why is traceability across the ai lifecycle central to watsonx.governance?

Traceability across the AI lifecycle is central to IBM watsonx.governance because it ensures compliance, audit readiness, accountability, and transparency in AI systems. It enables organizations to track data lineage, model versions, and decision-making processes throughout the AI lifecycle, which helps align AI outcomes with compliance and risk management standards. Traceability supports audit readiness by providing clear records of AI system development and operation, establishing accountability for decisions and predictions. It also enhances transparency, reducing reliance on external audits and building trust with stakeholders. Additionally, watsonx.governance automates capture of model metadata and maintains detailed audit trails vital for transparent, explainable AI at scale and for managing risk such as bias, drift, and accuracy issues. This comprehensive lifecycle governance is fundamental to driving responsible, trustworthy AI adoption while adhering to evolving legal and ethical requirements.