AI Threat Modeling for Real-Time Concurrent Risk Mitigation
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Solution Overview
Problem
Existing risk evaluation models are overly generalized, leading to potential harm by imparting a false sense of security and failing to handle concurrent threat events effectively, and they are often static and based on outdated assumptions, lacking the ability to adapt to real-time changes in risk scenarios.
Innovation Solution
A system and method utilizing artificial intelligence (AI) to create, enhance, and optimize a threat model that can dynamically evaluate and mitigate risks in real-time, incorporating a threat model built from historical experiences and instincts, with the ability to allocate resources and attention optimally to address threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If risk models use generalization and statistical assumptions to simplify analysis, then ease of operation and human comprehension improve, but measurement precision and reliability deteriorate due to false sense of security and inability to handle concurrent threats
Solution Approach 1:
The patent segments the risk evaluation process into multiple independent components: individual threat event analysis, inter-threat interaction modeling, and temporal progression tracking. Each segment handles specific aspects of risk without requiring full generalization, maintaining precision while improving operational clarity.
Solution Approach 2:
The patent implements dynamic risk models that continuously update as new threat events occur or conditions change. The system transitions from static generalizations to dynamic, adaptive evaluations that maintain precision by incorporating real-time data and inter-threat interactions without sacrificing operational ease through automated updates.
2Device complexity
If risk models are fixed in time based on historical data, then device complexity is reduced, but adaptability deteriorates as models cannot respond to real-time changes in threat scenarios
Solution Approach 1:
The patent prepares risk models in advance with predefined structures and relationships for common threat scenarios. When actual threats occur, the system activates and adjusts these pre-prepared models rather than building from scratch, maintaining low complexity while achieving rapid adaptability to real-time conditions.
Solution Approach 2:
The patent implements continuous feedback loops where risk model outputs are compared against actual threat events and outcomes. This feedback drives automatic model updates and adjustments, enabling the system to adapt to real-time changes without increasing operational complexity through automated learning and refinement processes.
3Device complexity
If traditional risk models handle threat events in isolation, then device complexity is reduced, but reliability deteriorates due to cascading failures when multiple concurrent threats occur
Solution Approach 1:
The patent merges individual threat event analyses with inter-threat interaction modeling into a unified evaluation framework. The system combines multiple threat assessments while accounting for their interactions, maintaining manageable complexity through structured integration while significantly improving reliability for concurrent threat scenarios.
Solution Approach 2:
The patent creates a composite risk evaluation structure that integrates multiple analytical layers: individual threat assessment, interaction effects, temporal progression, and mitigation strategies. This composite approach handles concurrent threats reliably while maintaining operational complexity at acceptable levels through modular architecture.
Data Source
AI summary
Systems and methods that create, use, enhance, maintain, and otherwise optimize a threat model—generally used for risk evaluation and threat mitigation—comprising artificial intelligence inherent in an entity is described. Certain embodiments describe, in countering a threat event, a need for an artificial intelligence entity to cooperate with non-expert users to give the users abilities to act on the domain in the users' self-interest. In countering a threat event, certain other embodiments describe that no single actor, in a heterogeneous collection of actors with varying abilities, may act in isolation to efficiently and effectively counter the threat to the collection; a minimum inevitable loss for the threat event may be achieved by an active cooperation of the heterogeneous actors of type comprising at least one of: expert users, non-expert users, and artificial intelligence entities that are sufficiently trained and knowledgeable on the threat event.