Cyber Defense Magazine Special Annual Edition for RSA Conference 2021

What’s the architecture for a successful deployment?
For security teams, the ideal is to have a “single-pane-of-glass” that collects and collates threat telemetry
from all of the different sources and provides a single view of threat activity across the threat lifecycle.
Typically, organizations are electing to implement a SIEM tool, or a data-lake that provides data mining
and search capabilities. SOAR tools are also rapidly gaining in popularity - as a way to help organizations
collect and analyze evidence of threat activity.
The key capability that organizations need is being able to quickly reconstruct events from the collected
and collated telemetry to understand what happened, how it happened, and what the impact of that event
is. With a centralized view of activity, analysts can ask and answer questions quickly to understand
whether there has been lateral movement from an initial compromise, whether data has been exfiltrated
or not, etc.
Having access to full packet capture data is an indispensable resource in enabling this capability. With
access to the actual packets, including payload, analysts can see what activity took place on the network
and reconstruct events precisely - right down to seeing what data may have been exfiltrated and how an
attacker is moving around the network to increase their foothold.
Full packet capture data is also an incredibly powerful resource for proactive threat hunting. Packet datadriven
threat hunting and simulation exercises are a great way for teams to determine the effectiveness
of their detection tools - including AI/ML tools - to understand why they are not detecting events that they
ought to, or alternatively why they are incorrectly flagging non-malicious activity as malicious.
Packet capture data is invaluable as an evidence source because it is complete and reliable. Where a
skilled attacker will often delete or modify logs to hide their activity, it's very difficult for them to manipulate
packet data captured off the network - particularly when in most cases they are not even aware that it’s
being captured and don’t have access to it. This makes packet data a trusted source of “truth” about
what’s really happening on the network.
Right deployment, right outcome
AI/ML detection tools have a lot of promise. However, there are pitfalls if the right architecture and
capabilities are not in place. In order for AI/ML threat detection tools to deliver on their promise to reliably
detect and remediate threats, companies must be able to trust them to make the right decisions, not to
miss things, and to act accurately. To achieve this level of trust, we must be able to always verify and
validate decisions made by AI/ML tools. To do this, companies need to ensure they have the right data.
Packets are an indispensable resource for validating AI decisions. But in order for packet data to be
useful it needs to be complete and accurate, with no blind spots, and provide as much lookback history
as possible. It also needs to be easily accessible and provide fast search and data mining.
Considering how packet data can be incorporated into workflows is also important. When packet data
can be integrated into security tools - enabling analysts to pivot from a specific alert or event to the related
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