How AI Explains Cyberattacks for Security Training

This patent describes a cybersecurity training tool that uses a large language model to explain why machine learning identified a cyber threat, based on both fake and real attacks, for security teams and regular users.

What it covers

The cyber security training tool uses a natural language processor and a large language model (LLM) to analyze cyberattacks. It can look at both a 'synthetic cyberattack' in a fake network that mirrors a real one, and a 'real cyberattack' happening in the actual network (Claim 1). The tool then provides an analysis and explanation, using the LLM, for why machine learning flagged these attacks as threats. This explanation is designed for training either regular users or cybersecurity team members. For example, it can use the LLM to highlight malicious parts of an email, like a phishing attempt, and explain immediately on screen why the email is dangerous (Claims 4, 5).

What it doesn't cover

• —Does not cover cybersecurity training that relies solely on human instructors without machine learning analysis of threats.
• —Does not cover systems that only analyze real cyberattacks without also using a mimic network for synthetic attacks.
• —Does not cover training tools that explain cyber threats without using a large language model.
• —Does not cover general IT security awareness training that isn't specifically tied to machine learning's identification of a threat.
• —Does not cover systems that only provide long-form reports days later, rather than immediate, on-the-spot feedback for users.
• —Does not cover training that doesn't involve a user interface displaying the explanation and understanding of the machine learning.

The clever bit

The truly novel aspect is using a large language model not just to detect threats, but to translate complex machine learning detections and network data into understandable, natural language explanations for human training.

Why it matters

Understanding complex cyber threats and the sophisticated machine learning models that detect them is a major challenge for both technical staff and everyday users. This patent addresses this by making the 'why' behind a threat detection accessible through AI-powered explanations. This can significantly improve how quickly and effectively people learn to identify and respond to cyber risks, reducing human error in a critical area.

Real-world examples

• 1.Darktrace's AI-driven security platforms
• 2.Security awareness training platforms with AI explainability
• 3.Phishing simulation and training tools that provide immediate feedback
• 4.AI-powered security operations center (SOC) tools