How to secure data

Gaurav Agarwal, Managing director, India and Saarc, Symantec
Machine learning today has come to the fore in the most dramatic way possible. It was not long ago that a machine, capable of learning and adapting on its own, would be restricted to the realm of science fiction. It doesn’t take much searching to a find a film that depicts computers overthrowing humans or conquering the world. Today, we are witnessing the widespread use of artificial intelligence (AI), but is it as mysterious as shown in movies or are we simply unable to shed a preconceived image of technology?


Take one of the hottest topics in the ever-growing Internet of Things (IoT) revolution: the rise of connected wearables, particularly in India. According to Nasscom, the IoT market in India is expected to account for roughly five per cent of the global market. This growth can be attributed in part to the digital push by the government, enabled by technologies such as IoT. It has paved the way for ubiquitous, always-connected applications, which are making waves across industries such as health care, manufacturing, retail, transport, logistics and utilities.


If you look at it from a consumer standpoint, the Indian wearables market clocked 2.5 million units in 2016, and within the first quarter of 2017, 612,000 wearable devices were shipped to the country, according to IDC. These gadgets have sensors that track various parameters such as location, temperature, ambient lighting, speed, and number of steps or for that matter, heartbeat. Used properly and selectively, they promise many benefits; however, they also expose data (both personal and critical), making security and privacy a matter of concern. This weak link results in tracking, storage and sharing of private information that others should not have access to.


Cyber security companies around the world deal with massive amounts of malicious code daily, designed to harm consumers as well as the businesses and institutions that serve them. It is not “human” to combat such exponential volume each day. But machines are able to deal with these volumes while also learning from each experience.


Leveraging the anomaly detection process, machines are now able to learn what’s “normal” in a given wearable device simply by monitoring previous behaviour. It can identify unusual behaviour that differs significantly from what has been observed before or from what is expected. Therefore, when an anomaly occurs, perhaps due to malicious action, a device is able to detect the change and bring itself to a safe halt. At the moment, much of the technology that detects changes in the system remains unaware of the advanced threats. This is changing today with intelligent machines and monitoring systems.


As we embrace digitisation, the way we protect ourselves must also evolve and there is a critical need to stay proactive against threats, instead of reacting to them. With the emergence of AI, we may just be able to stay one step ahead of cyber criminals.


Eventually, we will need to be able to build intelligence security systems that can not only learn faster than threats can present themselves but also be predictive of new attacks. It is forseeable that a cyber-security AI could observe all the outputs from machine learning models, looking at threats, anomalies and even current affairs news, and detect that attack is about to happen. This would be an amazing force multiplier for our sophisticated cyber security centres, making analysts even more productive.

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