workflows built around email alerts. Actuate\u2019s AI models apply the same stringent processing to video images, clips, and continuous streams, achieving uniform accuracy across all monitoring station customers.<\/p>\n
Actuate doesn\u2019t depend on AI edge computing. However, edge AI in cameras and NVR\/VMS systems does in fact act as an initial filter for alerts sent to Actuate. Thus, AI-enabled analytics in such cameras and NVR\/VMS systems be tuned for zero false negatives\u2014since Actuate does the work of assuring the lowest possible false positive rates for monitoring center processing.<\/p>\n
Alcatraz: Privacy-first edge AI for frictionless access control<\/p>\n
Alcatraz leverages advanced edge computing through its Rock product line, offering frictionless facial authentication for secure facility access. Here, \u201cfrictionless\u201d means users can simply approach an entry \u2014 without stopping to present a badge, enter a PIN, use a phone or provide a fingerprint or iris scan. Rock and Rock X devices incorporate AI-powered 3D sensing and mathematical facial modeling, authenticating users passively and seamlessly, so no physical interaction is necessary. Additionally, Rock devices alert on tailgating events \u2014 which is a valuable real-time capability that is unusual for a reader-type device.<\/p>\n
Core access control functions<\/p>\n
Before exploring Alcatraz\u2019s technology, it\u2019s important to understand the two foundational steps in physical access control and how they relate to biometric authentication and privacy regulations:<\/p>\n
Authentication: Verifies the identity of the user.
\nAuthorization: Determines what the authenticated user is permitted to do.<\/p>\n
Step 2, Authorization, is performed by the access controller connected to whatever authentication devices or systems are being used.<\/p>\n
There are many methods to perform step 1, Authentication, for user identification. Examples include:<\/p>\n
Keypad PIN entry
\nCard or fob electronic scan
\nFacial scan
\nFingerprint, palm, iris, or retina scan
\nSensor-based pedestrian gait scan
\nVoice analysis<\/p>\n
During the access control enrollment process, the images, scans, or recordings generated by biometric methods are stored in a system database for future use in user identification. In the U.S., definitions from the Department of Defense and the National Institute of Standards and Technology (NIST) classify the scan data collected by these technologies as personally identifiable information (PII).<\/p>\n
This classification applies because biometric data \u2014 whether stored in the access control system or a dedicated biometric system \u2014 is part of the user\u2019s access control record, which uniquely identifies the individual. Additionally, biometric scan results (such as facial images or voice recordings) can be viewed or played back to visually or audibly confirm a person\u2019s identity.<\/p>\n
How the Alcatraz technology is privacy compliant<\/p>\n
Alcatraz doesn\u2019t store facial images and explains that their mathematical facial template is not humanly identifiable PII under strict privacy definitions, on the basis that:<\/p>\n
It cannot, by itself, reveal or be used to reconstruct the facial likeness of an individual.
\nIt is only usable within the specific context of their access control product, in conjunction with enrolled badge IDs, and is never used for broad surveillance or open identification.
\nThe template is not usable out of context \u2014 an external party with access to the encrypted mathematical model alone would be unable to determine who it represents.
\nAdditionally, Alcatraz never shares or transfers any PII with other systems. This means Alcatraz never has access to information like names, birthdates, and similar data\u2014and conversely, other systems (such as the access control system) never receive Alcatraz Profiles (i.e., templates). All PII is segregated between Alcatraz and other systems, and is, of course, encrypted both at rest (AES-256) and in transit (TLS 1.2).<\/p>\n
Thus, Alcatraz\u2019s solution \u2014 including the Rock and Rock X \u2014 has been approved for use in several U.S. jurisdictions where\u00a0image-based facial recognition is banned. The key distinctions are:<\/p>\n
Explicit consent and enrollment:\u00a0Users actively enroll by providing their information; no covert or open-ended data collection occurs.
\nPurpose limitation:\u00a0The mathematical model is used strictly for authentication (one-to-one or one-to-few comparison), not for mass identification, surveillance or law enforcement tracking.
