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The term “cybersecurity” describes the tools and technologies designed to protect computers, networks, and data from unauthorized access, modification, dissemination or destruction. The skills required to monitor and defend these vital systems are in high demand, but there is a critical shortage of qualified applicants for security teams. To bridge this gap and bolster the lines of technological defense, organizations are increasingly turning to machine learning.

Machine learning is a specialized branch of data analysis that works on the premise that computers can learn and respond without being explicitly programmed for each situation. Essentially, this sort of artificial intelligence is structured around training and testing: collecting sample data, developing algorithms and models to interpret the data, and then evaluating the scenario to classify the data. In this way, cybersecurity defense is automated to a degree, with intrusion detection systems achieving some autonomy while scanning for both external (attacks from outside the organization) and internal (attacks from inside the organization’s local network) intrusions.

IBM Security is a leader in the development of machine learning for cybersecurity. IBM’s advances in artificially intelligent data defense fall into two broad categories: Intelligent Finding Analytics (IFA) and Intelligent Code Analytics (ICA). To begin with, Intelligent Finding Analytics represents IBM’s innovations in adaptive machine learning. This simulated cognitive ability allows machine learning to sort through the multitude of data collected and isolate true security breaches from the many false positive alerts that are generated as the system determines what constitutes valid computer usage from unauthorized intrusion. IFA delivers business value in improved response time, as machine learning trains itself without the need for results to be forwarded to a human security expert for interpretation. IBM boasts false positive removal rates of ninety-eight percent.

In conjunction, Intelligent Code Analytics (ICA) expands upon the findings of IFA to adapt to the increasing complexity of new and evolving programming languages. As ICA encounters a new application programming interface (API), machine learning uses previously collected data and security encounters to interpret whether the interface is vulnerable to intrusion and manipulation. From such testing, a new rule is created and the solution analysis engine of ICA monitors the application’s data flow contains a true vulnerability.

Thusly, the artificial intelligence of machine learning elevates cybersecurity through automated adaptation. With less human error and more timely defense, initiatives such as those developed by IBM lead the way to secure data amid evolving technologies.