Prevent: Security through Adversity

Read our Darktrace Discourse paper, authored by one of our researchers based at the Centre. The author has seven years’ experience in the automation of complex cyber-centric processes with specialization in the offensive domain, and holds a PhD in Astrophysics from the University of Cambridge. To close these gaps, the security industry must rethink how adversarial assessments are conducted. Automating certain aspects of red teaming could help organizations reduce costs and scale their efforts without compromising effectiveness. However, automation is no simple task—cyber adversaries continuously evolve, requiring human expertise to research and develop new attack simulations. This paper explores potential approaches to automating adversarial testing while addressing the psychological barriers that prevent broader adoption. By embracing the mindset of an attacker, organizations can strengthen their defenses and proactively mitigate risks before real-world threats strike. Download the full paper to learn how automation and strategic adversarial thinking can improve your organization's security posture.

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Although applicable to all areas of adversarial interaction, this short paper focuses on the ever-increasing requirement for augmentation of commonplace defensive measures with broad-spectrum adversarial simulation and/or emulation within the cyber security landscape. While all components of the Continuous Feedback Loop – prevent, detect, respond and heal are of great importance in establishing a robust defensive posture, particular focus will be granted within this paper to the first phase - prevention.

Researcher

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Research Abstracts

Rapid Process-Chain Anomaly Detection Using a Multistage Classifier

Sorting long lists of file names by relevance and sensitive content

A system for analyzing network activity to detect stealthy cryptocurrency mining

Autonomous detection of the intended function of a corporate inbox through meta-scoring

Using epidemiology theory to identify the most damaging network devices

Automatic Identification of Scanned IP Ranges

A real-time, self-correcting similarity classifier for emails

Using graph theory to identify critical nodes within computer networks

Analyzing network activity to detect compromised devices sending spam emails

Detecting and preventing misdirected emails with correspondence semantics

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