The Cyber Defense eMagazine January Edition for 2024

Privileged Access Analytics (PAA) is now one of the most valued capabilities in our entire portfolio—
because we started with a more precise problem statement.
Collect the right data
No AI model can be better than the data it's trained and operates on. The PAA models referenced could
not operate without knowledge of every Kerberos transaction and/or Azure AD action in the relevant
domain. That data trains its view of privilege and relationships, as well as gives the right insight to
evaluate account usage in real-time for detection. Similarly, reliably identifying network command and
control requires very granular time-series data on packet flow, along with a massive corpus of labeled
data for both bad and good traffic.
It may be tempting to use the data that’s most readily available. For networks, that may be flow or firewall
logs rather than detailed network metadata. But if you take shortcuts like that, it will dramatically impact
the value delivered.
Choose the best AI approach for each problem
You have the right problem statement and the right data; now it’s time to select an AI approach tailored
to the problem you’re trying to solve. There are a plethora of machine learning (ML) techniques
available—from neural networks and deep learning, to K-means clustering, novelties, and (the current
rage) transformer and large language models
As the “No free lunch” theorem dictates, just as with the data, there are no shortcuts to success when it
comes to working with AI algorithms. Data scientists and machine learning engineers (MLEs) need to
understand the data they’re working with and the problem at hand in order to select a specialized
algorithm that will achieve the desired results—and general-purpose algorithms won’t cut it. In fact,
choosing the wrong algorithm may give results that aren’t just suboptimal, but flat-out wrong.
Oh, and if you think that LLMs/transformers make this theorem obsolete, you’d be wrong: we’ve evaluated
state of the art for detection use cases and found that they underperform specialized models today. LLMs
are good at predicting what’s next (e.g. how many bytes will be in the next packet), but not so good at
categorizing things (e.g. is this connection malicious or benign).
Run at speed and scale (and cost-effectively!)
Cyberattacks happen fast. This is especially true in the cloud, but even in-network, ransomware attacks
can occur seemingly in the blink of an eye. Every minute counts for defenders. According to one study,
the vast majority of organizations—90 percent—can’t detect, contain, and resolve cyber threats within an
hour.
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