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Guidelines for Sanitizing Log Samples


Overview

To maintain privacy and security, it’s essential to properly sanitize log samples before sharing or using them in any context that may expose sensitive information. Follow these guidelines to ensure that no personally identifiable information (PII) or other sensitive data is included in the samples.

Definitions

Sanitization:

The process of modifying or removing sensitive information from data to prevent the exposure of PII or other confidential details. This can involve anonymization, pseudonymization, and the removal of specific data fields.

Anonymization:

Anonymization is the process of removing or modifying personal data in such a way that individuals cannot be identified, directly or indirectly. Once data is anonymized, it should be impossible to trace it back to an individual.

Anonymizing Identifiers

Anonymization should be applied to data elements that could uniquely identify an individual. This includes:

  • Names: Replace with general descriptors (e.g., “User1,” “Customer A”).
  • Email Addresses: Replace with non-descriptive placeholders (e.g., “email@example.com”).
  • Phone Numbers: Replace with generic placeholders (e.g., “555-1234”).
  • IP Addresses: Replace with non-identifiable placeholders (e.g., “192.0.2.0”).

Anonymization makes it impossible to identify individuals from the anonymized data alone.

Pseudonymization:

Pseudonymization is the process of replacing private identifiers with fake identifiers or pseudonyms. While pseudonymized data can be used for analysis or sharing, it can still be re-identified if the pseudonym is matched with the original data.

Pseudonymizing Identifiers

Pseudonymization can be used for identifiers that do not need to be completely anonymized but should be altered to maintain some degree of confidentiality. This includes:

  • User IDs: Replace with pseudonyms (e.g., “user123” becomes “uABC123”).
  • Transaction IDs: Replace with a different but unique pseudonym (e.g., “txn5678” becomes “txnXYZ5678”).
  • Custom Identifiers: Replace with systematic pseudonyms (e.g., “order789” becomes “orderABC789”).
Caution:

  • When to pseudonymize vs. anonymize: pseudonymize data that may still need to be re-linked for operational purposes but does not require direct identification of individuals. Anonymize data that should be completely de-identified to ensure privacy.
  • Risk of Re-Identification: Pseudonymized data can still be re-identified if matched with the original dataset. Use pseudonymization carefully when there is a potential risk of unauthorized data correlation.
Recommended Best Practices for Sanitizing Log Samples

To protect sensitive information and ensure compliance with privacy standards, it is crucial to sanitize log samples before using them with our AI tools. Follow these guidelines:

  1. Avoid Including Sensitive Data: Do not include names, email addresses, phone numbers, or IP addresses in the logs that will identify your organization or its stakeholders. Replace them with placeholders.

Remove Personally Identifiable Information (PII)

Examples: Names, addresses, phone numbers, email addresses, social security numbers, account numbers.
Replace PII with generic placeholders (e.g., John Doe becomes USER_NAME, 123 Main St becomes ADDRESS).

Exclude Confidential Business Information

Examples: Company financials, proprietary algorithms, sensitive internal communications.
Replace sensitive data with placeholders (e.g., proprietary_algorithm_v1 becomes ALGORITHM_NAME).

Mask IP Addresses and Hostnames

Examples: 192.168.1.1, myserver.company.com
Replace IP addresses with IP_ADDRESS and hostnames with HOSTNAME.

Anonymize and Psuedonymize Identifiers

Examples: User IDs, session tokens, unique identifiers.
Replace with generic terms 
Original: User John Doe logged in from 192.168.1.1 using johndoe@example.com
Anonymized: User [REDACTED] logged in from [REDACTED] using [REDACTED]
Pseudonymized: User u12345 logged in from 192.0.2.0 using email@example.com

Generalize Dates and Times

Examples: Specific timestamps, dates of transactions.
Use placeholders (e.g., 2023-05-01 08:00:00 becomes DATE_TIME). 

  1. Log Sample Entry: Pasting the log samples as guided below will help in maintaining the consistency of the log format for accurate analysis.
    • Enter Each Log Separately: Log samples should be entered as separate entries on new lines.
    • No Empty New Lines: Ensure there are no empty new lines between log entries.
    • Use Unique Log Events: Ensure to provide log samples pertaining to unique log events to enhance the quality of the DNIF AI extractor. This step will also benefit in manually building the extractor.

ENTER LOG SAMPLES AS FOLLOWS:
sample log 1
sample log 2
sample log 3