AI Red Team Phishing Simulation for Targeted Vulnerability Testing
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Solution Overview
Problem
Existing cyber threat protection systems rely on passive and generalized simulations, failing to identify specific vulnerabilities in organizations due to the inability to sift through vast amounts of security information and provide customizable attack simulations, leading to inefficiencies and increased costs with human red teams.
Innovation Solution
An AI adversary red team is configured to initiate penetration tests on email and network defense systems, utilizing machine learning to generate customized phishing emails and payloads tailored to specific organizational entities, devices, and users, simulating targeted attacks to identify vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If human red teams are hired to test defense systems, then customized attack simulations can be provided, but costs increase significantly
Solution Approach 1:
The patent creates a simulated red team using AI agents that copy and emulate the capabilities of human red team members. These digital twins can perform penetration testing, vulnerability assessment, and attack simulation without the high costs associated with hiring actual human security professionals, while maintaining the ability to provide customized attack scenarios.
Solution Approach 2:
The system enables organizations to perform their own security assessments using automated AI-driven tools. The simulated red team operates autonomously to conduct penetration tests and vulnerability assessments, eliminating the need for external human consultants and reducing dependency on expensive external services while maintaining assessment quality.
2Reliability
If passive and generalized simulations are used to detect cyber threats, then defense systems can be tested, but specific vulnerabilities in organizations cannot be identified
Solution Approach 1:
The patent implements attack simulations that are tailored to each organization's specific characteristics, including their industry, size, security posture, and known vulnerabilities. Rather than applying generic simulation scenarios, the system customizes each penetration test to match the local context and specific quality attributes of the target organization, enabling precise vulnerability identification.
Solution Approach 2:
The simulated red team dynamically adapts its attack strategies based on real-time feedback from the defense system and organizational characteristics. The AI agents can adjust their penetration testing approaches, pivot between different attack vectors, and evolve their tactics during the simulation based on what they discover, making the assessment highly adaptable to the specific vulnerabilities present.
3Adaptability or versatility
If human red team security professionals are hired, then attack simulations can be conducted, but time and resource consumption increase
Solution Approach 1:
The patent replaces the mechanical system of human red team operations with an automated AI-driven simulated red team. This substitution eliminates the time-consuming aspects of human coordination, planning, and execution while maintaining or enhancing attack simulation capabilities. The automated system can conduct comprehensive penetration tests much faster than human teams without sacrificing adaptability or versatility.
4Ease of manufacture
If traditional vulnerability scanning methods are used, then security resources can be allocated, but resources may be allocated to wrong places
Solution Approach 1:
The simulated red team implements continuous feedback loops during penetration testing, where AI agents analyze the results of each attack attempt and adjust their strategies accordingly. This feedback mechanism provides precise information about actual vulnerabilities versus perceived weaknesses, enabling organizations to allocate security resources accurately to the most critical areas rather than relying on imprecise traditional scanning data.
Data Source
AI summary
An AI adversary red team configured to pentest email and/or network defenses implemented by a cyber threat defense system used to protect an organization and all its entities. AI model(s) trained with machine learning on contextual knowledge of the organization and configured to identify data points from the contextual knowledge including language-based data, email/network connectivity and behavior pattern data, and historic knowledgebase data. The trained AI models cooperate with an AI classifier in producing specific organization-based classifiers for the AI classifier. A phishing email generator generates automated phishing emails to pentest the defense systems, where the phishing email generator cooperates with the AI models to customize the automated phishing emails based on the identified data points of the organization and its entities. The customized phishing emails are then used to initiate one or more specific attacks on one or more specific users associated with the organization and its entities.