En Adopción de IA surges, organizations are turning to vector databases like MongoDB Atlas Vector Search y Elasticsearch to power intelligent search and retrieval for AI and machine learning models. But as the data behind AI gets more complex, so do the risks.
BigID is setting a new standard for Gobernanza de datos de IA con el industry’s first capability to classify and label sensitive data inside vector databases — delivering unmatched visibility, precision, and control over the data powering AI.
Why It Matters: Securing the AI Stack Starts with the Data
Vector databases are fast becoming a core pillar of AI infrastructure — yet they’ve remained a blind spot for most data security and governance teams. These databases store high-dimensional embeddings derived from rich datasets like customer conversations, proprietary IP, or internal documents. If left unlabeled and unprotected, this data can easily be misused, overexposed, or mishandled by AI models.
With BigID, enterprises can now bring their existing data governance policies and classification accuracy into this new domain — automatically labeling and tagging sensitive content, enforcing access policies, and aligning usage with regulatory and ethical standards.
What’s New: Industry-First Labeling for Vector Databases
BigID’s vector database labeling capability enables organizations to:
- Automatically Label Sensitive Data: Continuously detect and tag personal, regulated, and proprietary data within MongoDB Atlas Vector Search and Elasticsearch.
- Enforce AI Access Controls: Prevent unauthorized access and reduce exposure by enforcing policy-based protections on sensitive vector data.
- Support Regulatory Compliance: Align with frameworks like GDPR, CCPA, y el Ley de AI de la UE by bringing transparency and accountability to AI inputs.
- Strengthen AI Integrity: Reduce risk, bias, and error by improving the data quality and governance of model inputs.
“As organizations embrace AI, they need fine-grained visibility and control over the data fueling their models. By extending our labeling capabilities to vector databases, we empower businesses to mitigate risk, enforce AI governance, and drive responsible AI adoption. -Dimitri Sirota, CEO of BigID
Business Impact: Enabling Responsible AI at Scale
BigID’s vector DB labeling isn’t just a technical advancement — it’s a business enabler. By bringing visibility and control to AI’s most critical data layer, organizations can:
- Accelerate Responsible AI: Govern sensitive training data to reduce bias, improve model accuracy, and avoid reputational risk.
- Mitigate AI Risk Exposure: Prevent misuse or overexposure of proprietary and personal data in AI systems.
- Improve AI Auditability: Make AI usage more transparent, traceable, and compliant with internal and external standards.
- Reduce Operational Overhead: Automate sensitive data labeling and policy enforcement to scale AI data governance without scaling headcount.
The BigID Difference: First & Only DSPM for Vector DBs
This innovation cements BigID’s leadership as the first and only DSPM provider to extend advanced data labeling into vector databases — and complements a broader set of capabilities to secure all enterprise data types:
- Structured, Unstructured, and Vector: One platform to classify and protect all your data.
- Unified Policies and Controls: Apply consistent governance across storage types, locations, and access paths.
- Modular & Scalable: Extend governance without disruption to your existing AI workflows and infrastructure.
Whether you’re training large language models, powering intelligent search, or enriching customer experiences with AI, BigID gives you the visibility and control to do it responsibly.
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