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To enhance your data management strategy, the Dryv platform’s A.I. and machine learning modules were architected from the ground-up to classify content at both scale and enhanced accuracy.
Its pre-trained A.I. module dramatically reduces effort to rapidly produce accurate results for PII, CCPA and other industry compliance mandates.
Select from an existing policy template library of recommended rules for your use case or customize from a variety of options. Rules are based on the data in the file and analyzed by advanced pattern matching and various A.I. modules for classification:
- Advanced Pattern Matching
- PII Identification and Extraction
- Document Type Classifier
- Standardized Form Matcher
- Language Detection
Reduce false positives, improve accuracy, and discover common sensitive values
- Pre-defined common regular expressions from an ever-growing library
- Validation routines and keyword proximity
- Social security numbers, driver’s license, bank numbers, credit cards, financial account ID’s, government document ID’s
Identify and extract personal data from content via deep artificial intelligence
- Names, ages, addresses, dates of birth, phone numbers, social security numbers
- Identify PII entities from structured forms—as well as unstructured text using deep learning technology
- Take action based on PII type, such as “label all documents with addresses as sensitive”
Identify and categorize documents—as well as federal and state government forms
- Identify files across 100 classification categories and 5,000 Federal & State Government forms
- Identify forms by name and take action if a form has been filled in with “sensitive” information
- Resumes, Bank Statements, Invoices, Contracts, Statements of Work, Tax Forms, Court Documents, Brochures, White Papers, Catalogs, Design Files, HR Files, Letters, Reports, Press Releases
- IRS forms: W9, 1040, 1120, 1095-A, etc.
- FDA documents: 0356h, 3911, 3926, etc.
Identify and group documents by language
- Detect and identify an item's written language from over 175 supported options
- Use the item language in criteria for matching rules or to optimize additional artificial intelligence processing
- Use confidence scores to identify ambiguous cases and non-natural languages [code, machine generated files, etc.]