\nRegulatory testing:\u00a0Alcatraz\u2019s systems have been independently tested and certified to comply with national and international privacy standards, including BIPA (Illinois), CCPA (California) and GDPR (EU).<\/p>\n
Technically accurate terms would be \u201cfacial image authentication\u201d and \u201cfacial mathematical-model authentication\u201d to distinguish typical facial recognition technology from the Alcatraz technology. However, without knowing the technical details (above) about the Alcatraz solution, those terms are meaningless on their own. Thus, it\u2019s easier to do what Alcatraz does, and call their own approach \u201cfacial authentication\u201d and the rest \u201cfacial recognition\u201d because the second term is already in popular use.<\/p>\n
All authentication and AI-based analytics occur locally on the Rock device, eliminating the need to transmit any facial images to the Alcatraz cloud. This is a pure application of cloud-managed AI edge computing. The Rock product line integrates with existing access control systems via standard protocols (Wiegand, OSDP), enabling organizations to add facial authentication strategically without costly equipment replacements or additions. The Alcatraz cloud platform adds further value through secure remote user enrollment, automated firmware updates, and tools that enhance privacy, efficiency and compliance.<\/p>\n
Ambient.ai: Enterprise caliber real-time risk identification<\/p>\n
The Ambient.ai cloud platform is engineered for early threat detection on premises and to provide actionable alerts to an on-site security team. It is purpose-built for\u00a0large, high-security and compliance-intensive environments. Its core approach is to watch all cameras all the time, applying a growing library of more than 150 threat signatures \u2014 part of their patented and continually evolving intelligence model, not just a static ruleset. <\/p>\n
This adaptive approach allows Ambient to (a) achieve zero false negatives, (b)\u00a0eliminate 90% or more of false positives, and (c) alert on actual risk situations at the earliest point where recognition is possible. The platform is designed for full enterprise integration \u2014 enabling seamless coordination with GSOC workflows, access control systems, alarm infrastructure and incident response protocols. <\/p>\n
Ambient.ai delivers contextualized real-time alerts that describe the activity or situation, enabling law enforcement and internal responders to respond with high situational awareness, supported by incident evidence and access to relevant live video feeds. Ambient\u2019s architecture also supports compliance with data governance, privacy regulations and audit requirements common in sectors like healthcare, finance and critical infrastructure. <\/p>\n
To do this cost-effectively and in a timely manner, Ambient uses a high-performance on-premises server (i.e., AI edge computing) to apply its threat signatures to video streams. When a match occurs, compact information about what was detected and when is sent to the cloud platform. The cloud then handles additional AI processing, analytics, and event correlation \u2014 linking related events from different cameras and sources to reveal patterns that may not be obvious in isolation. <\/p>\n
In a physical security system, an \u201cevent\u201d could be a door held open too long, tailgating, unusual activity detected by a camera, a badge scan where the presenting individual doesn\u2019t enter the protected area but someone else does, or an intrusion alarm. Event correlation links these based on factors like time and location to determine whether they\u2019re part of the same incident or threat \u2014 or part of a pattern of security violations. Event correlation over time is a sophisticated capability \u2014 and one of many unique functions that define Ambient\u2019s advanced video intelligence platform.<\/p>\n
The cloud platform tightly integrates with access control, alarm systems, GSOC workflows and automation frameworks to enable coordinated real-time response \u2014 such as automatically locking down areas, activating lighting or initiating two-way audio communication with trespassers \u2014 quickly placing now-situationally-aware human responders in the best position.<\/p>\n
Ambient\u2019s use of edge computing enables large-scale, high-performance AI processing for deployments with high camera counts and activity levels, where timely, well-informed response is critical.<\/p>\n","protected":false},"excerpt":{"rendered":"AI and edge computing A key concept for AI edge computing is placing the \u201cbrains\u201d (AI running on…\n","protected":false},"author":2,"featured_media":33355,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[21],"tags":[49,48,285,61],"class_list":{"0":"post-33354","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-computing","8":"tag-ca","9":"tag-canada","10":"tag-computing","11":"tag-technology"},"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/posts\/33354","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/comments?post=33354"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/posts\/33354\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/media\/33355"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/media?parent=33354"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/categories?post=33354"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/tags?post=33354"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}