The author provides best practices for AI security, privacy, and compliance, including putting controls in place based on data type, ensuring data is AI-safe, automating access governance, understanding data consumed by various models, managing data privacy, compliance, and security, and implementing controls across the entire data landscape. The ultimate goal is to unlock the full potential of AI innovation, minimize risk, meet ethical and regulatory standards, and drive more value.
Key takeaways:
- As the adoption of AI grows, there’s been a shift from analytics to AI, impacting traditional approaches to data cataloging, governance, privacy, security, quality, bias and compliance.
- Organizations need to automatically find, classify, and catalog their data to minimize risk, prepare data for AI, and automate data management and optimization.
- Identifying risky data is paramount in the era of data breaches and cyber threats, especially in unstructured data. Organizations need to detect and surface toxic combinations, preventing them from contaminating AI training data.
- Data privacy regulations, security frameworks and AI ethics guidelines are constantly evolving, so organizations need to stay ahead by automatically applying policies based on data type and regulation, assessing their data against the latest regulatory and ethical standards, and mitigating compliance risks.