Big Data: How to Apply it to Physical Security Programs

Big Data: How to Apply it to Physical Security Programs

April 12, 2018, 1:15 PM - 2:15 PM

Sands 304 Level 1


Gaining business insight into physical access security systems has traditionally been an afterthought. Most operational systems today have silos of valuable identity, authorization, authentication, alarm and other transactional and log data, as well as metadata, that are never exploited, much less linked with one another. These systems are processing transactions for employees, contractors, visitors, and customers (based on the use cases or missions supported) that can have significant positive impact to the bottom line, or negative exposure and liability, for an organization.

There is a sharp increase in the demand (and often times requirement) for insight into all systems that can affect business operations and continuity, policy and governance, security and risk posture, audit results, etc. Some of these requirements require the delivery and analysis of data in real-time, including the intersection of data to deliver a holistic view of the enterprise and to create feedback loops into the business operations including thresholds, alerts and user experience.

Enterprises are also implementing new database, big data management and analytics platforms, and technologies like NoSQL, Data Lakes, and Cloud GPUs that all departments in the enterprise are expected to take advantage of. Emerging technology such as mobile ID, machine learning and artificial intelligence, building automation, IoT devices and biometrics are also being adopted at increasing rates.

Therefore, the opportunity is now to fold-in physical security data into enterprise analytics and business insight platforms, and to leverage new technologies, to exploit big data to deliver significant improvement in normal and emergency operations, operator and user training and support, user experiences and behavior, policy compliance, failure detection and recovery, and so much more.

Key benefits can be well-established by analyzing and exploiting real-world operational data from a Fortune 1000 company, including data from a physical access control system, door readers, unified access mobile ID system, connected IoT devices, and physical access control and logical access control gateways. R language and machine learning reveal additional insight and establish dynamic thresholds to optimize operations, improve facility design, personalize user experience, and achieve an identity governance and access management continuous compliance model across the enterprise.

Learning Objectives:
1. Be able to describe the volume, variety, velocity and complexity of physical access data that must be managed and processed in a typical large enterprise.
2. Learn how to sort through key data to discover relationships and develop operational thresholds that drive alerts and notifications, as well as user experience and behavior.
3. Learn how digitizing the enterprise leveraging emerging technology such as mobile ID, machine learning and artificial intelligence, IoT devices and biometrics can make big data smarter and help the organization achieve continuous compliance.


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    Connected Security Technology

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