Machine Learning &
The advancement of technology and expansion of businesses might mean further development for the world as it is; however, it does mean that higher volumes of data and information has to be processed. As a result, data protection now sits on the top of the to-do list for almost every organization.
Notably, the high influx of technological advancement has made it such that handling volumes of data, taking note of their sensitivity and value while keeping them away from prying hands and eyes is getting increasingly difficult to do with the majority of data security solutions. This is why data classification has found an important place in organizations across the globe.
These days, with data classification at the base of data processing and security, evaluation and categorization of sensitive data into respective formats to be stored in has been made easier. However, thorough classification in this field is preceded by the identification of the necessary tools that would work best ensuring the optimal data protection solutions.
Given its reliance on user input, data classification has not only been viewed as a rather tedious task but also one that with time, bred computational errors and inaccuracy. This perhaps was a reason for the recent explosion of interest in machine learning since automation seemed to be a mechanism perceived to work well in coping with the large volumes of data to be handled over time.
In Walks Machine Learning
One of the arguments on the forefront of data protection and cyber security remains that cope with the high rate of data handled and gain optimal leverage against competition, it is critical that enterprises and organizations adopt automated approaches.
True to form, most data obtained by these organizations are streams of data like social media feeds, emails and business documents and in most cases, they are unstructured in nature. Thus, identifying and classifying their sensitivity and value is one major issue faced by data classification solutions today.
You see, data classification can only be possible if there is apt data discovery. However, statistics has led us to understand that with many classification solutions out there is a rather significant measure of inaccurate detection rates.
This, though, has been, in one way, linked to the large volumes compiled as mentioned above. A school of thought believes that their unstructured nature makes them quite difficult to identify and compute.
This is where automation comes in. True to form, there has been a revival of interest in Artificial Intelligence which had, in turn, bred a rise in popularity of its subset, machine learning. Today, apart from the constant growing volumes of data, the need for cheap computational analysis and storage has one of the most important factors advancing the power of this technology.
Introducing machine learning to data classification in its variation means that classification models are built with algorithms in place and human efforts or intervention is relegated to the backseat. In fact, experts believe that with machine learning in place, data discovery and cyber security will become more efficiently handled.
However, with Murphy’s Law in place, whatever can go wrong will eventually go wrong. The main issue with this is that while human efforts are relegated to the backseat probably to eliminate computational inaccuracies, there is still an element of human input in the application of machine learning.
All that Glitters
In general, machine learning opens the way to more sophisticated way of handling data by increasing the accuracy of computations and harnessing the power of intuitive algorithms in structuring unstructured data.
Machine learning in most cases, takes up the contextual analysis of the data to be classified by the using information on the user, the data itself and other relevant information. In form, this is meant to be a fast and efficient way in handling sensitive data.
Reality, however, says otherwise. One of the major challenges faced by this area is that the machine learning adopted by various organizations is in its true form still in a developing stage. As history has shown us, technology is a constantly evolving aspect of the world.
Thus, there is still a wide berth between machine learning in the future and machine learning now just as it was in time past. As a result, the room for error in analysis is quite significant. Another point that pushes machine learning in data classification towards inaccuracy is the human factor.
Regardless of however perfectly structured the algorithms employed are, there is still an input form the human faction. As mentioned above, machine learning as a computational analyst takes up information from the user for its analysis.
With this, of course, comes a possibility of inaccuracy on the part of the user and this in the end translates to a rate of inaccuracy in the computational power of machine learning. Also, machine learning is admittedly not exactly simple to understand.
In this case, there is a requirement for skilled operators which makes its application a tad uneasy. It also extends the possibility of high inaccuracy rates with operators without proper understanding of the basis of the technology.
Machine learning is admittedly something of the future. This technology, however, appears to be in its developmental stages which makes it quite difficult to simply apply directly to data classification.
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