High Tech — Regulatory Compliance Gap Assessment for AI Models

Overview

The primary purpose of this DAG is to evaluate compliance gaps for AI models concerning regulatory requirements in the high-tech industry. It begins by extracting data from existing systems, including internal databases and business APIs, to gather relevant information for the compliance assessment. The ingestion pipeline consists of several key steps: first, data is collected from specified sources, followed by a thorough analysis to identify discrepancies between current practices and regulato

The primary purpose of this DAG is to evaluate compliance gaps for AI models concerning regulatory requirements in the high-tech industry. It begins by extracting data from existing systems, including internal databases and business APIs, to gather relevant information for the compliance assessment. The ingestion pipeline consists of several key steps: first, data is collected from specified sources, followed by a thorough analysis to identify discrepancies between current practices and regulatory standards. Quality controls are implemented throughout the process to ensure data integrity, including validation checks and automated alerts for any detected non-compliance issues. Once the analysis is complete, a comprehensive assessment report is generated, detailing the findings and recommendations for remediation. Additionally, the compliance register is updated to reflect the current status of adherence to regulations. Monitoring key performance indicators (KPIs) such as the number of compliance gaps identified and the time taken to resolve them is essential for ongoing compliance management. This DAG not only aids in maintaining regulatory compliance but also enhances the overall governance of AI models, reducing risks associated with non-compliance and fostering trust with stakeholders.

Part of the Enterprise Search solution for the High Tech industry.

Use cases

• Reduces risk of regulatory penalties and fines
• Enhances trust and transparency with stakeholders
• Improves governance of AI model compliance
• Facilitates proactive compliance management
• Increases operational efficiency in compliance processes