THE DIGITAL lifestyle has pervaded every aspect of our lives – from banking and grocery shopping to even paying our taxes and brings us convenience. Every business is leveraging technology to attract and retain customers. Every online interaction – from our Facebook likes and connections on Instagram to our travel habits – contributes to an individual's online presence and digital identity. While companies can use this information customize their offerings to suit the consumer's buying patterns, the trade-off is that the high value of personal information in these digital identities brings a whole set of cybersecurity risks for both consumers and businesses. In an exclusive interview with Tech Wire Asia, Leonard Cheong, Managing Director of AdNovum Singapore discusses the challenges associated with data privacy and cybersecurity, and the role of machine learning (ML) in solving those problems.
DATA centers play a critical role in most organizations. Whether owned or leased, whole or shared, data centers must be run optimally and maintained well if organizations are to rely on them. And although humans operators do a great job, companies are waking up to the reality that maybe artificial intelligence (AI) is actually better suited for the role of managing and running data centers. With just a little support from maintenance staff, infrastructure experts are coming to the conclusion that AI can actually run a tighter ship -- delivering consistency, optimal performance, and cost reductions -- all at once. Aside from storing data, data centers generate a tonne of data themselves.
Decades even before the buzz went off, machine learning has proven its ability to decipher information from vast datasets to see hard-to-spot patterns, classify and cluster data, as well as make predictions using algorithms. With its myriad of real-life applications, cybersecurity remains to be one of its top use areas: It gives traditional cybersecurity solutions the edge it needs to catch destructive threats such as ransomware before it gets deployed in a system, which saves organizations' time, money, and reputations. Traditional machine learning largely deals with historical knowledge. It allows computers to make inferences based on datasets that have been previously labeled by humans. In cybersecurity, training a machine learning model to learn what malicious files and programs look like can help in the discovery of new, emerging, or unclassified threats via correlation.
It uses algorithms to examine large volumes of information or training data to discover unique patterns. This system analyzes these patterns, groups them accordingly, and makes predictions. With traditional machine learning, the computer learns how to decipher information as it has been labeled by humans -- hence, machine learning is a program that learns from a model of human-labeled datasets.