Research: Zero Trust 2.0: Advances, Challenges, and Future Directions in ZTA
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Predictive Modeling of Security Effectivenes
AI-Driven Analysis of Zero Trust Architectures
Objective: Use AI/ML to evaluate and predict the effectiveness of Zero Trust implementations.
Methodology: Leverage machine learning models to analyze real-world architecture data.
Data Source: Collect anonymized configurations, policy sets, and incident outcomes from participating organizations.
Focus Areas: Identity management, network segmentation, access controls, and endpoint posture.
ML Techniques: Apply supervised learning to detect patterns linked to reduced breach risk.
Outcome: Generate a predictive risk score for different Zero Trust maturity levels.
Validation: Compare model outputs with known breach events and red team assessments.
Deliverables: Publish a Zero Trust Effectiveness Index and a research whitepaper.
Collaboration: Invite public and private organizations to contribute via secure data-sharing agreements.
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