Life Science — Automated Compliance Incident Management for AI Models

Overview

The purpose of this DAG is to manage the registration and tracking of compliance incidents associated with AI models in the Life Sciences industry. It collects data from various monitoring and auditing systems, ensuring that incidents are documented and addressed effectively. The ingestion pipeline begins with the collection of incident data from sources such as monitoring logs, audit trails, and compliance reports. The processing steps include data validation, access verification through role-b

The purpose of this DAG is to manage the registration and tracking of compliance incidents associated with AI models in the Life Sciences industry. It collects data from various monitoring and auditing systems, ensuring that incidents are documented and addressed effectively. The ingestion pipeline begins with the collection of incident data from sources such as monitoring logs, audit trails, and compliance reports. The processing steps include data validation, access verification through role-based access control (RBAC), and the generation of incident reports. Quality controls are integrated into the workflow to ensure data integrity and compliance with regulatory standards. The outputs of this DAG include detailed incident reports, compliance dashboards, and metrics for incident resolution. Monitoring key performance indicators (KPIs) such as incident resolution time and the frequency of recurring incidents provides insights into operational efficiency and compliance adherence. In the event of a failure, the DAG is designed to automatically restart after a predefined period, ensuring minimal disruption to incident management processes. This automated approach not only streamlines compliance tracking but also enhances the overall reliability of AI model governance, delivering significant business value by reducing compliance risks and improving response times.

Part of the Enterprise Search solution for the Life Science industry.

Use cases

• Reduces compliance risks associated with AI model usage
• Enhances operational efficiency through automation
• Improves visibility into incident management processes
• Facilitates adherence to regulatory standards in Life Sciences
• Enables proactive incident resolution and reporting