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BigID Introduces Data Labeling for AI: A Solution to Manage Data Risks

In a digital landscape increasingly dominated by artificial intelligence, ensuring data security and compliance has never been more critical. BigID, a frontrunner in data security, privacy, compliance, and AI governance, has unveiled a groundbreaking capability—Data Labeling for AI. This innovative feature empowers organizations to classify and regulate data usage in generative AI models, copilots, and agentic AI systems.

Understanding Data Labeling for AI

Data Labeling for AI is designed to assist organizations in answering the pressing question: "Is this data suitable for AI?" With this new tool, security and governance teams can apply usage-based labels—ranging from AI-approved to restricted and prohibited—tailored to meet internal risk frameworks and regulatory standards. By providing a scalable solution, BigID allows organizations to tag data effectively, significantly reducing the chances of data misuse, leakage, or policy violations.

Key Features of the New Capability

The Data Labeling for AI solution comes with a multitude of features that enhance data governance:

• - Automated Data Labeling: Organizations can automatically label data as safe, restricted, or prohibited based on predefined criteria.
• - Custom Labeling Sets: Users have the flexibility to create custom labels that align with their specific internal policies and regulatory requirements.
• - Risk Prevention: The capability helps prevent sensitive or high-risk data from being utilized in large language models (LLMs), copilots, and Rapid Application Development (RAD) workflows.
• - Comprehensive Coverage: It supports both structured and unstructured data sources across various environments, including cloud services, SaaS, and collaboration platforms.

How It Works

Data Labeling for AI is designed to enforce usage policies early in the data pipeline, ensuring that data inaccuracies or security concerns are addressed before reaching AI models. This solution combines advanced classification techniques, policy enforcement, and workflow remediation, transforming visibility into actionable insights.

Dimitri Sirota, CEO and Co-Founder of BigID, emphasized the significance of this solution, stating, Security teams need a way to control what data gets used in AI before it becomes a problem. 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.

Why Data Labeling is Critical for AI Governance

As organizations increasingly rely on AI, the ability to securely manage and govern data becomes essential. Effective data governance reduces risks associated with AI system deployments, ensuring compliance with evolving regulations. The introduction of this capability signifies BigID's commitment to enabling organizations to better manage their data landscape, mitigating risks across their data environments.

By utilizing BigID's Data Labeling for AI, businesses can significantly enhance their security posture while navigating the complexities of modern data governance frameworks. The integration of comprehensive classification and governance measures positions organizations to harness the power of AI responsibly and securely.

Conclusion

BigID’s innovative approach to data labeling not only simplifies the daunting task of ensuring AI compliance but also promotes better risk management. In a world where data misuse can lead to serious repercussions, equipping organizations with the tools to manage their data effectively is vital. As BigID continues to lead in data security and governance, the introduction of Data Labeling for AI represents a significant step forward in making AI safer and more compliant across various industries.

To learn more about how BigID can assist your organization in navigating today’s data challenges, visit BigID.com/blog.