Data Lineage and Traceability in AI Outputs

Track how data flows through Generative AI systems to ensure transparency and accountability.

Pillar

Data – Readiness, Governance, Quality & Ethics

Overview

This course explores the critical practice of data lineage and traceability within GenAI systems. Participants will learn how to map and monitor data movement and transformations across AI workflows to maintain transparency, support compliance, and improve trust in AI-generated outputs.

Learning Objectives

Participants will be able to:

• Understand the concepts and importance of data lineage in AI systems

• Map data flows and transformations in GenAI pipelines

• Implement traceability mechanisms to audit data usage and output generation

• Support regulatory compliance through transparent data tracking

• Diagnose issues and improve data governance using lineage insights

Target Audience

• Data engineers and architects

• AI developers and MLOps specialists

• Data governance and compliance officers

• AI project managers

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Instructor-led sessions on lineage principles and tools

• Hands-on mapping of data flow in GenAI workflows

• Case studies on transparency and audit in AI systems

• Group exercises in designing traceability frameworks

Materials Provided

• Templates for data lineage mapping

• Guides on traceability best practices and tool selection

• Example audit logs and reports

Outcomes

• Ability to implement comprehensive data lineage in GenAI projects

• Enhanced transparency and trust in AI-generated content

• Stronger compliance with data and AI regulations

• Improved issue resolution through detailed data tracking

Outline / Content

Day 1: Fundamentals of Data Lineage and Traceability

• Introduction to data lineage concepts

• Importance for AI transparency and governance

Day 2: Mapping Data Flows in GenAI Systems

• Techniques for tracking data ingestion, processing, and output

• Tools and technologies for lineage capture

Day 3: Implementing Traceability Frameworks

• Designing audit trails for AI workflows

• Integrating lineage tracking into AI pipelines

Day 4: Leveraging Lineage for Compliance and Improvement

• Using lineage data for regulatory audits

• Continuous monitoring and refinement of data flows