Using the Cloud and Deep Learning for Proactive Campus Security

Reacting to an unforeseen security issue used to be the only option, but combining modern technologies can change that.

Using the Cloud and Deep Learning for Proactive Campus Security

While it may have once seemed like an uncertain, overwhelming concept, the cloud has officially made its impact on the security industry and isn’t going anywhere any time soon. Propelled by the interconnectivity and increased communication of the Internet of Things (IoT), today’s organizations and leaders seek to take advantage of the many opportunities available in the cloud.

The cloud has gained significant traction, but it can be argued that it is misunderstood by the general public, end users and even integrators as far as functionality goes. Almost every company today has some form of a cloud solution that they rely on every day, but there still are barriers to adoption in certain industries, such as the physical security and investigations market.

Therefore, it is important to understand that different types of organizations will have different ways they approach using the cloud, some of which require creative thinking.

For example, organizations that are waiting for advancements in bandwidth and storage costs are using the cloud, but on a limited scope. Rather than a complete on-premise or cloud solution, many organizations choose to use a hybrid of sorts, which allows them to use the cloud for what it does well: processing heavy functions like video analytics. But organizations that do their homework or seek the advice of a well-informed integrator have come to realize that the cloud is a worthy investment.

When the cloud, analytics and deep learning intersect, campuses are equipped with an advanced solution that can provide proactive, as opposed to reactive, security measures.

Fewer IT Resources

In simple terms, “the cloud” refers to the storing and accessing of data over the Internet as opposed to on-premise hard drives. These platforms allow campuses to take advantage of the latest technology with fewer IT resources and upfront costs associated with traditional, on-premise software deployments. These solutions are also easier to install and configure as well as simpler to manage, support and use, resulting in a lower overall total cost of ownership and an improved experience.

The potential for the cloud is nearly limitless; as higher upstream bandwidth continues to expand and the cost of cloud computing and storage continues to decrease, organizations are presented with new levels of flexibility. Within this structure, they can choose whichever architecture best suits their needs, whether it be a fully on-premise solution, fully cloud-enabled solution or a hybrid structure.

To address the sense of fear surrounding the cybersecurity of the cloud, organizations are taking a collaborative approach to ensure comprehensive protection. Dialogue between IT and physical security teams is becoming mandatory, as more conversations are needed to proactively identify vulnerabilities and protect physical assets, networks and valuable data.

For example, in the banking and financial market, despite being primary targets for cyber threats and hackers, mansy departments and divisions have developed promising practices for cybersecurity and are thriving on cloud-based solutions. Improving and simplifying customer engagement is a critical component of a financial institution’s success, and customers are demanding on-the-go financial capabilities. The cloud allows these institutions to provide virtual and instantaneous services, such as mobile banking, to address this need.

Additionally, the ability for multiple branch locations to access all of a financial organization’s data in one unified platform significantly enhances operations and streamlines processes. The cloud is both resilient and scalable, allowing banks to use only the space needed, and to move to the most current and updated software versions automatically. When it comes to addressing and identifying security threats, leveraging the cloud can help financial institutions to share real-time intelligence, mitigate risk and ensure efficient response.

Analytics and Deep Learning

While the cloud has been deemed the real game-changer, combining it with analytic capabilities is how organizations can move from a reactive to a proactive approach to security. The power of analytics has come a long way, and its use in various types of organizations is more prominent now than ever before. Deep learning is being embraced across the board as a new method of obtaining intelligence in a number of business operations, from security to marketing to entertainment, and the banking environment is no exception.

Deep learning is defined as a machine learning technique that learns features and tasks directly from data. Its significant value stems from the need for executives and administrators to be able to quickly make sense of vast amounts of information for more effective decisions and immediate action. Machine learning has set the stage for deep learning to take over.

In surveillance, for example, networked deep learning systems can now recognize the same faces from photos and video feeds from different times and locations. In the automotive industry, deep learning allows driverless cars to be based on visual pattern recognition rather than sensor information.

Use cases for these solutions are only just beginning to be imagined. On the security side, integrated analytic solutions leverage artificial intelligence (AI) to enhance a bank’s monitoring, reporting and investigative capabilities by automatically pinpointing potential breaches and events. Deep learning analytics can be used to track an individual by utilizing a series of various camera views.

Instead of spending time manually locating a suspect’s whereabouts, that crucial piece of information can be achieved instantly, ultimately improving accuracy and reducing operating costs. When combined with the cloud, information sharing becomes instantaneous, in turn reducing investigation times.

Deep learning applications can also be used to amplify a financial organization’s customer safety and engagement. Analytics can help mitigate fraud by determining trends in data or abnormal activity, facilitating a more proactive approach. And customer service can be improved, as online chatbots are becoming smarter and can be tailored to a specific customer profile.

Temper Expectations

While the cloud does have many benefits, it is important for security professionals to be realistic in their expectations and recognize that cloud capabilities are still in their infancy. This is where processing-heavy functions like video analytics come into play. When deep learning and the cloud are used in conjunction together, end users are provided with a proactive solution that increases efficiencies and reduces investigation time.

At an ATM, branch, cash vault or even an online banking website, for example, the minute a security incident begins, officials have no time to waste. One small breach can rapidly lead to a larger disruption, ultimately affecting the institution’s brand and customer engagement. Instead of spending valuable time attempting to mitigate the incident as it could be spreading, the trend toward a proactive approach allows security teams to identify an event before it occurs or assess and respond quicker to one that has already begun.

The key to being proactive is knowledge: the more pieces of data gathered, the greater the likelihood of recognizing a vulnerability. Trends such as Big Data analysis, deep learning and the Internet of Things (IoT) have greatly elevated the ability to look at security in a proactive manner. But the ability to be proactive is only as strong as the infrastructure a company has in place.

For example, an airport might have stringent security measures in place to conduct people detection, which is very effective with deep learning analytics. Still, it requires a fair amount of horsepower to analyze the video in a way that can provide actionable intelligence to the end user.

While a large location like an airport may have the infrastructure to handle large amounts of data, other organizations must be strategic in how and where they use analytics. This is where the cloud comes into play: rather than sending all video to the cloud for analysis, users can opt to send a subset of video at a local location to the cloud, where relevant stakeholders can gain access and make appropriate decisions.

The Bottom Line

Reacting after the fact to an unforeseen security event used to be the only option. But now, the combination of different technologies can change that.

The cloud is here to help achieve this goal, and its capabilities can be capitalized on by organizations that possess the creativity and know-how to master and embrace this technology. Pairing the cloud with the advanced analytics of deep learning is one way to ensure security breaches do not go undetected and help organizations drive productivity and future-proof security operations.

Matt Tengwall is General Manager, Fraud and Security Solutions, at Verint. This article originally was published in CS sister publication Security Sales & Integration and has been edited.

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