How Can Machine Learning Keep Your Network Safe?

The cyber security industry is ripe for machine-learning applications. Cyber security solutions need to analyse huge amounts of data in the form of alerts, and machine learning when applied correctly excels at aggregating it and presenting it in a way that cyber security professionals get an intuitive picture of what is happening and can act quickly to improve the security posture of their organization.

Bearing this in mind, it becomes apparent why machine learning solutions could provide a sustainable solution. Whether direct cyberattacks or insider threats, machine learning’s ability to analyze complex and large data sets is invaluable, especially when considering the current cyber skills shortage which is being felt globally.

Imperva  uses machine learning technology and its domain expertise in data and application security to solve cyber security problems. For example, in CounterBreach, machine learning algorithms analyse every SQL query by each unique user in an organisation. This allows security professionals to see beyond login records and gain valuable insight into exactly what data was accessed by which user, so they can zero in on abnormal user behaviours and head off potential threats.

It can also detect abnormal insider behavior because many users have legitimate access to the data. Machine learning helps them track and analyse users’ activity and flag inappropriate or abusive behaviour.

Terry Ray, CTO at Imperva has long championed machine learning techniques as a solution to complex cybersecurity problems. He said, "Businesses can employ solutions, especially those based on machine learning technology that can process and analyse vast amounts of data, to help them pinpoint critical anomalies that indicate misuse of enterprise data and that also help them to quickly quarantine risky users to prevent and contain data breaches proactively."