People, Data and AI

Each year, through its well recognized industry market research, Gartner lays out the strategic technology trends for the upcoming year. Recently, Gartner published the top 10 strategic technology trends for 2020.

According to Gartner, The trends are structured around the idea of “people-centric smart spaces,” which means taking into consideration how these technologies will affect people (i.e., customers, employees) and the places that they live in (i.e., home, office, car).

In my own view, the majority of these strategic trends center around the technologies that “help people or organizations to effectively ingest and digest vast and explosive amount of data and information intelligently, automatically and reliably to ease the daunting efforts and workloads that these individuals or organizations have to spend on collecting, storing, processing, analyzing and presenting the data. ”

It is not an exaggeration to state that modern society is represented by data, lots of data, lots, and lots of data. Data is everywhere, data is growing at an explosive rate, data is part of our lives, data is also connected, networked and can be accessed physically, virtually, locally or globally. We cannot conduct normal business or even live normal lives without being exposed to, accessing and manipulating all sorts of data anywhere and everywhere, anytime and at all the times. The availability of data brings conveniences to people and societies, serving a good purpose to improve the quality of human lives. It is also the grass root foundations of technological advancements. With the availability and easy access of data, technologies such as artificial intelligence, computer vision, machine learning (which used to be only meaningful in academic research with lab data) now find their killer applications and reach their critical mass in the areas like network security, auto-driving and health care.

Just as the availability and easy access to data bring us these benefits, it also brings with it the potential risks that can even do harm to individuals, organizations, governments or societies. Data integrity, security and privacy need to be maintained in order to sustain and keep normalcy and order for individuals and society, as well as businesses.

In the network and security industry, companies are creating more powerful big data analytical platforms and products that have even larger data storages capacities, data computing and analytical capabilities, either on perimeters or in the cloud. Data is collected from a variety of sources such as hosts or server machines, network equipment, firewalls and all sorts of other security or management devices like IDS, IPS, WAF, Anti-virus, HSM, as well as network, security or management software applications like AD services, SNMP, DHCP, DNS— to name a few. With the advent of 5G and edge computing, billions or more of IoT devices are joining data sources via wired, wireless or mobile networking. Third party integration such as intelligent data also brings billions or even trillions of bytes of data from different external data sources.

The nature and diversity of the data collected also change dramatically, from legacy security device logs to data including user level information and application level content, network traffic logging, AI or ML data sets, intermediate analytical results etc., just to name a few. All these different types of data from different sources are constantly funneling through the data collecting modules of the big data analytical platforms, being preprocessed, stored, analyzed and finally visible to users thanks to the blend of techniques used at the backend powered by AI and automation tools.

Under these circumstances, it is clearly impossible to manually ingest and sort through this data in a systematic and visible manner, let alone to conduct analysis and dig out the gold from the dirt. Fortunately, driven by modern technologies like AI, machine learning and other big data analytical tools and automation techniques, efficient tools and procedures are created to ease this burden and empower researchers, developers and network or security administrators and analysts to conduct powerful and effective data mining and analysis.

To make better use of the data, all big data analytical products or AI algorithms have to ensure these basic data analytical requirements. Visible, visibility can provide users insights into the traces of data, correlate actions in progress, enrich the results of data analysis; Explainable, this is especially important using AI, ML or deep learning based big data analytical products. Users need to have meaningful and explainable results presented to them in order to have tangible conclusions; Security, this is critical to ensure data integrity and privacy in terms of data access, storage and analysis. Without this, there will be no trust in the data and thus there will be no internet based business, finances or economies. Automatic, it is evident that without automation, it is impractical to process and analyze data manually. People will be buried in the oceans of data conducting tedious daily jobs inefficiently and ineffectively, companies are making an effort providing automation tools and processes to alleviate the pains. For example, we have seen a lot of security companies providing what’s called security orchestration, automation and response (SOAR) capabilities in their big data analytical products this year.

Gartner’s top strategic technology trends for 2020 also clearly highlights these philosophies surrounding how to make big data accessible to people more easily, intelligently and securely. Among these trends, Hyperautomation, refers to automation using technology to automate tasks that once required humans; Democration, refers to the democratization of technology to provide people with easy access to technical or business expertise without extensive (and costly) training; Transparency and traceability, refers to the efforts to achieve trust in data usages in term of ethics, integrity, openness, accountability, competence and consistency; Human Augmentation, is the use of technology to enhance a person’s cognitive and physical experiences which also indicates the trend to provide easy access to the data available to us; Automated Things, which include drones, robots, ships and appliances, exploit AI to perform tasks usually done by humans; The Distributed Cloud, allows data to be stored anywhere, providing easy access as well as security and high availability of data; AI Security involves technologies such as hyperautomation and autonomous things That offer transformational opportunities in the business world.

Today’s society centers around data and information. Data enhances the quality of human lives; data also brings risks and danger to human lives as well. It is the incumbent duty and responsibility of the technology industries to make relentless efforts in advanced technologies to provide easy access to data and to ensure the security and integrity of the data in order for people to utilize them in more effective and efficient ways.

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