Israel-based startup Oligo Security is exiting stealth mode with the public launch of its namesake software, offering a new wrinkle in library-based application security monitoring, observability, and remediation. Utilizing a technology called extended Berkeley Packet Filter (eBPF), it is able to provide agentless security coverage for open source code.

Given the prevalence of open source code in modern software — Oligo contends that it accounts for something like 80% or 90% — there is a need for software composition analysis solutions that can check the code for potential vulnerabilities. The current generation of solutions, however, is “noisy,” according to Oligo. It tends to produce a lot of false positives, and doesn’t contextualize alerts within a given runtime. The latter tendency is unhelpful for setting remediation priorities.

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China-based cyberespionage actor DEV-0147 has been observed compromising diplomatic targets in South America, according to Microsoft’s Security Intelligence team. 

The initiative is “a notable expansion of the group’s data exfiltration operations that traditionally targeted gov’t agencies and think tanks in Asia and Europe,” the team tweeted on Monday. 

DEV-0147’s attacks in South America included post-exploitation activity involving the abuse of on-premises identity infrastructure for reconnaissance and lateral movement, and the use of Cobalt Strike — a penetration testing tool — for command and control and data exfiltration, Microsoft wrote in its tweet. 

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Machine learning (ML) is a commonly used term across nearly every sector of IT today. And while ML has frequently been used to make sense of big data—to improve business performance and processes and help make predictions—it has also proven priceless in other applications, including cybersecurity. This article will share reasons why ML has risen to such importance in cybersecurity, share some of the challenges of this particular application of the technology and describe the future that machine learning enables.

Why Machine Learning Has Become Vital for Cybersecurity

The need for machine learning has to do with complexity. Many organizations today possess a growing number of Internet of Things (IoT) devices that aren’t all known or managed by IT. All data and applications aren’t running on-premises, as hybrid and multicloud are the new normal. Users are no longer mostly in the office, as remote work is widely accepted.

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