I found the article below more interesting than the others I happened to read primarily for reasons dealing with competitive advantage and the fact that it is discussing a proactive and cheaper solution to IT security. \u00a0As the number of ways an individual can attack a system increases, as should our number ways to defend against those attacks. \u00a0In my opinion, IT security or cyber security seems to always be reactive in nature or “damage control” as other articles point to a speedy reaction time as being key to mitigating a business’ loss. \u00a0Imagine a world were intrusions and attacks can be predicted and avoided as opposed to hardening a system with the hope that an attack or intrusion is unsuccessful. \u00a0From an enterprise risk management perspective having a predictive\u00a0approach to IT security on top of solid detective and compensating controls could be the solution to better mitigating loss to the business. \u00a0What does this mean with regards to competing in the market? \u00a0It means margin; if two companies are competing directly in the e-commerce market place and one company has an automated machine learning approach to IT security, that means it doesn’t have the expense that comes with hiring humans, even if it is one less human. \u00a0One less human means one less employee benefit package and salary, which means decreases expenses and increases margins. \u00a0The long-term viability of the firm that implements a Machine Learning approach to IT security is greatly increased. \u00a0Implementing cheaper more efficient means of doing any business function almost always means more profits and better share performance.<\/p>\n
At my firm we are working on such Machine Learning algorithms, and most of the executives say, “it won’t work…” but that is because they don’t understand the math behind the algorithms or the applications of Machine Learning. \u00a0Pattern recognition and response time to the n’th degree and at levels far beyond that of a human. \u00a0I’ve heard and have been involved in many debates around combining Machine Learning and Cyber Security.<\/p>\n
So I pose a question, should this type of technology be used as a decision support tool within the business or should it be used as a stand-alone IT tool with minimal human interaction? \u00a0To play devil’s advocate, on May 6, 2010 the ‘Flash Crash’ was said to have been caused by a trader spoofing the algorithm. \u00a0Could this happen in this case?<\/p>\n
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