Surveillance’s Shift to the Datacenter: How to Leverage the Cloud and AI

March 25, 2019
The growth of the surveillance industry requires an infrastructure that can handle its storage needs

Over the past 10 years, we have seen substantial growth of video and smart sensors in its use in smart buildings and smart cities.  We have also seen the growth of converged security systems that rely on metadata from the edge to define and deter potential threats; connected systems that reside on networks and the cloud with the use of deep learning and machine learning have given our world the promise of greater safety and security.  With this in mind, the push to move from on-premise solutions to cloud-centric infrastructure have put data centers at the eye of the storm. 

“Surveillance unchained” becomes the motto for the new converged smart cities and smart buildings world.  The explosion of IoT and the growth in popularity of video as the preferred tool for detection has pushed those in leadership positions such as the CIO and security directors to rely on cloud infrastructure.  This infrastructure would inevitably be too burdensome to control on site so many are relying on data centers to become their savior.  This does not come without potential risk, however.  As much of the “surveillance risk” is pushed to the data center the hope is that the data centers will take on some of the responsibilities assigned to corporate liability.  This encompasses compliance requirements as well as regulatory restrictions due to data privacy.  The data center is not, however, the “fall guy” and clearly, they are setting the boundaries for this world of surveillance in the cloud environment. 

Although many data centers are becoming compliant to most industries such as credit card processing (PCI DSS) and data and healthcare (HIPAA, HITRUST), they will only go so far in giving carte blanche acceptance of their client’s risk.   Many larger enterprises build their own private cloud and use their own data centers to leverage redundancy and exponential growth by creating on- and off-premise cloud solutions.  It is also clear that many corporations are leaning towards using cloud video and access control that transfer the burden from the consumer to the vendor (video and access control manufacturers).  This latter solution becomes the “unicorn”; if it can be leveraged properly, it becomes the most affordable and truly efficient solution for the end user.  

Fundamentally, the use of video and analytics connected to AI (machine and deep learning) now has a shifted infrastructure that supports the entire domain into a cloud environment.  The interconnectivity between the ability to monitor, maintain and grow surveillance as a service becomes the choice of preference for businesses.  To make this a reality, data centers such as the largest data storage entities in the world are moving to the use of AI and machine learning to help make it possible for surveillance companies to provide the best solutions to its customers.

Leveraging machine learning and AI to implement rapid development and business processes with security and compliance in mind is the hope for the new connected world.   Large companies are harnessing the push towards providing infrastructure to support the surveillance “boom.” According to the claims of Gartner Research Vice President Milind Govekar, who gave a presentation at Gartner’s annual conference for IT infrastructure operations professionals, 2017 in Las Vegas, 80 percent of enterprises will have shut down their traditional data centers by 2025, compared to just 10 percent today. The figures are fitting considering the host of problems faced by traditional data centers. The solution to all these problems lies right in front of us - incorporating intelligence in traditional data centers.

To support this claim, Govekar also predicts that by 2020, more than 30 percent of data centers that fail to implement AI and machine learning will cease to be operationally and economically viable. Across the globe, data science and AI are influencing the design and development of modern data centers. With the surge in the amount of data every day, traditional data centers will eventually get slow and result in an inefficient output. Utilizing AI in ingenious ways, data center operators can drive efficiencies up and costs down. A fitting example of this is the tier-two automated control system implemented at Google to cool its data centers autonomously. The system makes all the cooling-plant tweaks on its own, continuously, in real-time- thus saving up to 30 percent of the plant’s energy annually.

AI has enabled data center operators to add more workloads on the same physical silicon architecture. They can aggregate and analyze data quickly and generate productive outputs, which is specifically beneficial to companies that deal with immense amounts of data like hospitals, genomic systems, airports, and media companies.

Inevitably, the growth of the surveillance industry has led to a need for greater accessibility to infrastructure that can handle its storage requirements.  Data centers that leverage AI and machine learning will be better prepared to provide the service at a lower cost.  The challenge will be securing this data and securing the transaction of this information, which always will rely on ensuring the end user has seamless and secure physical access using proper identity management and secured entry.  This will be always required if the promise of the cloud can be realized in the IT, OT, and physical security arena.

About the Author:

Pierre Bourgeix, MA, MBA is an Enterprise Consultant with Boon Edam Inc. and has spent 20 years in the security industry as a global security consultant and passionate innovator – carving out a career with ADT, HySecurity, Wallace International, Tyco, SecureState, and ESICONVERGENT LLC. He is an expert on video security analytics, integration, physical and cyber security governance and the design of enterprise converged security systems. Bourgeix’s focus is to help enterprise customers define entry solutions that are integrated with their IT, OT and Physical Security plan to mitigate overall company risk, liability and if necessary, regulatory compliance.