Digital Risk Twins: AI-Driven Simulation for Enterprise Compliance
Keywords:
Digital Risk Twins, AI-driven Simulation, Compliance Management, Machine Learning, Deep Learning, Cybersecurity, GDPR, Financial Compliance, Healthcare Compliance, Risk Mitigation, Dynamic Regulations, Compliance Gaps, Real-Time Monitoring, Data Protection, Self-Learning SystemsAbstract
In this paper, the author examines such a concept as Digital Risk Twins (DRTs) as driven by AI to address the facilities of the enterprise as they relate to compliance management in a dynamic regulatory landscape. DRTs simulate potential risks, vulnerabilities, and compliance gaps with the help of AI-based simulating virtual representation of the infrastructure of an organization. DRTs can in real time adapt to new regulatory changes and emergent threats through the application of machine learning (ML) and deep learning (DL). Various simulated situations prove the benefit of DRTs in such fields as GDPR compliance, monitoring financial transactions, and safe storage of health-related data. Even though issues such as data quality and computing power exist, DRTs provide a scalable, proactive compliance management solution.
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