Unstructured Data Protection

Discover & Secure Sensitive Data.
Anywhere it lives.

Protegrity enables protection of sensitive data in unstructured file formats through discovery and file-level safeguards—in documents, text-based files, and other supported unstructured formats. By applying policy-driven discovery and protection controls to unstructured file content, Protegrity ensures comprehensive data security, supporting compliance for data used in AI/ML pipelines where sensitive content may be present.
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What You Need
To Know

What It Is

Unstructured data protection involves identifying, classifying, and applying security measures to sensitive information residing within non-tabular formats such as documents, emails, and free-text fields—where supported.

When to Use It

Unstructured data protection is necessary when you need to secure sensitive PII, PHI, PCI, or IP that’s hiding within natural language text, transcripts, documents, chatbot logs, and other challenging formats used in AI/ML and analytics.

Why It Matters

Protecting unstructured data is essential for privacy compliance, AI safety, and holistic risk management—especially as files, transcripts, logs, and unstructured formats grow across enterprises. Traditional security tools typically miss sensitive information hidden within these common and rapidly growing data types.

The Protegrity Advantage

Our Unique Approach to Unstructured Data Protection

Protegrity’s comprehensive platform extends robust data protection to unstructured data, ensuring sensitive information is secured across its lifecycle—no matter its format or location.
01
ML-Powered Discovery
Rapidly and reliably identify and classify sensitive data (PII, PHI, PCI) across structured and unstructured sources using advanced ML and rules-based classification.
02
Unstructured Input Support
Purpose-built to discover sensitive data within natural language text, transcripts, documents, chatbots, and other challenging formats often missed by traditional DLP tools.
03
Flexible Protection Methods
Apply robust, field-level protection methods like tokenization, encryption, or masking via flexible enforcement points to secure data while keeping it usable across diverse environments.
04
Integrated Governance
Feed discovery results into Protegrity Governance to inform policy creation, refine protection rules, and validate security posture for unstructured data.
05
Cloud & Legacy Coverage
Secure unstructured data across diverse environments—from on-prem file repositories to cloud object storage (S3, Blob, GCS) and data lakes, where supported.

    How Unstructured Data Protection Works

    Discovery & Classification
    Advanced PII classification combines ML language models (RoBERTa) with a rules-based engine (Presidio) to accurately identify sensitive data within unstructured text.
    Targeted Protection
    Once identified, sensitive fields or elements within unstructured data can be tokenized, encrypted, or masked based on policy—even within free-text fields.
    API Access & Embeddability
    Easily integrate Protegrity Discovery into any application, script, or workflow using a lightweight REST API and Python SDK, enabling PII detection directly within custom application logic.
    Containerized Deployment
    Deploy Protegrity Discovery within your own infrastructure using Docker containers or leverage managed Kubernetes support for cloud-native scalability.

      Why Unstructured Data
      matters In Your Protection Strategy

      Protecting unstructured data is vital for minimizing risk, achieving comprehensive compliance, and unlocking value from vast datasets without compromising privacy.

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      Comprehensive Risk Reduction

      Go beyond structured databases to reliably find and protect sensitive data hidden within free-text fields, documents, and supported unstructured formats.

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      Enable secure AI/GenAI

      Address requirements for structured and unstructured data under regulations like GDPR, HIPAA, and PCI DSS.

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      Broad compliance coverage

      Address requirements for structured and unstructured data under regulations like GDPR, HIPAA, and PCI DSS.

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      Maximize data value

      Securely unlock the insights from rich information streams like call center transcripts or medical notes, allowing them to feed analytics and business intelligence applications.

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      Consistent security posture

      Apply a unified data protection strategy across structured and supported unstructured data environments, ensuring consistent security regardless of where the data resides or how it’s consumed.

      When Should You Use Unstructured Data Protection?

      Unstructured data protection is essential in any scenario involving the collection, processing, and/or storage of sensitive information outside of traditional databases.
      01
      GenAI RAG Pre-Processing
      Scan and scrub sensitive information from documents before vectorization to prevent PII leakage into Retrieval-Augmented Generation (RAG) pipelines and LLM prompts.
      02
      Chatbot Redaction
      Automatically redact sensitive customer inputs (PII, PHI, etc.) within chatbot conversations in real time to ensure privacy and compliance.
      03
      Transcription Cleanup
      Automatically remove sensitive information from call center transcripts or medical notes before storage, processing, or analysis.
      04
      File-Based Data Lakes
      Tokenize sensitive data stored in on-prem file repositories or file-based data lakes before ingestion into cloud analytics environments.
      05
      Secure File Transfers & Exports
      Apply tokenization or masking to sensitive data within files (CSV, JSON, etc.) used for reporting, exports, or sharing with external partners.
      06
      App-Embedded Classification
      Seamlessly integrate PII detection directly into app workflows via API/SDK to classify and protect data during ingestion, processing, or storage.
        Choosing the Right Protection Method

        How Unstructured Data Protection COMPARES TO OTHER METHODS 

        Protecting unstructured data often requires a combination of appropriate protection methods — and their application to unstructured data demands specialized capabilities.

        Explore how unstructured data protection integrates with and complements other methods—and when each is the right fit.

        Unstructured Data Protection

        Vaultless Tokenization

        Data Anonymization

        Dynamic Data Masking

        Format-Preserving Encryption

        Data Pseudonymization

        The Protegrity Data Protection Platform

        Explore Data-Centric Data Protection

        The Protegrity Platform delivers comprehensive governance and field-level data protection within a modular framework that fits your data environment, enabling a fit-for-purpose approach to data security and privacy. 

        Discovery

        Identify sensitive data (PII, PHI, PCI, IP) across structured and unstructured sources using ML and rule-based classification.

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        Governance

        Define and manage access and protection policies based on role, region, or data type—centrally enforced and audited across systems.

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        Protection

        Apply field-level protection methods—like tokenization, encryption, or masking—through enforcement points such as native integrations, proxies, or SDKs.

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        Privacy

        Support analytics and AI by removing or transforming identifiers using anonymization, pseudonymization, or synthetic data generation—balancing privacy with utility.

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        Frequently Asked Question

        Take the next step

        See how Protegrity’s fine grain data protection solutions can enable your data security, compliance, sharing, and analytics.

        Get an online or custom live demo.

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