Threat history is generally available in three places: network, endpoint and logs.
Endpoint Detection and Response (EDR) provides a detailed ground-level view of the processes running on a host and interactions between them.
Network Detection and Response (NDR) provides an aerial view of the interactions between all devices on the network.
Security teams then configure Security Information and Even Management (SIEMs) to collect event log information from other systems and correlate between data sources.
Security teams that deploy these tools are empowered to answer a broad range of questions when responding to an incident or hunting for threats.
For example, they can answer: What did this asset or account do before the alert? What did it do after the alert? Can we find out when things started to turn bad?
Of this group, NDR is most critical because it provides perspective where the others cannot.
For example, exploits that operate at the BIOS level of a device can subvert EDR or malicious activity may simply not be reflected in logs.
But, their activity will be visible by network tools as soon as they interact with any other system through the network.
Effective AI-driven network detection and response platforms collect and store the right metadata and enrich it with AI-derived security insights.
Effective use of AI can then drive the detection of attackers in real time and perform conclusive incident investigations.
Powered by AI – machine learning, deep learning and neural networks – attacker behavior models constantly learn and adapt to automatically detect advanced threats in real time, before they do damage.
In addition to threat detection, AI is instrumental in triaging and prioritizing the highest-risk threats and correlating attacks with compromised host devices.
Tier 1automation alone can condense weeks – and even months – of work into minutes and reduce the security analyst workload by 37X.
Here are some tips to improve thedetection of attacker behaviors in network traffic, security posture and riskmanagement:
When a threat is detected, security teams must answer a wide range of questions in order to respond quickly and decisively. These include:
The first step is to make sure that the attributes necessary to answer these questions are readily available to the analyst. Relevant security information, rich in context, must available to incident responders.
Machine learning-derived attributes, including host identity and beaconing, provide vital context that reveals the complete scope and scale of an attack.
Context that puts the most relevant information at your fingertips provides a trail of forensic evidence about threat behaviors throughout the entire attack lifecycle while eliminating the endless hunt and search for threats.