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Glossary

Our Criblpedia glossary pages provide explanations to technical and industry-specific terms, offering valuable high-level introduction to these concepts.

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Data Redaction

What is Data Redaction?

Data redaction is the process of masking or hiding sensitive information within data fields to protect it from unauthorized access. When redacting data, such as a social security number, you usually define specific fields or patterns to be redacted and establish a standard or customized replacement.

A good example would be the previously mentioned SSN. You could search for the common SSN format (xxx-xx-xxxx) or look for a field titled SSN (or similar). However, it gets a little more difficult if it is just represented by a series of numbers, such as 1234567о89. In such scenarios, additional information might be required as this number string could be almost anything.

Data Redaction vs Data Obfuscation

Data redaction and data obfuscation both aim to protect sensitive information, but they are used in slightly different contexts and have different methods.

Data redaction involves permanently removing or concealing sensitive information within a dataset to prevent unauthorized access. This is often used to comply with privacy laws or regulations. In data redaction, parts of the data are generally removed or replaced with a placeholder such as “REDACTED”.

Data obfuscation, on the other hand, involves deliberately introducing a level of complexity into the data to make it hard to understand without necessarily removing any part of it. This technique often modifies the representation of data. It masks it in a way that makes it incomprehensible to unauthorized users, while still retaining its original structure.

What are the key differences?

  • Permanence: Redaction is generally a permanent removal or replacement of data. Obfuscation retains the original data structure, just makes it unreadable.
  • Usage: Redaction is often used for regulatory compliance and data privacy. Obfuscation is more about making data unusable to unauthorized parties while retaining its original structure and usability for intended purposes.
  • Techniques: Redaction techniques focus on data removal or replacement with placeholder text. Obfuscation techniques focus on altering the data in a reversible (e.g., with the right key) or non-reversible (e.g., hash) way.

What Data needs to be redacted?

Data that typically need to be redacted includes any sensitive or personally identifiable information (PII) that can compromise privacy or security if exposed. This includes but is not limited to:

  • Personal Information: SSN, Driver’s license numbers, National identification numbers, Passport numbers, Phone numbers, Email address, Birthdate, etc.
  • Financial Information: Credit card numbers, Bank account numbers, Transaction details, Tax identification numbers, etc.
  • Health Information: Medical records, Healthcare provider details, Insurance policy numbers (HIPAA type info)
  • Credential Information: Usernames/passwords, Security questions/answers, Cryptographic keys, Token IDs, etc.
  • Corporate Information: Trade secrets, Proprietary business details, Confidential agreements and contracts, Internal communications, such as emails and memos, etc.

Data Redaction techniques

To protect sensitive information, various data redaction techniques can be employed. These techniques achieve anonymization by obscuring or transforming critical data elements within a dataset.

  • Pattern Matching and Replacement: Identify sensitive data using Regex or predefined patterns and replace them with placeholder text (e.g., “REDACTED”).
  • Character Substitution: Replace specific characters within a sensitive field with a masking character (e.g., replacing all but the last four digits of a credit card number with asterisks: ****-****-****-1234).
  • Data Tokenization: Convert sensitive data into random tokens that have no exploitable value on their own. Tokens can be mapped back to the original data using a secure tokenization system.
  • Shuffling: Rearrange data within a dataset while keeping the overall dataset structure intact. For example, swapping data elements within columns of a database to anonymize specific entries.
  • Nulling Out: Completely remove or ‘null out’ sensitive fields by replacing them with null values. This erases effectively their content from the dataset.
  • Generalization: Replace specific data with more general information to maintain some level of usability while protecting sensitive details. Example: Replacing a specific age with an age range (e.g., “34” becomes “30-40”).
  • Aggregation: Combine or summarize sensitive data to display only aggregate totals or summaries. In that way, the risk of identifying individuals from the data is reduced.
  • Pseudonymization: Replace identifying fields within a dataset with pseudonyms or artificial identifiers. This allows the data to be used in analytics while preserving privacy.

Static vs Dynamic redaction: What is the difference?

The choice of redaction method depends on the type of data being processed. There are 2 main types, static and dynamic redaction. Let’s break them down.

