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A Practical Seven-Field JSON Logging Baseline for Small SaaS Teams

A consistent, typed JSON schema helps small SaaS teams debug failures and investigate important actions. Start with seven signals, then control sensitive data and strengthen audit records for value-changing operations.
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A small SaaS team can make application logs far more useful by giving every service a consistent, typed JSON schema for event time, severity, event name, service context, request or trace correlation, actor or tenant, and outcome. These seven signals are a practical framework synthesized from OpenTelemetry and OWASP guidance—not a list prescribed by either project. JSON syntax alone is not enough: stable field names and types are what make records dependable to search, compare, and process.

Start with one schema, not seven unrelated log formats

Structured logging means each record follows a consistent schema with well-defined fields and types. OpenTelemetry describes why that structure matters: downstream tools can rely on the fields rather than trying to interpret arbitrary message text. See OpenTelemetry’s overview of logs.

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Keep field names and meanings consistent across services. For example, if one service emits request_id and another emits correlation for the same identifier, cross-service searches become harder. Decide which fields are required, which are conditional, and their types; document that contract alongside the application.

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The seven signals to include

1. Event time

Record when the event happened, using a consistent timezone and precision across services. OpenTelemetry’s data model defines Timestamp as the time the event occurred. If the event’s occurrence time differs from when collection code received it, preserve that separately as observed time; OpenTelemetry defines ObservedTimestamp for that purpose. See the OpenTelemetry Logs Data Model.

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2. Severity

Use a consistent severity level so operators can filter and compare records. OpenTelemetry distinguishes readable SeverityText from numeric SeverityNumber; numeric values are useful for ordering and comparisons when the logging pipeline supports them. Keep the mapping between your application’s levels and the exported representation consistent.

3. Event name or type

Give each kind of event a stable name, such as auth.login_failed or billing.payment_declined. OpenTelemetry calls this field EventName, the name identifying the event class or type. Define what each event means and which attributes it may carry; avoid relying on a free-form message as the only way to distinguish event types.

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4. Service and deployment context

Identify the producer with context such as service name, deployed version, and environment. OpenTelemetry models this producer information as a Resource, distinct from attributes describing an individual event. Keeping it separate lets a query identify both what happened and which application or deployment emitted it.

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5. Request and trace correlation

Carry a request identifier or trace context through the work triggered by a request. Include trace and span IDs when available so a log record can be joined to a trace and related work across components. OpenTelemetry describes trace context as a way to correlate logs with traces and participating services. These identifiers are conditional: not every event necessarily has an associated trace. See OpenTelemetry Logging.

6. Actor or tenant context

For events where identity matters, include a controlled identifier for the user, service account, or tenant that acted. Application code often has context about who performed an action and what happened that infrastructure logs do not. OWASP’s Logging Cheat Sheet recommends capturing useful event context, but this is not a reason to copy a full user profile into every record.

7. Outcome and bounded error context

Record the action’s result and, when useful, a concise reason or exception context. Examples include a status such as denied and a controlled reason code such as invalid_credentials. OWASP identifies action, object, status, reason, and description as potentially useful event details; OpenTelemetry supports structured exception information in attributes under its exception conventions. Keep the detail sufficient for investigation without dumping sensitive payloads.

An illustrative record

This example shows one possible shape, not a prescribed standard or tested implementation. Its identifiers and values are illustrative.

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{
  "timestamp": "2026-10-07T15:14:43.774355Z",
  "severity_text": "WARN",
  "event_name": "auth.login_failed",
  "service": {
    "name": "accounts-api",
    "version": "1.8.2",
    "environment": "production"
  },
  "trace_id": "example-trace-id",
  "actor": {
    "user_id": "internal-user-key"
  },
  "outcome": {
    "status": "denied",
    "reason_code": "invalid_credentials"
  }
}

A production schema may also include fields such as a span ID, observed timestamp, or exception attributes when relevant. The key is to define those fields deliberately and use the same meanings and types across services.

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Keep logs useful without turning them into a data liability

OWASP frames useful application logging around when, where, who, and what. Before adding a field, ask whether it helps answer one of those questions for a real operational or security use case.

  • Do not log passwords, access tokens, encryption keys, connection strings, or raw session identifiers. If a legitimate need remains for a sensitive value, assess whether it can be masked, sanitized, hashed, or encrypted instead.
  • Avoid request and response bodies by default, and minimize personal information. An IP address may count as personal data depending on context, so weigh its investigative value against the responsibilities of storing it.
  • Limit who can read logs, set retention and deletion rules appropriate to your deployment, and review what is being collected. OWASP’s guidance does not establish one retention period that fits every organization.
  • Ensure untrusted values cannot forge or corrupt log records; validate and sanitize data before recording it.

OWASP notes that application code can have the richest view of identity, roles, permissions, targets, actions, and outcomes. That makes application logs valuable, but it also makes deliberate data minimization and access controls important.

Give sensitive value-changing actions stronger audit treatment

For actions that move money, grant permissions, or dispense value, capture the authenticated actor, target resource, action, outcome, enough request context to reconstruct the event, and relevant business context. These records serve an audit purpose beyond routine debugging. OWASP recommends that such logs be tamper-evident and separate from general application logs; see the Business Logic Security Cheat Sheet.

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Turn the baseline into a working logging system

  1. Define the event contract. Specify field names, types, allowed severity values, timestamp conventions, and which fields are required or conditional. Document event names and their meanings.
  2. Instrument application events. Emit records at meaningful boundaries—such as authentication decisions, failed dependencies, and important business actions—rather than logging every variable or copying entire requests.
  3. Propagate correlation context. Preserve request or trace context as work crosses service boundaries, and attach trace and span IDs when present.
  4. Check records at ingestion. Validate that services follow the shared schema and that required context is present. Treat inconsistent names or types as a schema defect, not a query problem.
  5. Build queries around questions. Confirm that operators can find failures by time, severity, event, service version, request or trace, actor, and outcome. Create alerts only for conditions someone can investigate and act on.
  6. Review exposure and retention. Inspect actual emitted records for secrets and unnecessary personal data, then verify access, retention, and deletion controls against the deployment’s requirements.

Choosing where to send and search the logs

No particular product is required to implement this baseline. When comparing an implementation or hosted log service, assess whether it works with OpenTelemetry and your schema, supports log-to-trace correlation, fits expected ingestion and retention volume, provides the queries and alerts your team needs, and offers appropriate access controls and data handling. These are evaluation criteria derived from the technical needs, not vendor rankings; verify current capabilities and terms with the provider.

A small team can begin with the logging pipeline it already operates if it can preserve the schema, search records, and control access. Centralized search, longer retention, alerting, or trace correlation may justify a dedicated service as operational needs grow; the decision depends on your volume, workflow, and data requirements.

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