AWS Marketplace
Azure Marketplace
Google Cloud MarketplaceLabels the model can learn around
Replace names and contact details with labels such as [PERSON] and [EMAIL], so the text keeps its shape for training while the personal data is gone.
AI training and fine-tuning data
PII Redaction for AI Training Data
A model can repeat what it was trained on, and personal data cannot be trained back out. Philterd de-identifies the corpus in your own cloud before training, with a person reviewing the uncertain cases and the result measured against your own data.
For teams preparing training data
For teams that want it done with them
What Philterd does with a training corpus
A person reviews the uncertain cases
In Philter Workspace, reviewers accept or override the detections Philter is less sure of, so the hard cases get a human decision.
Measured, not assumed
Philter Scope scores a policy’s precision and recall against records your reviewers have marked, so you know how well it works on your data before you train.
Which fits your team
Redaction at corpus scale
Philter
Redacts text at corpus scale through an API in the same cloud account as your training data, so the raw corpus does not move.
About Philter →Human review
Philter Workspace
Reviewers accept or override proposed redactions, and every decision is recorded.
About Philter Workspace →Measurement
Philter Scope
Scores a redaction policy on precision and recall against gold-standard data, so a policy change can be measured.
About Philter Scope →A team that wants help
AI Training Data
We de-identify the corpus with you and hand back a precision and recall report scored per entity type.
See the engagement →Getting started
- Deploy Philter from your cloud’s marketplace into the same account as your training data.
- Start from the LLM training data preparation policy in the policy library, and add patterns for identifiers specific to your data.
- Build a gold standard. Have reviewers mark a sample of your corpus, then score the policy against it with Philter Scope and adjust it until the results meet your bar.
- Run the corpus, sending uncertain detections to Philter Workspace for review before the data is used for training.
Ready-to-use redaction policies
Free, ready-to-use policies from the open source policy library. Download and load into your Philter instance.
AI Training
v2.0.0
LLM Training Data Preparation
Aggressive PII redaction for documents being fed into LLM training, fine-tuning, or RAG vector stores — preserves semantic structure with type tokens.
Healthcare
v2.0.0
Clinical Notes De-Identification (Date-Shifted)
De-identify clinical notes for research, ML training, or analytics — preserving temporal relationships via per-patient date shifting.
General
v2.0.0
General-Purpose Starter Policy
A balanced starting policy covering common PII types — names, contact info, government IDs, payment data — with no vertical-specific tuning.
Recent writing on AI training data
The Clean Room for AI Training Data: Redact, Review, Measure
Once PII is baked into model weights it is extractable for the model's life. The clean room pattern: Philter redacts, Arbiter reviews, Philter Scope measures.
Can an LLM Leak Its Training Data, and Why You Cannot Un-Train PII
Research shows LLMs memorize and leak training data, and unlearning it afterward is unreliable. The dependable control is to redact before training.
The Ethics of Training: Why We Use Synthetic Data
A privacy tool should never be trained on the data it protects. Why Philterd's models are built entirely on synthetic data, and what that means for compliance.