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How the Intersection of Machine Learning (ML), Endpoint Security Transforms Cybersecurity | #hacking | #cybersecurity | #infosec | #comptia | #pentest | #ransomware

In episode 95 of the Cybersecurity Minute, Rob Wood discusses how machine learning is impacting endpoint security.


00:18 — Machine learning is not super new in cybersecurity. In recent years, it’s gotten significantly better. One development that is exciting to Rob is the expansion of lanes of tools.

01:00 — Usually while doing your portfolio management, you think in swimlanes: your endpoint, network, applications, data, etc. Now, there’s this trend of collapsing these things in on themselves and leveraging machine learning to look at patterns across them.

01:30 — A great example is extended detection and response (XDR), which combines the network and endpoint swimlanes together and some others as well.

02:06 — These kinds of technologies are reactive in nature; something has to happen in order for them to detect it, and then you have to respond to it. But as you start to build together more of these effective, rapid-fire, high-signal detection and response engines and then couple them with automation, you can really get ahead of things like excessive dwell time and response times.

03:22 — Rob is excited about it all. This is something all folks in cybersecurity should really be looking at and keeping their eyes on.

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