The first challenge is to forget the idea that Machine Learning is a stand-alone “magic bullet” solution to cybersecurity. Machine Learning cannot deliver results if it’s simply layered over an existing, ineffective security solution. It needs to be incorporated as one element of a full security solution.
When we say a “full security solution”, we mean a solution that combines a multi-functional AI platform (which includes Machine Learning), with a full staff of cybersecurity experts, that protects you at each stage of a threat’s lifecycle.
For this reason, many organizations need to make substantial changes to their cybersecurity approach before they can adopt Machine Learning. Most organizations lack sufficient cybersecurity staff, and are working with traditional MSSPs who maintain a last-generation approach to security. For most organizations, the only solution to adopt Machine Learning is to choose a better partner who covers their staffing and technology gaps, and who offers comprehensive services.
My advice to enterprises adopting Machine Learning is to look for a cyber security partner that can employ algorithms customized to their business, and not accept a blanket approach that is used across the board. This is paramount because every business is different and needs a tailored approach for optimal protection against today’s sophisticated cyber threats.
It isn’t only our opinion but a number of experts have voiced how Machine Learning can identify cybersecurity vulnerabilities. Enterprises are already adopting this model and top security experts and firms are constantly making the case for it. An ABI Research recently predicted that machine learning in cybersecurity will boost big data, intelligence, and analytics spending to $96 billion by 2021.
Machine Learning—especially as part of a full, AI-driven MDR program—provides multiple advantages over existing, traditional cybersecurity models. It accelerates an organization’s ability to identify, and protect against, emerging global threats.
It makes Threat Hunting a viable activity, as it can mine and hunt through terabytes of data, across thousands of devices, in near real-time, not weeks, increasing the speed of detection and reducing an advanced attack’s dwell time from months to minutes.
The need for Machine Learning will increase until it becomes a standard element of cybersecurity.
Now, we argue Machine Learning already needs to be a standard element of cybersecurity, for two reasons. First, the previously mentioned explosion in threat data. Organizations already need Machine Learning to effectively process this data, and the reasons for this explosion in threat data will only escalate in coming years. Second, because cybercriminals are already using Machine Learning to superpower and accelerate their attacks.
Our only defense: we need to bring greater Machine Learning to our defense.
And we are putting our money where our mouth is. Our full MDR service is already driven by our AI platform—AI.saac—which utilizes Machine Learning at every stage of our full left-to-right-of-hack security services.