Webinar Trilogy: De-Regulate Your Regulated Data
Part 1: How to Shrink Your Domain of Regulatory Compliance
AI adoption is moving faster than governance, and unaware workforces are using public GenAI tools and private LLMs to process business data. Without proper AI data governance, AI systems can easily ingest regulated and confidential information, creating compliance, security, and privacy risks for your business.
Join our live webinar to learn why AI initiatives stall at the governance layer, how data readiness and lifecycle management can help you reduce risk, and what it takes to enable responsible AI adoption across the enterprise.
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How Do You Stay Compliant in Today’s AI-Enabled World?
Without proper controls to prevent regulated data from entering AI workflows, compliance, security, and privacy obligations become barriers rather than enablers. Threats like shadow AI, unmanaged data flows, and inconsistent governance models keep businesses from scaling AI responsibly, leaving innovation constrained and risk exposure unaddressed.
In this webinar, we'll explain how AI data governance enables safe AI adoption by preparing data for public and private GenAI use, reducing regulatory exposure, and supporting compliance through data lifecycle management (DLM) and privacy-by-design (PbD). You'll walk away with a practical roadmap for governing AI data, so your organization can innovate with confidence.
What You'll Learn:
- Where regulated data exists across front-office and back-office workflows.
- The connection between data classification policies and AI data governance requirements.
- Practical ways to reduce compliance, security, and privacy risk within AI ecosystems.
- How to prepare and de-regulate data for AI use, including masking, anonymization, tokenization, and synthetic data.
- The role of data lifecycle management (DLM) and privacy-by-design (PbD) in responsible AI adoption.