July 17, 2018
There is no denying the fact that we are more connected than ever today and this connectivity only seems to increase by the day. The world today has shrunk within a small handheld mobile device. Hasn’t it? Smarter technology is bringing not only the world but the future closer.
Alongside, this trend has exponentially increased the rate of data generation. Servers are not the only high-volume data-sources anymore. Mobile devices and internet of things (IoT) are churning out a copious amount of information each second. As the number of smartphones and connected devices grows, this inflow of data multiplies too. It should be noted that this data is multiplying with each second and getting more and more massive in size.
With this being said, it should come as no surprise that existence of this enormous amount of ever-evolving data, in a way, rolls out a red carpet for cyber attackers. It sends out an invite to numerous suspicious activities, malware variants and security threats. Interestingly, the information-set we are talking about here is so colossal in size and scattered that human capacity is far from able to process it and hence the timely detection of a threat is nearly impossible that way.
Thankfully, the advent of machine learning companies has proven to be a paradigm shift in this direction. Unlike humans, artificial intelligence (AI) can perform mundane and repetitive activities with a consistent efficiency and can assemble and process large scattered data-sets and extract intelligent insights out of them. Moreover, learning algorithms can update the models according to the changing trends in real-time. Recent years have witnessed these algorithms contributing to analytical systems to mine and process big data intelligently.
Worldwide, organizations are spending millions of dollars each year on their security infrastructure. However, the breaches are occurring despite all the traditional attempts. This is posing a potential threat to the organizations and impacting their abilities to grow and succeed. Machine learning as a service is proving to be a potent solution to security and fraud. Machines are far more efficacious in identifying the subtle patterns of abnormality when compared to humans, especially with increasing information-sets. Based on predetermined guidelines, machine learning models can detect relationships, similarities and polarities between various parameters such as organizations, people, transactions etc. immediately with an accuracy existed never before. Now it is possible to ascertain which traffic is not normal for the network. Machine learning has enabled the IT departments of the organizations to detect breaches and attacks in early stages thus giving them sufficient time to take appropriate cybersecurity measures.
It is obvious that if you know the danger, you are better prepared for it. That is why predictive analytics has been increasingly receiving special liking from all over the business world. Over the years, it has been offering significant dependency to the organizations across the departments including operations, marketing, risk, and security predominantly. Besides bringing efficiencies to the operations, it can also play a major role in gauging customer behavior patterns and identifying anomalies to fortify the security infrastructure. In fact, it has had a major application in cybersecurity which has been one of the top items in the list of strategic priorities of the businesses across the globe.
It analyzes huge volumes of past data to understand the cause-effect patterns and provides deep insights into the sources of threats, their probability, levels of severity and safeguard options to address them. Predictive analytics helps the CIOs and security executives be prepared for probable occurrences of future.
The introduction of machine learning in predictive modeling given it a revolutionary shift. Predictive analytics, in its independent form, can only provide insights into the probable threats to make appropriate preparations. However, this approach has certain limitations and may not always be able to defend against the attacks in real-time. That is where machine learning arrives to enhance the predictive models. When coupled with predictive modeling, it is able to revamp the overall security infrastructure making it stronger than ever before.
Since machines can process volumes and volumes of raw data without getting tired, learning all along the way, it is possible or rather easy for them to spot anomalies in the patterns and predict the probability of threats. Therefore, there can help make prior arrangements to tackle security issues.
Machine learning applications are miles ahead of traditional security frameworks which used to be rule-based systems. So over time, it became easy for the attackers to identify the patterns in the security rules and breach them. Machine learning has an advanced, new-age way to solve that problem today. It is quite nimble and able to adapt to changing network through an automated update in the model. Each iteration in the network makes the systems smarter and more accurate than before.
Since predictive models generally use machine learning algorithms, a predictive analytics software works closely with machine learning. You can train the software over a period of time and customize them accordingly to respond to new values accurately. So it is becoming possible now to match and even exceed the pace of cyberthreat evolution.
Cybersecurity leaders always need foresight and a sharp vision to identify and know everything about the probable threats by their occurrence and intensity. Smart insights and real-time mechanism can provide them with an accurate assessment of the threats quite well in advance. This insight into the future can not only help them in gauging the nature of the threat but also preventing the unfavorable incidents and responding to them in an optimum manner.
Clearly, predictive analytics and machine learning can equip you to correctly mine and analyze an enormous pool of information. Leveraging this, you can make more informed and better decisions. They together can help you make the most out of your enterprise data and empower you to be flexible to address the challenges in a more precise manner. This keeps your IT department at ease by taking a huge pile of task off its shoulders.
Surely, enterprises have a reason to cheer as machine learning coupled with predictive analytics has a distinct potential to introduce unprecedented security measures in their systems and applications. However, what you need to remember that ubiquitous intervention of artificial intelligence and machine learning will not be sufficient independently. For predictive models enabled by machine learning to be successful, it is crucial to understand the know-how of the application. It requires a mix of experience and a meticulous planning. Security practitioners have started to capitalize the knowledge of the experts to use various algorithms and extract valuable insights from the raw data.
Machine learning and big data have set off on their journey to making inroads into the organizations all over the world now. It will not be surprising to see them hold a unique place across the functions, industries, and geographies in the future not very far from today. It should be fair to say that the machine learning and predictive analytics will have a critical role to play as they are slated to redefine enterprise security.
This has only started. What follows will be worth watching!