Energy — AI Model Compliance and Audit Management Pipeline

Overview

The primary purpose of this DAG is to create and maintain a comprehensive registry of AI models utilized within the energy industry, ensuring compliance with regulatory standards and internal policies. The architecture involves integrating data from various internal systems, such as ERP transaction logs and external sources like industry compliance databases. The ingestion pipeline begins with data collection, followed by validation steps that check for data quality and security. Role-based acce

The primary purpose of this DAG is to create and maintain a comprehensive registry of AI models utilized within the energy industry, ensuring compliance with regulatory standards and internal policies. The architecture involves integrating data from various internal systems, such as ERP transaction logs and external sources like industry compliance databases. The ingestion pipeline begins with data collection, followed by validation steps that check for data quality and security. Role-based access controls are implemented to ensure that only authorized personnel can modify or access sensitive information. The processing logic includes regular audits of the AI models, generating reports that highlight compliance status, and triggering alerts when anomalies are detected. Key performance indicators (KPIs) related to compliance, such as the number of audits completed and the percentage of compliant models, are monitored to assess the effectiveness of the workflow. The outputs consist of detailed compliance reports and alert notifications, which are essential for decision-making and risk management. This DAG not only helps in maintaining regulatory compliance but also enhances the overall governance of AI model usage, ultimately leading to improved operational efficiency and reduced risk in the energy sector.

Part of the Enterprise Search solution for the Energy industry.

Use cases

• Ensures adherence to regulatory requirements in energy
• Reduces risks associated with non-compliance
• Enhances transparency in AI model usage
• Improves decision-making through detailed reporting
• Facilitates proactive management of AI model governance