AI-Driven Phishing Campaign Controller for Adaptive Security Training

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Solution Overview

Problem

Phishing attacks are becoming increasingly sophisticated, and existing security awareness systems struggle to effectively train users to detect highly individualized and real-time threats, as they lack the ability to create a simulated phishing environment that mimics real-world attacks.

Innovation Solution

An artificial intelligence-driven security awareness system that uses machine learning algorithms to send varied simulated phishing messages, adapting the campaign based on user responses and behavior, and creates personalized phishing scenarios by analyzing user history, attributes, and industry profiles to mimic real-world threats.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If a security awareness system uses simulated phishing attacks to train users, then users can learn to recognize phishing threats, but the system cannot effectively train users for highly sophisticated and personalized attacks because it lacks adaptability to individual user behaviors and real-time conditions

Engineering Contradiction:

Improveeffectiveness of phishing attack trainingVSAvoidability to create personalized phishing scenarios

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The system dynamically adapts phishing campaign parameters including message content, timing, frequency, and channel selection based on real-time user behavior data, industry profiles, and response patterns. This dynamic adjustment enables the system to create personalized phishing scenarios that mirror actual attack tactics while training users effectively.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system incorporates feedback loops that analyze user responses to simulated phishing attacks and use this information to refine future campaign strategies. By continuously learning from user interactions and adjusting the simulated attacks accordingly, the system improves both training effectiveness and personalization capability.

Inventive Principle:

Principle #23Feedback

2Reliability

If the system sends multiple simulated phishing messages with varying parameters to train users, then training effectiveness improves, but the complexity of managing and coordinating these varied messages increases

Engineering Contradiction:

Improvetraining effectivenessVSAvoidcampaign management complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system autonomously manages campaign coordination by automatically selecting optimal message parameters, timing, and channels based on pre-configured industry profiles and real-time user behavior analysis. This self-service capability reduces manual management complexity while maintaining high training effectiveness through intelligent automation.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system manages complexity by systematically varying key parameters such as message content, timing, frequency, and delivery channel according to predefined patterns and user profiles. This structured approach to parameter variation enables diverse training scenarios without proportionally increasing management complexity.

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If the system adapts campaigns based on real-time user responses and behavior, then personalization and realism improve, but the time required to analyze data and adjust campaigns increases

Engineering Contradiction:

Improvereal-time adaptation capabilityVSAvoidcampaign adjustment time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-configuring industry profiles, user segments, and campaign templates before actual training occurs. This preparation enables rapid real-time adaptation by selecting from pre-defined frameworks rather than creating scenarios from scratch, reducing adjustment time while maintaining personalization.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system replaces manual analysis and adjustment processes with automated machine learning algorithms that continuously process user behavior data and generate campaign adjustments. This substitution of mechanical manual operations with automated intelligent systems enables real-time adaptation without proportionally increasing time investment.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11799906B2Systems and methods for artificial intelligence driven agent campaign controller

Publication Date: 2023.10.24 KNOWBE4 INC

AI summary

The present disclose describes systems and methods for creating a simulated phishing campaign for a user based on at least a history of the user with respect to simulated phishing campaigns. A database may be configured to store simulated phishing campaign history of a user, the simulated phishing campaign history comprising information on events associated with the user during one or more previous simulated phishing campaigns, A campaign controller may identify the simulated phishing campaign history of the user from the database, determine based at least on the simulated phishing campaign history of the user, a model from a plurality of models for creating a simulated phishing campaign directed to the user; and create, responsive to the determination, the simulated phishing campaign using the model.