Static redaction
Static redaction is a predefined, fixed process where specific data fields or patterns are consistently redacted based on set rules. This method is useful when you have predictable, unchanging data that needs redacting. Such examples are specific keywords, phrases, or identifiable patterns (e.g., credit card numbers or social security numbers).

Dynamic Redaction
Dynamic Redaction involves evaluating data on the fly using more complex logic or scripts. Often adapts to varying inputs and requires real-time assessment. This method is beneficial for scenarios where redaction rules need to adapt based on the content or context of the data streaming through the system. For example, redacting variable-length sensitive information or data dependent on certain conditions.

Data Redaction Use cases

Redacting data involves removing sensitive information to protect individuals’ privacy, maintain compliance with regulations, and prevent misuse of the data. Redaction processes are commonly customized to ensure that sensitive, verbose, or undesired data within datasets are not exposed unnecessarily. This is crucial for maintaining security, privacy, and compliance with internal policies or legal requirements,including:

  1. Compliance with Privacy Regulations:
    • GDPR: Redacting personally identifiable information (PII) such as names, addresses, and email addresses to comply with the General Data Protection Regulation (GDPR).
    • HIPAA: Ensuring health data privacy by redacting protected health information (PHI) such as patient names, medical records, and other sensitive info in compliance with the Health Insurance Portability and Accountability Act (HIPAA).
  2. Securing Sensitive Information:
    • Credit Card Details: Redacting credit card numbers from logs to protect credit card information and reduce PCI DSS compliance scope.
    • Social Security Numbers: Removing or masking social security numbers to prevent identity theft and ensure data security.
  3. Protecting Internal Data:
    • Internal IP Addresses: Redacting internal IPs in logs that are routed to external partners to protect network infrastructure details.
    • Employee Information: Masking or removing internal employee data such as user IDs or email addresses in logs shared with third-party vendors.
  4. Protecting Intellectual Property and Trade Secrets:
    • Source Code Logs: Redacting pieces of sensitive source code or proprietary algorithms that may appear in application logs.
    • Confidential Business Information: Ensuring that confidential business information such as financial projections or strategic plans is redacted from logs before external sharing.

Data Redaction Best practices

Contrary to popular belief there is no one way to redact data. It’s usually based on use cases and the type of data encountered. However, redaction best practices can help ensure the effectiveness and security of the redaction process. Here are some best practices highlighted in the context of Cribl products:

Identify Sensitive Information
Clearly define what constitutes sensitive data (e.g., PII, PHI, PCI) in your organization to ensure you are protecting the right information.

Use Standard Redaction Patterns
Consistently apply a standard redaction pattern. For example, Cribl Edge uses a pattern that echoes the first and last two characters of a value while replacing intermediate characters with ellipses (??…??).

Customize as Needed
Utilize the custom redact string feature to override default patterns if necessary. This will ensure that the redaction meets your specific requirements.

Leverage Detection Engines
Integrate tools like Nightfall’s Data Loss Prevention (DLP) engine to automatically detect and redact sensitive information using machine learning.

Test Thoroughly
Ensure that redaction settings are thoroughly tested to avoid accidental exposure of sensitive data.

Regulatory Compliance
Ensure your redaction practices comply with relevant data protection compliance frameworks like GDPR, HIPAA, etc.

Documentation and Training
Document your redaction policies and ensure that all relevant personnel are trained on these practices.

By applying these best practices, you can effectively secure sensitive data and maintain compliance with data protection regulations.

Cribl Redaction Capabilities

Cribl offers robust capabilities for data redaction. In Cribl Stream, you can redact sensitive information from your data using Pipeline Functions.

For example, you might use the following Pipeline Functions designed for redacting:

  • Regex Extract: Use this function to match and manipulate specific text patterns.
  • Mask: This function can be used to replace identified sensitive information with a placeholder. Learn more about Data Masking here.
  • A deeper dive into this topic is available here.

By following this configuration, Cribl products can help systematically redact sensitive data, ensuring your logs and data streams are secure and compliant with your data protection policies.

Why is Data Redaction important?

Data redaction is important because it protects sensitive information from unauthorized access. By redacting it, organizations avoid exposing individuals to identity theft or fraud. Data redaction also helps businesses comply with regulations that protect personal information.

Want to learn more?
Read our solution brief title Data Protection and Cost Reduction with Cribl Stream and Nightfall AI.
Read solution brief

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