Page 15 - Cyber Defense Magazine RSA Edition for 2021

approach to analyzing network activity. This alternate method of uncovering security threats may prove
to be a pivotal technology in the struggle to protect critical infrastructure, important assets and sensitive
data.

However, AI/ML threat detection tools require a significant investment in time and resources to deploy
correctly to each environment. It’s not enough to just throw them into an infrastructure: we need to make
sure that we can trust the technology, and that it’s attuned to our environment. It’s imperative to make
sure that it is deployed in such a way that increases efficiency, provides greater clarity into lurking threats,
and reduces the number of alerts we investigate every day, rather than simply adding additional noise
and workload to already overstretched security teams.

The promise of AI and ML

Adopting AI/ML threat detection tools is not about replacing existing security tools.
Rather, it’s about supplementing them with AI/ML to deliver additional capability
and benefits.

AI/ML threat detection tools have the potential to significantly improve detection
by identifying threats that other tools can't, especially at the earliest stages of the
attack lifecycle. They can potentially detect emerging unknown threats such as
Zero-day vulnerabilities - for which there are no existing ‘signatures’ - or threats
that signature-based tools struggle to detect - such as fileless malware. And they
can help associate related events to identify coordinated attack activity that might
indicate a high priority threat while reducing the amount of “noise” caused by lots
of individual event alerts. Their ability to detect abnormal behavior also enables
them to spot potentially malicious “insider threats” other tools may miss.

The other potential benefit that AI/ML tools offer is to improve productivity by
automating elements of the threat remediation process - particularly for commonly
occurring threats - and thereby free up time for analysts to focus on the high-priority
and more advanced threats.

Ultimately, AI/ML tools have the potential to automate many of the manual activities involved in SecOps,
such as isolating suspected compromised hosts from the network and blocking access to the network
from potentially compromised devices or users. The challenge is, however, that in order for security teams
to hand over responsibility for these sorts of activities to an AI/ML tool, they need to be able to trust that
tool to make the right decisions and know how it arrived at its decision. Otherwise, the danger is that

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