Data Classification Types: Context-Aware Detection for AI Workflows

Technical Documentation

Table of Contents

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Overview

Wald provides context-aware data classification for AI assistants.

Traditional Data Loss Prevention (DLP) systems rely on pattern matching techniques such as regular expressions to identify sensitive information. While effective for structured identifiers like credit card numbers or Social Security numbers, these systems:

  • Generate high false positives
  • Miss business-critical confidential data expressed in natural language

Wald takes a different approach.

Wald’s classification engine uses semantic understanding models to identify sensitive data based on meaning and context, not just patterns.

This enables:

  • Detection of sensitive information beyond structured formats
  • Significant reduction in false positives

Key capabilities include:

  • Context-aware sensitive data detection
  • Multi-label classification of data types
  • Fine-grained taxonomy of sensitive data categories
  • Integration with enterprise DLP and governance framework.
Traditional Data Classification Types Wald’s Context-Aware Classification
Pattern matching (regex) Semantic understanding
High false positives Dramatically reduced false positives
Misses natural language data Detects confidential information in context
Single-label detection Multi-label classification
Requires constant rule updates Adapts to meaning and context

Identity Data

Information that directly identifies an individual.

Name Data

Names or aliases that identify a specific individual.

Examples:

  • Jane Doe
  • Dr. Michael Patel
  • Anya Sharma

Context:

  • Please send the contract to Dr. Michael Patel.

National ID Data

Government-issued identification numbers tied to individuals.

Examples:

  • US Social Security Number
  • UK National Insurance Number

Context:

  • My Social Security number is required for verification.

Passport Data

Identifiers associated with passports.

Examples:

  • Passport number
  • Issuing country
  • Expiration date

Context:

  • Passport number 123456789 expires in 2029.

Driver’s License Data

Government-issued driver license identifiers.

Examples:

  • License number
  • License class
  • Issue date
  • Expiration date

Context:

  • License number F1234567 needs renewal.

Phone Number Data

Telephone numbers including mobile and landline.

Examples:

  • +1 (555) 123-4567

Financial Data

Financial identifiers and records associated with accounts or transactions.

Credit Card Data

Payment card information.

Examples:

  • 4111 1111 1111 1111
  • CVV
  • Expiry date

Context:

  • My credit card number is 4111 1111 1111 1111.

Bank Data

Bank account identifiers.

Examples:

  • Account numbers
  • Routing numbers
  • SWIFT codes

Context:

  • Account number 123456789

IBAN Data

International bank account identifiers.

Examples:

  • GB29 NWBK 6016 1331 9268 19
  • DE89 3704 0044 0532 0130 00

Financial Records

Sensitive financial information.

Examples:

  • Salaries
  • Portfolio values
  • Financial reports

Context:

  • The executive compensation package is $450,000 annually.

Healthcare Data

Health-related information tied to individuals.

Medical Data

Medical conditions, treatments, or clinical terms.

Examples:

  • Type 2 Diabetes
  • Appendectomy
  • Lisinopril

Health Identifiers

Identifiers used in healthcare systems.

Examples:

  • Medical Record Number (MRN)
  • Patient ID

Context:

  • Patient MRN 123456789

Insurance Data

Insurance-related identifiers.

Examples:

  • Policy number
  • Group ID
  • Claims history

Corporate Data

Information related to organizations and internal operations.

Company Data

Identifies organizations or internal initiatives.

Examples:

  • Verizon Communications
  • The Mayo Clinic
  • Project Atlas

Context:

  • Draft an email announcing Project Phoenix.

Product Data

Details about products or services.

Examples:

  • Product roadmaps
  • Feature specifications
  • Release timelines

Context:

  • Next-generation EV model roadmap.

Intellectual Property Data

Confidential or proprietary knowledge.

Examples:

  • Unpublished research
  • Internal datasets
  • Research notes

Employment Data

Information related to roles, performance, or workforce activity.

Examples:

  • Performance reviews
  • Resume content
  • Internal role changes

Technical and Security Data

Identifiers used in systems, infrastructure, and security.

API Credentials

Identifiers used for integrations.

Examples:

  • AWS Access Key ID
  • OAuth client ID
  • Google Cloud Client ID

Secret Keys

Private credentials for authentication or encryption.

Examples:

  • AWS Secret Access Key
  • SSH private key
  • JWT signing secret

Hardware Identifiers

Device-level identifiers.

Examples:

  • MAC address
  • IMEI
  • Serial number

Internet Identifiers

Online communication identifiers.

Examples:

  • IP addresses
  • Email addresses
  • Domain names
  • Usernames

Personal Attributes

Characteristics describing an individual.

Demographic and Identity Attributes

Examples:

  • Gender
  • Ethnicity
  • Nationality
  • Political affiliation
  • Sexual orientation

Context:

  • She was born in Mexico.

