Security Leadership and the importance of investing in cybersecurity

Tending to security needs, making crucial and timeous decisions, and giving real leadership on IT security is as key as ever so that history doesn’t repeat itself for another year of potential and actual cyber security attacks.

“Cyber attacks will evolve through mediums such as the Internet of Things (IoT), cloud and mobile. Network complexity will grow for the same reasons, perpetuating the challenges we face. We’ll also see more innovation when it comes to security solutions. Technologies such as machine learning-based analytics and threat hunting will help analysts address security challenges more proactively. I believe this innovation will pave the way to the next iteration of risk mitigation,” says a security intelligence industry expert.

“I go back to my career-long assertion that unless and until security professionals master the basics, they’ll continue to suffer at the hands of cybercriminals. No technology can fix careless oversights involving patches, passwords and information management. Not in 2017, not in 2027, not ever. Spending money and busywork will no doubt make it look like things are happening, but when you’re not focusing on the right target, anything can and will happen. That’s not a risk you should toy with.”

A new evolution in endpoint security – responsive machine learning

According to an article by Jack Danahy, Security Intelligence, machine learning is changing the way industries address critical challenges by using the combined power of automation, cloud-based scalability and specialized programming to surface unexpected relationships and insights. With thousands of new malicious programs emerging every day, security solutions that integrate responsive machine learning can identify and block threats that haven’t been seen before, so long as those systems are trained and tested at an appropriate pace. With this technology, it is now possible to derive value from vast quantities of data in a way that was unimaginable twenty years ago.

Machine learning in action can best be seen in the Health Care sector as it was a natural early application for this breakthrough and it was applied to the challenge of understanding the language of medicine. It uses specific algorithms to identify subtle patterns and highlight the elements that contributed most to the illness under consideration, providing an evolutionary endpoint in information. The same can apply to cybersecurity.

Learning the Language of Cybersecurity

Machine learning becomes more informed and accurate over time. The longer a condition is observed or a language is studied, the more precise the model becomes. Making use of decades of experience in security management, strategy and incident response, machines can be trained to understand the language of cybersecurity, recognize root causes, highlight urgent threats and provide answers to security questions for less experienced analysts. Of course, there are also limits to conventional machine learning, and maintaining endpoint security in a rapidly changing environment requires testing and training machine learning models in near real time to maintain confidence amid the sheer volume of constantly changing data related to endpoint software and threats.

Machine learning is a hot topic at the moment, and for enterprises evaluating endpoint security solutions, it’s important to understand the differences between conventional and responsive machine learning. At the moment, machine learning is rapidly changing the security solution landscape and looks to be delivering the forward-looking coverage and accuracy that modern businesses need.

 

Compiled by Karin Dubois, Network Group