Challenges of Digitalization

With a roaming digitalization, the growth of educated data security staff does not correspond to the actual needs that arise. How can you prevent or minimize data scams now and in the future if you do not have enough computer security specialists dealing with the escalating issues that the digital wave represents?

With daily reports of new data breaches that affect companies and organizations, it is undoubted to face major challenges to comply with the new legal requirements for PII (Personally Identifiable Information) and Sensitive Personal Information (SPI).

Companies and organizations affected by data breaches can now receive major financial penalties, and their reputations with customers and suppliers can be negatively affected. With the new data layers in place, it is now possible to predict, prevent, and stop data extrusion quickly as well as have a solution that releases key personnel to do their remediation work.

Below you will find a brief excerpt from a report released by the consulting company Deloitte in 2017.

In an attempt to challenge the more traditional working methods, private and public organizations should rethink their approach to internal resources and instead consider using cognitive technology to facilitate data security awareness in less time. Such an approach can enable a safer computer environment by taking targeted and proactive measures to prevent incidents before happening.

At a leading investment company, it was noted that their own data security analysts spent 30-45 minutes reviewing checklists with security warnings. In addition, because the work was monotonous, analysts missed important steps, resulting in poorer and insufficient security. By automating processes and surveys, the same work was performed in 40 seconds. Also, the analysts were released to focus on the remediation work. What was the end result? The productivity of the analysts was tripled.

The report below describes a growing data security issue and how cognitive technology can enhance security by allowing artificial intelligence to work in a network to prevent and stop data extrusion.

What does cognitive technology in computing and analysis mean?

Cognitive computing refers to “systems that learn to scale, resonate for one purpose, and interact with people naturally.” It includes technologies such as artificial intelligence, text and signal processing for speech, automation, robotics, and machine learning. Its use can be categorized into three areas:  product programs to improve customer benefit, process programs to improve an organization’s workflow and business, and insights that can help provide informed decisions.

An estimate shows that medium-sized networks with 20,000 devices (laptops, smartphones, and servers) will transmit more than five gigabytes of data per second and 50 terabytes of data over a 24-hour period.

Using supercomputers and artificial intelligence systems to analyze such large data streams can, according to the report, help detect high-end real-time threats, identify the most likely types of network attacks, and reveal deviating patterns on the network


It has been a little over a year since the Deloitte report was released. In early September 2018, Jazz Networks launched its active machine learning platform and real-time user-driven analysis with CPU power not exceeding two percent on the device. With this technique, we have taken a big step forward for cognitive computing. Having this technology means that you can leave parts of the monotonous work with checklists of security alerts to a machine learning system. We can ensure that technology helps security personnel monitor deviant events inside and outside the network.

Jazz Network’s cognitive computing technology is an outstandingly well-functioning solution and puts the finger on the growing problem of demand and access to talented data analysts for internal data security. Let intelligent software actively illuminate the network. Allow machine learning to create user-based workflows within and outside the network. Then key people within an organization will be relieved to perform the most important job in a data incident – remediation work! By integrating active machine learning for data security reporting, companies and organizations will better manage the digitalization wave now and in the future!