Behavioral and Activity Data

Information generated from user actions and interactions.

Behavioral Data

Examples:

  • Browsing history
  • Clickstream data
  • Search history

Geolocation Data

Precise location information.

Examples:

  • 40.7128° N, 74.0060° W
  • GPS location history

Contextual Detection Differentiators

Wald’s core advantage lies in contextual interpretation.

Date Disambiguation

  • My card will expire on 01/25 → Credit Card Data
  • My license is expiring on 01/28 → Driver’s License Data

Ambiguous Identifiers

Same number, different meanings:

  • Account number 123456789 → Bank Data
  • SSN 123-45-6789 → National ID Data
  • Passport No. 123456789 → Passport Data
  • Patient MRN 123456789 → Healthcare Data
  • Order number 123456789 → Non-sensitive transactional data

Credit Card vs Order Number

  • 378282246310005 (with payment context) → Credit Card Data
  • Order number 378282246310005 → Non-sensitive data

Wald Data Classification Taxonomy

Wald uses a hierarchical taxonomy aligned with major compliance and security frameworks.

It extends traditional classification systems to support:

  • Company data
  • Product data
  • Financial transactions
  • HR interactions
  • Other unstructured enterprise data

This reduces false negatives that pattern-based systems typically miss.

Top-level categories:

  • Identity Data
  • Financial Data
  • Healthcare Data
  • Corporate Data
  • Technical and Security Data
  • Personal Attributes
  • Behavioral and Activity Data

Why This Matters for CISOs

Reduced False Positives

  • Fewer blocked workflows
  • Better user adoption
  • Lower operational overhead

Higher Detection Accuracy

Wald detects sensitive data in:

  • Natural language
  • Unstructured prompts
  • Mixed-content inputs

Better Policy Control

  • Fine-grained classification
  • Role-based enforcement
  • Audit-ready governance

Contextual Disambiguation

The same value can represent different data types depending on context.

Example: 123456789

  • Checking account number → Bank Data
  • SSN → National ID Data
  • Patient MRN → Healthcare Data
  • Order number → Non-sensitive data

Reduction of False Positives

Pattern-based systems often misclassify.

Example:

  • 378282246310005 → flagged as credit card by regex

But in context:

  • Your order number is 378282246310005 → correctly classified as non-sensitive

This significantly improves real-world usability.

Integration with Enterprise Data Governance

Wald integrates with existing classification frameworks such as:

  • Microsoft Purview
  • PAN-based taxonomies
  • Internal enterprise classification standards

Organizations can define custom policies using Wald’s classification outputs.

Summary

Wald provides a context-driven approach to sensitive data classification built for AI workflows.

It enables:

  • Higher detection accuracy
  • Lower false positives
  • Richer classification signals
  • Stronger AI governance controls

Frequently Asked Questions: Data Classification Types

Q1. What are data classification types?

Data classification types are categories used to identify and organize sensitive information based on its nature and risk level. Traditional systems rely on pattern matching to detect structured data like credit card numbers or Social Security numbers. Wald takes a different approach with context-aware data classification that understands meaning, not just patterns.

Wald’s classification system covers seven major categories:

  • Identity Data (names, national IDs, passports, driver’s licenses)
  • Financial Data (credit cards, bank accounts, IBANs, financial records)
  • Healthcare Data (medical conditions, health identifiers, insurance information)
  • Corporate Data (company information, product details, intellectual property)
  • Technical and Security Data (API credentials, secret keys, hardware identifiers)
  • Personal Attributes (demographic information, identity characteristics)
  • Behavioral and Activity Data (browsing history, geolocation, user actions)

Q2. How does Wald’s context-aware classification differ from traditional DLP data types?

Traditional DLP systems use pattern matching techniques like regular expressions. They generate high false positives and miss business-critical confidential data expressed in natural language.

Wald’s classification engine uses semantic understanding models to identify sensitive data based on meaning and context. This creates a fundamental difference in detection accuracy.

Traditional DLP vs. Wald’s Context-Aware Classification:

  • Pattern matching (regex) vs. Semantic understanding
  • High false positives vs. Dramatically reduced false positives
  • Misses natural language data vs. Detects confidential information in context
  • Single-label detection vs. Multi-label classification
  • Requires constant rule updates vs. Adapts to meaning and context

Q3. How does context-aware classification reduce false positives?

Wald interprets the meaning behind the data, not just the format. Pattern-based systems often misclassify innocent information as sensitive.

Example of false positive reduction:

Traditional DLP flags 378282246310005 as a credit card number every time it appears.

