Conquer AI model governance
Overcome challenges for AI success
Frequently Asked Questions
Non-compliance with regulations can result in penalties, reputational damage, and legal issues.
To address these challenges, deploy governance frameworks that ensure your AI is in compliance with GDPR, HIPAA or industry-specific AI regulations. Define and enforce standardized data models for regulatory adherence and enable automated data discovery, classification and lineage tracking to maintain compliance.
Biased AI outcomes can harm decision-making credibility and inclusivity.
To offset bias in your AI, monitor training datasets and outputs to detect bias and identify the needed changes. Use standardized data definitions to ensure fairness and reduce inconsistencies in training datasets and automate the profiling and tagging of the sensitive or biased attributes in those datasets.
Model drift refers to declining AI performance caused by changing data patterns and operational conditions, which can undermine accuracy and effectiveness.
To effectively manage and mitigate drift, continuously monitor AI model performance and adapt to the evolution of your data. Map and model the evolution of datasets to anticipate areas where drift may occur and enable real-time monitoring and lineage updates to flag drift and maintain transparency.
A lack of visibility into AI decisions can lead to a loss of trust and an inability to justify outcomes.
Data lineage can improve transparency and accountability in AI by providing traceability of the datasets that feed your AI models. Visualize your data structures and relationships to create a clear blueprint for AI inputs and track and display data lineage across the data lifecycle to ensure your stakeholders understand the flow and transformation of data.
AI deployments without proper validation can lead to increased risks from operational issues or ethical concerns.
To safely test AI models in controlled environments, leverage the Medallion Framework approach. Design and prototype data flows and schemas for AI experimentation and manage access and governance policies in sandboxes to ensure controlled, ethical experimentation.