Darktrace Releases Attack Path Modeling Research
Darktrace, a global leader in cyber security AI, announced that it has released its research on Attack Path Modeling: the technology that underpins the ‘Prevent’ product suite. The research paper titled, ‘Using graph theory to identify critical nodes within computer networks’, details how graph theory can be used to map cross-domain, realistic, and risk-assessed attack paths across an entire digital enterprise.
A skilled cyber adversary strives to exploit vulnerabilities spanning a wide variety of domains, internal and external to an organization. As a result, sourcing data across those domains is critical to creating a realistic, end-to-end model of attack paths exploited by cyber adversaries. If one or more of these domains is overlooked, the security team will be unable to fully identify or evaluate vulnerabilities to attack; nor will it be possible to optimize defensive resources and remediation efforts.
Emerging from research carried out in Darktrace’s Cyber AI Research Centre in Cambridge, the Prevent product suite is designed to tip the scales in favour of the defender by using AI to identify pathways which lead to key assets, and then make it harder for an attacker to access those pathways by hardening the environment. As a proactive risk-reducing approach, Attack Path Modeling gives security teams the ability to assess risk, identify vulnerabilities, and take counter measures to protect key assets, even disrupt the “disruptors.”
To support its Attack Path Modeling, a capability has been developed that models probable attacks against an organization’s crown jewels by analysing real-time telemetry passively and continuously. It emulates what an attack would look like using real data, specific to that organization, to create a unique understanding of whether the attack would be successful against existing defences. This initial module has been rolled out to Darktrace’s early adopter customers for testing, feedback and user interface refinement.
Darktrace has published supporting resource including:
- A new webpage for Darktrace’s Cyber AI Research Centre, consolidating research titles and abstracts detailing the most recent breakthroughs in Attack Path Modeling
- A research paper titled, ‘Using graph theory to identify critical nodes within computer networks’, which details how graph theory can be used to map cross-domain, realistic, and risk-assessed attack paths across an entire digital enterprise
- An Attack Path Modeling webpage, diving into the new technology and how it will turn the tables on cyber adversaries
- A new Discourse Paper entitled ‘Prevent: Security through Adversity’, exploring the core concepts behind the ‘Prevent’ product family
- A new Attack Path Modeling video exploring how the technology will leverage information from across the digital estate to determine the most realistic, end-to-end attack paths an adversary may take
“Darktrace research marks a major shift in mindset that will be critical in preventing cyber-attacks and we are proud to be leading the way. With Darktrace’s Attack Path Modeling, security teams will not only be able to react to threats, but get proactive by emulating and simulating the very paths an attacker will likely follow to get to critical assets,” commented Jack Stockdale OBE, Chief Technology Officer at Darktrace. “Think of it as turning the tables on ‘bad actors.’ This research has the potential to give security teams ways to ‘future proof’ people and organizations against unknown threats. It gives them the power to shift to offense to defeat an aggressive enemy.”
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