Posted July 27, 2014 Smart Data Collective
If there’s one thing that keeps business leaders awake at night, it’s worries over data security. Nowadays, every company no matter the size uses technology in their operations, whether its using cloud systems for emails, massive server rooms for handling online transactions, or simply allowing employees to access company information on their smartphones. One misstep could end up leading to data loss or even data theft, which could end up costing the company some big money. Even mega-corporations like Target aren’t immune to this unfortunate trend. Businesses are looking for ways to make their information more secure, so to do that, many security systems are turning to big data, or more specifically to machine learning as a way to prevent and combat threats.
When you get right down to it, computer security is all about being able to analyze the data. A company’s security is largely dependent on the amount of data analysis they’re capable of, along with the quality of that data. A company that can collect a lot of data at once but doesn’t have the means to analyze it properly for threats won’t get very far. The same goes for a business with excellent analytic tools but without the resources to gather and store that information. These facts are very important because without a lot of data, machine learning simply can’t be as effective.
For those who aren’t familiar with machine learning, it essentially means a system that is capable of learning from data. The system is given a task, and from that algorithm can constantly get better, performing the task more efficiently and perhaps even finding new ways to do it. The more data a machine learning system has to work with, the better it will be at its assigned duties. In the case of cyber security, a machine learning system is able to sort through vast sets of big data in order to identify certain complex signals that it has deemed to be particularly damaging or a threat.