Abstract: Artificial intelligence and machine learning systems provide collection and correlation between publicly available regulatory and technology industry framework authoritative sources for proactive operational risk management, guidance, and regulatory obligation adherence. These systems can employ artificial intelligence techniques such as Robotic Process Automation, Natural Language Processing, Explication, Statistical Data Aggregation, Heuristics, and Machine Learning algorithms to perform analysis using machine understanding to identify assets or policies that warrant closer human scrutiny for governance, risk and compliance.

Abstract: Methods and apparatuses, including computer program products, are described for transaction-based security risk aggregation and analysis. A server computing device receives security risk data elements from a plurality of data sources, the data elements corresponding to a transaction submitted by a remote computing device to the server computing device for execution. The server computing device aggregates the security risk data elements into a weighted risk matrix. The server computing device generates a risk score for the submitted transaction based upon the weighted risk matrix. The server computing device determines an internal environment risk factor and an external environment risk factor based upon environmental risk data received from the plurality of data sources.

Abstract: Methods and apparatuses, including computer program products, are described for transaction-based security risk aggregation and analysis. A server computing device receives security risk data elements from a plurality of data sources. The security risk data elements correspond to a transaction submitted by a remote computing device to the server computing device for execution. The server computing device aggregates the security risk data elements into a weighted risk matrix and generates a risk score for the submitted transaction based upon the weighted risk matrix. The server computing device determines a business-level context and an execution priority of the submitted transaction, the business-level context and the execution priority based upon the security risk data elements.