AI Cyber Security Analyst Using Adaptive Threat Hypotheses

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Legacy cyber security tools are inadequate in detecting evolving cyber threats due to their reliance on predefined rules and signatures, failing to recognize novel attacks and subtle changes, and struggle with insider threats, necessitating a more automated and adaptive approach.

Innovation Solution

An AI cyber security analyst system that uses machine learning to identify abnormal behavior, form hypotheses on potential threats, gather and analyze data, and generate formalized reports to assist human analysts, leveraging multiple AI models and machine learning techniques for automated threat detection and analysis.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional signature-based detection methods are used, then known threats can be detected, but novel attacks and subtle changes go undetected

Engineering Contradiction:

Improvedetection accuracyVSAvoidability to detect novel threats

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The system transitions from static signature-based detection to dynamic behavioral analysis using machine learning models that continuously adapt to new threat patterns. The AI models learn from historical data and evolve their detection capabilities without requiring manual signature updates, enabling automatic adaptation to novel attacks.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent replaces the mechanical rule-based detection system with an intelligent AI-based system. Instead of relying on predefined signatures and manual rules, the system uses machine learning algorithms that automatically analyze behavioral patterns, enabling detection of both known and unknown threats through intelligent inference rather than pattern matching.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If more security rules and policies are defined to cover more threats, then detection coverage improves, but the complexity of maintenance increases

Engineering Contradiction:

Improvethreat coverageVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The AI system performs self-learning and self-updating without requiring manual intervention. The machine learning models automatically train on new data, adjust their parameters, and improve their detection capabilities autonomously, eliminating the need for continuous manual rule creation and system reconfiguration by security personnel.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The AI-based detection system serves multiple functions simultaneously: it detects known threats, identifies novel attacks, analyzes behavioral patterns, and adapts to new threat landscapes all through a single unified platform. This multi-functional approach replaces the need for multiple separate rule sets and detection mechanisms.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Measurement precision

If manual analysis of security incidents is performed, then accurate threat assessment is achieved, but the volume of incidents overwhelms human analysts

Engineering Contradiction:

Improvethreat assessment accuracyVSAvoidincident processing capacity

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The system segments the incident analysis process into distinct phases: automated data collection, AI-based pattern recognition, hypothesis generation, and human analyst review. This segmentation allows AI to handle the high-volume initial filtering and analysis, reserving human expertise for complex decision-making and strategic responses.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The AI system acts as an intermediary between the vast volume of security incidents and human analysts. It processes and pre-analyzes incidents, generating structured reports and prioritized alerts that facilitate more efficient human review, thereby amplifying the productive capacity of security teams without compromising assessment quality.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Speed

If automated threat detection is implemented, then response time improves, but the ability to detect sophisticated insider threats decreases

Engineering Contradiction:

Improveresponse timeVSAvoidinsider threat detection

Core Design Contradiction:

SpeedVSReliability

Solution Approach 1:

The system changes the detection parameters from static signatures to dynamic behavioral baselines. By establishing normal behavioral patterns for users and systems over time, the AI can detect deviations that indicate insider threats, maintaining high detection reliability while enabling rapid automated response to anomalous activities.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12407712B2Artificial intelligence cyber security analyst

Publication Date: 2025.09.02 DARKTRACE HLDG LTD

AI summary

An analyzer module forms a hypothesis on what are a possible set of cyber threats that could include the identified abnormal behavior and/or suspicious activity with AI models trained with machine learning on possible cyber threats. The Analyzer analyzes a collection of system data, including metric data, to support or refute each of the possible cyber threat hypotheses that could include the identified abnormal behavior and/or suspicious activity data with the AI models. A formatting and ranking module outputs supported possible cyber threat hypotheses into a formalized report that is presented in 1) printable report, 2) presented digitally on a user interface, or 3) both.