How to Handle Sensitive Data in Your Logs Without Compromising Observability

When things break, your logs should help you fix issues, not pile on a compliance headache. But if there’s user-specific data in the mix, especially in industries like healthcare or finance, things get complicated fast.

The good news is that you can still get the visibility you need without compromising privacy. Let’s walk through how to handle Personally Identifiable Information (PII) and Protected Health Information (PHI) in LM Logs so your team can troubleshoot smarter and safely.

You don’t need to choose between visibility and compliance.

What Kind of Logs Are We Talking About?

Not all logs are equal when it comes to compliance risk.

LM Logs is built to help you troubleshoot metric-based alerts. It automatically aligns relevant logs with alert timelines so you can get to the root cause faster. That’s why the most valuable logs for LM Logs are:

• System logs
• OS logs
• Device and application logs
• Syslog data

These logs are typically used for performance monitoring and system diagnostics. For example, a system log might capture a configuration change right before an alert fired, giving you a clear trail to investigate. We’ve seen teams identify a misapplied patch or a service that restarted unexpectedly without needing to dig through unrelated user data. They rarely contain Personally Identifiable Information or Protected Health Information.

Where things get tricky is with custom application logs. These can contain user-specific data (like login info, transaction IDs, or even health or financial information) depending on what your dev teams choose to log.

How to Identify Personally Identifiable Information and Protected Health Information in Logs

These fall into a few categories:

Direct Identifiers	Indirect Identifiers	Sensitive Personal Information
– Full name – Social Security number – Passport or driver’s license number – Email address (if tied to a specific individual) – Phone number	– Date of birth – Gender – Usernames or account IDs – IP addresses (in some contexts) – Biometric data	– Financial data (e.g., credit card or bank account numbers) – Health records or insurance data – Racial, ethnic, or sexual orientation data

The compliance risk increases when logs contain more than one of these fields in a way that makes an individual identifiable (this is called “linkability”). A single username might not be a problem. A username + email + credit card? That’s a risk.

Best Practices for Managing PII and PHI in LM Logs

1. Avoid logging sensitive data in the first place

If it’s not in the logs, it can’t be exposed. Train dev teams to:

• Use unique, non-identifying codes like session or transaction IDs
• Avoid logging names, emails, or account numbers unless absolutely necessary
• Challenge the need for user-specific data in any log stream

2. Sanitize data before ingestion

Use Fluentd, Fluent Bit, or Logstash with LogicMonitor-supported plugins. These tools let you:

• Drop sensitive log lines.
  • Fluentd: Use fluent-plugin-grep to filter out entries with sensitive keywords.
  • Fluent Bit: Use the built-in Grep Filter to include or exclude records based on pattern matching.

For example, a log line contains a keyword like “password” that should never be ingested.

Before (raw log):

2024-05-10 12:03:45 Login failed for user admin. Reason: Incorrect password

After (dropped): Log line is not ingested or forwarded due to matching a sensitive keyword filter.

• Mask or hash sensitive fields.
  • Fluentd: Use fluent-plugin-record-reformer to hash or redact sensitive values like email addresses or credit card numbers.
  • Fluent Bit: Use the Modify Filter with the REPLACE or REMOVE_KEY operations, or the Lua Filter to script custom redaction or transformation logic.

An example of this is an email and credit card number that needs to be redacted or partially masked.

Before (raw log):

{

  "user": "[email protected]",

  "credit_card": "4111111111111111",

  "action": "purchase"

}

After (masked/redacted):

{

  "user": "jane.doe@####",

  "credit_card": "************1111",

  "action": "purchase"

}

• Anonymize IPs and user IDs:
  • Fluentd: Use fluent-plugin-anonymizer to transform identifiable fields with safe placeholders.
  • Fluent Bit: Use the Lua Filter to write a function that anonymizes fields such as IP addresses or user IDs before forwarding.

For example, an IP address and user ID should be anonymized for privacy compliance.

Before (raw log):

{

  "ip": "192.168.1.102",

  "user_id": "user-123456",

  "status": "success"

}

After (anonymized):

{

  "ip": "192.168.1.xxx",

  "user_id": "user-xxxxxx",

  "status": "success"

}

And if you don’t already have log processing capabilities in place, we recommend starting with Fluent Bit. It’s powerful, flexible, and LM-supported.

3. Segment logs by sensitivity

If sensitive data must be logged:

• Use a dedicated log level (e.g., debug_secure) or separate log files
• Limit ingestion of those logs into LM Logs
• Tag and document them clearly for downstream filtering

4. Apply role-based access control (RBAC)

LogicMonitor lets you restrict log visibility:

• Use RBAC to limit access to sensitive logs
• Only grant permissions to those who truly need it
• Align access rules with your internal data-handling policies

5. Set log retention policies

Sensitive logs should have shorter lifespans:

• Follow the principle of least privilege and least retention
• Retain logs with sensitive data only as long as needed to resolve issues
• LM Logs supports retention tiers of 7 days, 1 month, 90 days, and 1 year
• Pro tip: Most teams retain sensitive application logs for 7 days or less

When a log is ingested into LM Logs, three important things happen:

• It’s tagged with its retention length. This depends on your subscription tier and helps control how long the data stays in the platform.
• It’s stored securely. Logs live in encrypted S3 buckets with at-rest encryption to protect your data.
• It’s automatically deleted. Once the log hits its expiration timestamp, it’s wiped, and no manual cleanup is needed.

This process helps ensure your log data is managed responsibly from the moment it enters the system.

Secure your log ingestion pipeline

Make sure your ingestion process is locked down:

• Use in-transit TLS encryption
• Ingest logs via LM’s secure API with a unique API key, Fluent-Bit
• Configure Fluentd or Logstash to transmit sanitized logs only

7. Track user actions with audit logs

LM Logs integrates with LogicMonitor Audit Logs, which:

• Tracks log configuration changes
• Records who accessed what and when
• Provides visibility into alert updates, login attempts, and more

These logs are tamper-proof and system-level only, so individual entries can’t be altered post-creation.

LM Logs: Designed for Visibility, Built for Trust

LM Logs isn’t a SIEM, and that’s by design. It’s not built for threat detection or forensic analysis. Instead, LM Logs focuses on helping your IT team find and fix problems faster by aligning logs with alerts in a way that saves time, reduces MTTR, and eliminates finger-pointing.

By following these best practices, you can get the full value of LM Logs without putting your organization’s data or compliance posture at risk.

Need help setting this up? Reach out to your LogicMonitor customer success team. We’re here to help you design a logging strategy that’s powerful, practical, and privacy-aware.

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