Optimizing Enterprise Risk Management for Decision Making Using Knowledge Graph

Abstract

The challenge in current enterprise risk management is that hundreds of risks are eventually recorded without knowing how hazards relate to one another or cascade. The distinction between peripheral and critical hazards is unknown to decision-makers. Organizations can depict the interconnectedness of risk in a structured, adaptable, and understandable way by showing these components as nodes and their interactions as edges. This knowledge graph makes it possible to store and query risk data in ways that are not entirely supported by conventional relational models. This method's ability to execute graph queries that uncover links and patterns that would otherwise be obscured in siloed datasets is one of its main advantages. Such inquiries can reveal how a single threat can lead to many vulnerabilities across multiple assets, or how flaws in shared systems can directly and indirectly raise exposure to interconnected hazards. These revelations draw attention to structural flaws that linear or isolated investigations frequently ignore. Organizations can improve situational awareness and long-term risk governance by using such a knowledge graph to find hidden trends, pinpoint important risk spots, and more efficiently prioritize mitigation efforts. The knowledge graph also helps to optimize enterprise risk management goals like resource allocation, control prioritization, and prompt reaction planning. Enterprise risk management can effectively represent the intricate relationships between risks, vulnerabilities, threats, and assets by incorporating a knowledge graph. Businesses can concentrate mitigation efforts where they will have the biggest impact by determining which nodes and edges are the most important and highest impact. This focused strategy increases overall resilience and decreases inefficiencies.