Open Innovation Security Modeling Under Adversary Uncertainty
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Solution Overview
Problem
Open innovation ecosystems face significant security challenges due to static security measures that fail to account for the strategic and adaptive nature of adversaries, leading to underinvestment or misallocation of resources, and lack of integration with probabilistic models and entropy-based measurements, resulting in incomplete risk assessments.
Innovation Solution
A system integrating game-theoretic security modeling, Bayesian risk assessment, and uncertainty quantification to analyze and mitigate security risks by representing innovator-adversary interactions as a two-player zero-sum game, computing Nash equilibrium, and using entropy-based risk assessment to determine optimal defensive investments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static security measures are adopted, then implementation simplicity is improved, but security effectiveness deteriorates due to inability to account for adaptive adversary behavior
Solution Approach 1:
The patent transforms static security measures into dynamic adaptive security measures by implementing continuous monitoring of adversary behaviors and automatically adjusting security controls. The system evolves security strategies in real-time based on detected threat patterns, transitioning from fixed configurations to adaptive responses that match the dynamic nature of cyber threats.
Solution Approach 2:
The patent incorporates feedback mechanisms where security outcomes are continuously measured and fed back into the security decision-making process. This includes monitoring adversary responses to security controls, analyzing attack patterns, and using this information to refine and adjust security strategies, creating a closed-loop adaptive security system.
2Device complexity
If uniform security investment is allocated across all areas, then resource allocation simplicity is improved, but security coverage deteriorates due to misallocation away from high-value assets
Solution Approach 1:
The patent applies local quality by differentiating security investment levels based on the specific characteristics and value of different assets, departments, and threat vectors. High-value critical assets receive enhanced security measures and higher investment, while lower-risk areas receive proportionate protection, optimizing the security portfolio across the organization.
Solution Approach 2:
The patent dynamically adjusts security investment parameters based on changing threat landscapes, asset criticality assessments, and risk analyses. Security budgets and resource allocation are not fixed but are continuously recalibrated based on measured risk levels and security performance metrics, allowing flexible reallocation to emerging threats.
3Productivity
If deterministic scoring models are used, then assessment speed is improved, but risk assessment accuracy deteriorates due to inability to capture uncertainty in adversary behavior
Solution Approach 1:
The patent replaces deterministic mechanical scoring models with probabilistic and information-theoretic approaches. Instead of fixed point-based risk scores, the system uses probability distributions, entropy measures, and statistical models to represent and quantify uncertainty in risk assessments, providing a more nuanced and accurate characterization of security risks.
4Measurement precision
If comprehensive probabilistic modeling is implemented, then risk assessment accuracy is improved, but computational complexity deteriorates
Solution Approach 1:
The patent segments the overall risk assessment problem into smaller, more manageable components that can be modeled and analyzed separately. By dividing complex adversary decision-making processes into discrete states, actions, and outcomes, the system can apply probabilistic modeling to specific segments rather than attempting to model the entire system at once, reducing computational burden.
Solution Approach 2:
The patent implements probabilistic modeling at appropriate levels of detail rather than comprehensively across all security aspects. The system applies sophisticated probabilistic and information-theoretic methods where they provide the most value (in adversary behavior modeling and uncertainty quantification) while using simpler approaches for well-understood or low-impact areas, optimizing the balance between accuracy and complexity.
Data Source
AI summary
A method and system for analysing and mitigating security risks in open innovation ecosystem is disclosed. The system comprises detects potential vulnerabilities in open innovation activities, including intellectual property exchanges, research data handling, and partner collaboration processes to provide identified threat data as input. The system represents interactions between an innovator and an adversary as a two-player zero-sum game. The system computes Nash equilibrium from the payoff matrix. The Nash equilibrium represents optimal defensive investments under adversarial conditions. The system also models adversary uncertainty using probability distributions, and further updates the equilibrium strategies based on incomplete or dynamic information. The system, thereafter, evaluates adversary uncertainty using Entropy-based risk assessment to determine levels of security investment resources responsive to the quantified uncertainty. Finally, the system integrates results of the equilibrium analysis, probabilistic inference, and uncertainty quantification to generate actionable security recommendations and guidelines for mitigation strategies.