Wald understands context:

  • “My credit card number is 378282246310005” → Credit Card Data (sensitive)
  • “Your order number is 378282246310005” → Non-sensitive data (safe)

This contextual interpretation significantly improves real-world usability. Your teams face fewer blocked workflows, better user adoption, and lower operational overhead.

Q4. Can data classification types identify the same number differently based on context?

Yes. The same value can represent different data types depending on context. This is where Wald’s semantic understanding creates massive advantages over traditional systems.

Example: The number 123456789

Wald correctly classifies based on surrounding context:

  • “Checking account number 123456789” → Bank Data
  • “My SSN is 123-45-6789” → National ID Data
  • “Patient MRN 123456789” → Healthcare Data
  • “Order number 123456789” → Non-sensitive data

Date disambiguation example:

  • “My card will expire on 01/25” → Credit Card Data
  • “My license is expiring on 01/28” → Driver’s License Data

Traditional pattern-matching systems cannot make these distinctions. They flag everything or miss critical exposures.

Q5. What types of sensitive data do traditional DLP systems miss?

Traditional DLP systems excel at detecting structured identifiers but fail with unstructured, natural language content.

Data traditional systems miss:

  • Product roadmaps shared in conversational prompts
  • Unpublished research discussed in AI chats
  • Performance reviews mentioned in natural language
  • Financial compensation details expressed as sentences
  • Internal project names and strategic initiatives
  • Proprietary methodologies described in context

Wald’s classification taxonomy extends beyond structured formats to support company data, product information, financial transactions, HR interactions, and other unstructured enterprise data. This reduces false negatives that pattern-based systems typically miss.

Q6. How does Wald’s data classification taxonomy work?

Wald uses a hierarchical taxonomy aligned with major compliance and security frameworks. The system provides fine-grained classification with multi-label detection capabilities.

Top-level categories include:

  1. Identity Data - Information that directly identifies individuals
  2. Financial Data - Financial identifiers and transaction records
  3. Healthcare Data - Health-related information tied to individuals
  4. Corporate Data - Organizational and internal operations information
  5. Technical and Security Data - System identifiers and credentials
  6. Personal Attributes - Characteristics describing individuals
  7. Behavioral and Activity Data - Information from user actions

Each category contains specific subcategories for precise classification. This enables role-based enforcement and audit-ready governance.

Q7. Why does context-aware classification matter for CISOs?

Context-aware data classification types deliver three critical advantages:

Reduced False Positives:

  • Fewer blocked workflows mean better productivity
  • Higher user adoption of security controls
  • Lower operational overhead for security teams

Higher Detection Accuracy:
Wald detects sensitive data in natural language, unstructured prompts, and mixed-content inputs. Traditional systems miss these exposures entirely.

Better Policy Control:

  • Fine-grained classification enables precise policies
  • Role-based enforcement matches your organizational structure
  • Audit-ready governance supports compliance requirements

Between June 2022 and May 2023, over 100,000 stolen ChatGPT account credentials were found on dark web marketplaces. Context-aware classification helps prevent the data exposures that lead to these breaches.

Q8. Does Wald integrate with existing enterprise data governance frameworks?

Yes. Wald integrates with existing classification frameworks including:

  • Microsoft Purview
  • PAN-based taxonomies
  • Internal enterprise classification standards

Organizations can define custom policies using Wald’s classification outputs. This means you don’t need to replace your existing governance infrastructure. Wald enhances it with context-aware detection capabilities that traditional systems cannot provide.

Q9. What data classification types protect against AI-specific risks?

AI workflows create unique exposure risks that traditional data classification types weren’t designed to handle.

AI-specific risks Wald addresses:

  • Sensitive data in conversational prompts
  • Confidential information in natural language queries
  • Mixed-content inputs combining multiple data types
  • Unstructured business data in AI assistant interactions

A 2024 EU audit brought to light that 63% of ChatGPT user data contained personally identifiable information (PII). Wald’s context-aware classification detects these exposures before they reach public AI models.

Q10. How do I implement context-aware data classification types?

Wald provides immediate protection without disrupting your workflows.

Implementation approach:

  1. Deploy Wald’s Context Intelligence platform - Automatic detection starts immediately
  2. Configure policies - Use fine-grained classification for role-based controls
  3. Monitor and refine - Audit-ready governance tracks all AI interactions
  4. Integrate with existing systems - Connect to your current DLP and governance frameworks

The platform automatically detects and sanitizes sensitive information in real time. Your teams can use AI capabilities while your data stays secure.

Best practice: Before you share anything with AI assistants, ask yourself: “Would I feel okay if this showed up in public?” If the answer is no, you need context-aware classification protecting your prompts.

Protecting your sensitive information must be your top priority in today’s AI world. Wald’s context-aware data classification types give you the detection accuracy and policy control that traditional DLP systems cannot deliver.

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