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Accuracy

Up to 99.999% detection accuracy across supported entity types, combining AI model recognition with pattern-based rules.

Pricing

Starting at $0.10 per million tokens processed. Only detected and transformed tokens count toward usage.
DataFramer’s detection and anonymization feature scans your datasets for sensitive information and redacts it:
  • Detection — identify sensitive entities across your data, combining AI model recognition with pattern-based rules
  • Anonymization (redaction) — replace detected entities with mask tokens to remove sensitive information
Detection covers seven categories of sensitive information:
  • Personal — First name, last name, date of birth, dates, age, gender, nationality, race/ethnicity, marital status
  • Contact — Email, phone number, street address, postal/ZIP code, city, state, country
  • Financial — SSN, credit/debit card, bank routing number, routing number, tax ID, IBAN
  • Digital — IP address, URL, username, password, MAC address, device identifier
  • Identity Documents — Passport number, license/certificate number, national ID, voter ID
  • Medical / PHI — Medical record number, diagnosis, medication, health plan number, patient ID, lab result
  • Professional — Company name, occupation, employee ID, salary

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Creating a job

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Step 1: Select dataset

Choose a seed dataset from your library as the input for the job. Step 1 – Select a dataset for anonymization

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Step 2: Detection configuration

Configure how sensitive entities are detected and which model evaluates the results. Step 2 – Detection configuration: choose detection method, confidence threshold, and evaluation judge

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Detection methods

Delegates all detection to an LLM. The most flexible option for unusual or domain-specific entity types.
Combines PII-M1, LLM, and Heuristics in a union. Best for maximum coverage when false negatives are unacceptable.

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Confidence threshold

The confidence threshold controls the trade-off between recall and precision. Lower values (e.g., 0.1) produce more detections with more potential false positives. Higher values (e.g., 0.9) produce fewer detections but with higher certainty. The default of 0.30 works well for most datasets.

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Evaluation judge model

After the job completes, an LLM evaluates the quality of the results. Select the model to use for this evaluation.

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Step 3: Entity types & masks

Select which entity types to detect and configure the mask token each one is replaced with in the output. Step 3 – Select sensitive entity types and configure mask tokens The full set of supported entity types is organized by category:
First Name, Last Name, Date of Birth, Date, Age, Gender, Nationality, Race / Ethnicity, Marital Status
Email, Phone Number, Street Address, Postal / ZIP Code, City, State, Country
Social Security Number, Credit / Debit Card, Bank Routing Number, Routing Number, Tax ID, IBAN
IP Address, URL, Username, Password, MAC Address, Device Identifier
Passport Number, License / Certificate Number, National ID, Voter ID
Medical Record Number, Diagnosis, Medication, Health Plan Number, Patient ID, Lab Result
Company Name, Occupation, Employee ID, Salary
Each selected type maps to a mask token in the output—for example, first_name → <FIRST NAME> or date_of_birth → <DOB>. You can customize the mask token for each type.

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Step 4: Review & submit

Review your full configuration before submitting. The summary shows your full configuration—dataset, detection method, threshold, evaluation model, and all selected entity types with their mask tokens. Step 4 – Review and submit the anonymization job After submission, the job runs in the background. You can monitor progress on the job detail page.