Energy — AI Project Governance Document Management Pipeline

Overview

The purpose of this DAG is to manage the ingestion and governance of compliance documents related to AI projects in the energy sector. It ingests various document types, including regulatory guidelines, compliance reports, and project governance documents. The ingestion pipeline begins with the collection of these documents from multiple sources, ensuring that all relevant information is captured. Once ingested, the documents undergo a normalization process where they are standardized according

The purpose of this DAG is to manage the ingestion and governance of compliance documents related to AI projects in the energy sector. It ingests various document types, including regulatory guidelines, compliance reports, and project governance documents. The ingestion pipeline begins with the collection of these documents from multiple sources, ensuring that all relevant information is captured. Once ingested, the documents undergo a normalization process where they are standardized according to predefined compliance criteria. Following normalization, a validation step is performed by subject matter experts to ensure that all regulatory requirements are met. This validation process is crucial for maintaining the integrity of the governance framework. The validated documents are subsequently archived in a compliance register, which serves as a central repository for all governance-related documentation. Regular audits are conducted to ensure traceability and adherence to compliance standards. Key performance indicators (KPIs) for monitoring include the number of documents ingested, the validation success rate, and audit frequency. The business value of this DAG lies in its ability to streamline governance processes, enhance compliance adherence, and provide a reliable framework for managing AI projects in the energy sector.

Part of the Knowledge Portal & Ontologies solution for the Energy industry.

Use cases

• Improved compliance with regulatory requirements
• Enhanced traceability of governance documents
• Streamlined processes for AI project management
• Reduced risk of non-compliance penalties
• Increased confidence in AI project governance