Know What's in Your Data Before It Trains Your AI.
Once PII enters your training pipeline, it's baked into the model. Scan datasets before fine-tuning, RAG ingestion, or ML training. Don't let sensitive data become an AI governance nightmare.
Scan before you train.
What's Really in That Training Dataset?
You're feeding data into an LLM, fine-tuning a model, or building a RAG pipeline. But do you know exactly what's in those files? One overlooked SSN or medical record could mean your model memorizes — and regurgitates — PII.
Training Datasets
Large datasets scraped, purchased, or aggregated from internal sources — often containing PII no one realized was there.
Fine-Tuning Data
Customer interactions, support tickets, and internal documents used to customize models — packed with real names, accounts, and PII.
RAG Document Stores
Knowledge bases, wikis, and document repositories indexed for retrieval — where sensitive contracts and HR files can hide.
AI Governance Is Now a Board-Level Priority
"By 2026, AI governance will be the top security priority. As AI tools fragment and remix information, sensitive data flows further and faster than ever before." — Industry Analysts
Once PII is in the model, it's nearly impossible to remove. Scan before you train.
How Risk Finder Protects Your AI Pipeline
Scan datasets before they enter your training pipeline. Know exactly what sensitive data exists so you can remove it before it becomes part of your model.
1. Scan Before Training
Run the scanner on any dataset before it enters your ML pipeline. 150+ classifiers catch SSNs, credit cards, medical records, and more — before they're baked into your model.
2. Audit RAG Sources
Scan document stores, knowledge bases, and file repositories before indexing them for retrieval. Know exactly what PII your RAG system might surface in responses.
3. Document Compliance
Generate reports proving you scanned training data for PII before use. Create an audit trail for AI governance reviews and regulatory inquiries.
Common AI Data Scenarios
LLM Fine-Tuning
- Scan customer service transcripts before fine-tuning
- Catch PII in support tickets and chat logs
- Verify "anonymized" datasets are actually clean
- Document what was scanned for compliance records
RAG & Knowledge Bases
- Audit document stores before RAG indexing
- Find PII hiding in PDFs, contracts, and HR docs
- Prevent sensitive data from surfacing in AI responses
- Scan Confluence, SharePoint, and internal wikis
ML Training Datasets
- Scan datasets before ML model training
- Catch hidden PII in CSVs and data exports
- Verify third-party datasets don't contain PII
- Create audit trail for model governance
AI Compliance & Governance
- Prove GDPR compliance for AI training data
- Document due diligence for AI governance reviews
- Generate reports for regulatory inquiries
- Evidence you looked before you trained
What We Catch Before It Enters Your Model
Personal Identifiers
SSNs, driver's licenses, passport numbers hiding in training data and chat logs.
Financial Data
Credit cards, bank accounts, and tax forms in customer interaction datasets.
Health Information
PHI, medical codes, and health records that would trigger HIPAA violations if memorized.
150+ classifiers scan for sensitive data across all common file types — so you know exactly what's entering your AI pipeline.
Why Pre-Training Scans Matter
Models Memorize PII
LLMs can memorize and regurgitate training data — including SSNs, credit cards, and medical records. Once it's in, it's nearly impossible to remove.
Regulators Are Watching
GDPR, CCPA, and emerging AI-specific regulations require you to demonstrate due diligence over what data enters AI systems. "We didn't know" isn't a defense.
Prove You Looked
Generate compliance reports showing you scanned training data before use. Create an audit trail that demonstrates responsible AI development practices.
Ready to Train AI Responsibly?
Know exactly what's in your data before it becomes part of your model. Start scanning today.
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