AI models are only as good as the data that trains them. Most pipelines are messy, incomplete, or noncompliant — putting accuracy, privacy, and safety at risk. BigID helps organizations build secure AI data pipelines by:
Classifying structured and unstructured data (including code, chat, and logs) by sensitivity
Categorizing datasets with business taxonomies for better context
Cataloging with a unified, searchable metadata index
Curating training datasets with semantic search for relevance and quality
Cleansing and redacting sensitive or toxic data before training
Compliance-checking datasets against global regulations and internal policies
Controlling staged data pipelines with policy guardrails and governance
The 7 Cs of clean, compliant, and controlled AI pipelines.
Automatically scan structured and unstructured data — from databases and data lakes to chat logs, code repositories, and files — and tag by sensitivity and type.
Go beyond samples to scan petabytes at scale
Detect PII, PHI, financial data, and more
Apply business taxonomies and labels for context so AI knows quoi the data is and comment it should be used.
Align datasets with internal policies and business rules
Standardize naming conventions across environments
Build a searchable metadata index that makes all AI-ready datasets visible and accessible.
Centralize metadata across structured + unstructured sources
Eliminate duplication and blind spots
Use semantic search and similarity clustering to assemble the right datasets for training and testing AI models.
Identify related or similar documents for richer training sets
Remove irrelevant or low-value data automatically
Redact sensitive data before it ever reaches AI models.
Protect personal, regulated, or toxic data at ingestion
Standardize data quality to improve model accuracy
Validate datasets against regulatory frameworks and internal governance policies.
Ensure training data aligns with GDPR, CPRA, EU AI Act, NIST AI RMF, and more
Automate policy enforcement on pipeline inputs
Enforce guardrails on staged AI training data pipelines to reduce risk and improve reliability.
Block unapproved datasets from entering the pipeline
Monitor and govern data usage throughout the lifecycle
Operate across petabytes of enterprise data, not just limited samples.
Continuous scanning with low-latency impact
Support for multi-cloud, SaaS, and on-prem data
Manage every step of the pipeline in one platform: discovery, classification, cleansing, compliance, and control.
Consolidate point tools into a single AI data pipeline solution
Provide one source of truth for AI data governance
Train AI on trusted data — and keep accuracy, compliance, and control.
