BigID Introduces Data Labeling for AI to Enhance Data Governance
BigID has launched a new feature called Data Labeling for AI, designed to help organizations classify and control data usage in AI models, announced in a press release. This capability allows security and governance teams to apply usage-based labels to data, ensuring that only appropriate data is used in generative AI models, copilots, and agentic AI systems.
The Data Labeling for AI feature offers a scalable, policy-driven approach to data classification, enabling organizations to use predefined labels such as "AI-approved," "restricted," or "prohibited," or to create custom labels that align with internal risk frameworks and regulatory requirements. This helps prevent sensitive or high-risk data from entering large language models (LLMs) and other AI workflows.
Supporting both structured and unstructured data across cloud, SaaS, and collaboration environments, the feature enforces usage policies early in the data pipeline. It combines deep classification, policy enforcement, and remediation workflows to provide actionable insights and control over data usage in AI systems.
We hope you enjoyed this article
Consider subscribing to one of our newsletters like AI Policy Brief, Enterprise AI Brief or Daily AI Brief.
Also, consider following us on social media:
More from AI Safety
Oct 16 Nvidia CEO says chip supply could stop over unsafe AI Oct 15 Anthropic Gives METR Employee Level Access for Safety Reviews Oct 15 Bill Gates Prepares Major AI Safety Push Oct 15 Singapore Launches Three AI Safety Initiatives Oct 15 Anthropic Tests How AI Worms Could Spread Across Smart DevicesMore from Enterprise
Oct 16 BOC Group Adds AI Assistant to ADOIT 21 Oct 16 Huawei Brings Upgraded Stellar Network Solution to Europe Oct 16 Element to Manage Waymo's Toronto Fleet for 2028 Launch Oct 16 CTM and Huawei Plan AI Network Security Upgrades in Macao Oct 16 Huawei and 14 partners form Global AI CITY allianceAI Policy Brief
Weekly report on AI regulations, safety standards, government policies, and compliance requirements worldwide.
Whitepaper
Tensordyne Napier: What If One Rack Could Do the Work of Nine?
Tensordyne
This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.
Read moreYou may also like
Armor Founder Calls for Governed Enterprise AI at GovWare
YouTube Music Adds Labels for AI Generated Songs
Gurucul Launches AI Risk and Response Security Tool
Gen Digital launches AI Data Checker beta
Everpure Adds AI Data Management and Inference Tools
Daily AI Brief: the AI news that matters, in your inbox.