Data Management Glossary
Sensitive Data Detection
Sensitive data detection is the process of identifying and flagging (see data tagging) sensitive or confidential information within a system, document, or dataset. Sensitive data can include personally identifiable information (PII), financial data, healthcare records, proprietary information, and more. The detection process is critical for privacy, security, and compliance with data protection regulations like GDPR, HIPAA, and CCPA. As AI data governance and compliance concerns in the enterprise grow, sensitive data protection is growing in importance.
What are the common types of sensitive data that must be protected?
Personally Identifiable Information (PII):
- Name
- Social Security Number (SSN)
- Date of birth
- Address
- Phone number
- Email address
Financial Data:
- Credit card numbers
- Bank account numbers
- Financial statements
Healthcare Data:
- Medical records
- Health insurance details
- Prescription information
Intellectual Property (IP):
- Trade secrets
- Patents
- Proprietary formulas or algorithms
Authentication Data:
- Passwords
- Security tokens
- Encryption keys
What are some methods of sensitive data detection?
Pattern Matching (Regular Expressions): Detects patterns in text that match formats commonly used for sensitive data, such as credit card numbers (e.g., Luhn’s algorithm for validation), social security numbers, or email addresses.
Data Classification: Systems use rules or machine learning algorithms to categorize data based on its content and context. This can be based on pre-set categories such as “confidential,” “public,” or “internal use only.”
Natural Language Processing (NLP): NLP techniques are used to analyze and understand the context of text, which helps identify sensitive information that doesn’t follow a predictable pattern but can be inferred through the meaning of the text.
Machine Learning: Machine learning models can be trained to recognize sensitive data by analyzing a large corpus of labeled data. Once trained, they can generalize from this data to detect sensitive information in new documents or datasets.
Data Masking and Tokenization: Detecting and replacing sensitive information with anonymized values to protect it during storage or transmission.
Contextual Analysis: A more advanced approach that looks at the surrounding text and metadata to understand whether the data could be sensitive, rather than just relying on pattern recognition.
What are some tools for sensitive data detection?
Today there are many categories of vendors and solution providers who provide elements of sensitive data protection, including data back-up and data storage vendors and increasingly sensitive data protection is becoming part of a holistic unstructured data management strategy. Examples of sensitive data tools include:
DLP (Data Loss Prevention) solutions: Used to monitor and protect sensitive data in transit, at rest, and in use.
Regular expression engines: For detecting simple patterns.
Cloud services: Providers like AWS Macie and Azure Information Protection and Microsoft Purview offer sensitive data detection for data stored in their environments.
Open-source tools: Like Octopii for detecting sensitive information in code repositories.
What are some common sensitive data protection use cases?
- Compliance Audits: Ensuring that data handling adheres to regulations.
- Data Breach Prevention: Detecting and protecting sensitive data before unauthorized access occurs.
- Encryption Management: Identifying sensitive data that should be encrypted.
- AI Data Governance and Compliance: Ensure only the right data is being delivered to AI services in the enterprise.
