Machine Learning in Cyber Security - Efficiency and a Higher Purpose

The Great Resignation, as we are used to calling the ongoing trend for employees to voluntarily quit their jobs en masse, beginning in early 2021, has an alarming role in the lack of qualified cybersecurity experts able to tackle the grave cyber-security threats at a global scale. In the past two years many, including highly skilled tech workers, changed jobs or quit to find the right salaries, benefits, work arrangements and callings. Could machine learning help overcome the scarcity of skilled professionals?

Aparna Rayasam, head of Trellix Threat Labs, shares her thoughts on the role of machine learning in the future of cyber security.

How machine learning is tackling the talent shortage?

Everyone in cybersecurity will have to be more efficient than ever as coveted technologists are more sought-after due to worsening cyber threats. Automation is an obvious way to leverage software and maximize contributions from mature and junior staffers.

1.    Research

Trellix is in the process of exploring what is common across all of these research functions to determine which can be automated. In this environment, a researcher’s time is precious and must be used in, say, training the datasets, rather than running the queries themselves. Less interesting and more processing-heavy work can be delegated to programming and computing tools. The human element must be as optimized as possible.

2.    Private sector

Finding high-quality talent for their growing security teams has been challenging for customers. Trellix encourages the same efficiency solutions that it employs for itself. For example, partnerships and vendors like Trellix can address the bulk of customers’ security challenges in a more comprehensive manner, given how many ways vulnerable network systems can be exploited. These goals are perfectly suited to the XDR platform, which allows security operations folk to aggregate all of the pathways for an attack and ensures that the problem is solved with as little disruption to their processes as possible.

3.    Public sector

The applied aggregation has led one of Trellix’s clients, a government agency, to reduce the number of people operating on a security posture. People, not machines, used to watch network traffic. But the range of threats from actual breaches to suspicious activity and potential threats ware not addressed. So Trellix created playbooks that aggregate across multiple vectors. This particular customer has hundreds of agencies relying on their lead, so this streamlined, more efficient process has been invaluable.

A call for unity

Cybersecurity saves lives – and balance sheets. But there isn’t much of a security community across the vendors. Typically, attacks are coordinated, so why aren’t security vendors? Trellix calls for the industry to work as a team for more efficiency, and without worrying about undermining our competitive edge.

 

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