BigID Launches Data Labeling for AI to Enforce Usage Policies and Reduce AI Risk
BigID, the leading platform for data security, privacy, compliance, and AI governance, announced Data Labeling for AI, a new capability that helps organizations classify and control which data can be used in generative AI models, copilots, and agentic AI systems. Security and governance teams can now apply usage-based labels to guide how data flows into AI – reducing the risk of data misuse, leakage, or policy violations.
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Security and governance teams are under pressure to answer one critical question: “Is this data appropriate for AI?” BigID’s Data Labeling for AI provides a scalable, policy-driven way to classify and tag data for AI use. Organizations can apply out-of-the-box labels like “AI-approved,” “restricted,” or “prohibited,” or create custom labels aligned to internal risk frameworks and regulatory requirements.
With support for structured and unstructured data across cloud, SaaS, and collaboration environments, Data Labeling for AI helps enforce usage policies early in the pipeline – before data reaches AI models. It combines deep classification, policy enforcement, and remediation workflows to turn visibility into action.
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Key Takeaways
- Automatically label data as safe, restricted, or prohibited for AI use
- Customize label sets to align with internal policies and regulatory needs
- Prevent sensitive or high-risk data from entering LLMs, copilots, and RAG workflows
- Apply usage-based labeling across both structured and unstructured data sources
“Security teams need a way to control what data gets used in AI before it becomes a problem,” said Dimitri Sirota, CEO and Co-Founder at BigID. “With Safe-for-AI Labeling, organizations can apply the right labels, enforce the right policies, and take the right actions to keep their data – and their AI – under control.”
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