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Log Exports

DeepIntShield Enterprise provides comprehensive log export capabilities, allowing you to automatically export request logs, traces, and telemetry data to various storage systems and data lakes on configurable schedules.

Log exports are managed entirely from the DeepIntShield Workspace under Analytics → Exports. Each export is created as a job with a destination, a data configuration, and an optional schedule.

The log export system enables:

  • Scheduled Exports: Daily, weekly, or monthly automated exports
  • Multiple Destinations: Object stores, data warehouses, and data lakes
  • Format Flexibility: JSON or CSV, with optional gzip compression
  • Filtering & Transformation: Export specific data subsets with custom transformations
  • Compliance: Meet data retention and audit requirements

From Analytics → Exports in the DeepIntShield dashboard:

  1. Click New Export and give the job a descriptive Name.
  2. Choose a Destination and fill in its connection fields (see Supported Export Destinations).
  3. Configure the Data to export - the format, compression, which datasets to include, and any filters or transformations.
  4. (Optional) Set a Schedule to run the export automatically, or leave it off to run on demand.
  5. Save the job, then use Run now to export immediately. Completed exports appear in the Exports list, where you can download the produced artifacts.

The sections below describe the destination, data, schedule, and transformation options you configure on this page.

Each destination is configured with a type and a set of credentials and location fields entered in the export form.

Choose Local to keep the artifact on the gateway and download it from the Exports list - useful for one-off audits before wiring a remote destination.

FieldExample
Bucketdeepintshield-logs
Regionus-west-2
Prefixlogs/{year}/{month}/{day}/
Access Key ID${AWS_ACCESS_KEY_ID}
Secret Access Key${AWS_SECRET_ACCESS_KEY}
FieldExample
Bucketdeepintshield-logs
Prefixlogs/{year}/{month}/{day}/
Service Account Key${GCP_SERVICE_ACCOUNT_KEY}
FieldExample
Containerdeepintshield-logs
Account Name${AZURE_ACCOUNT_NAME}
Account Key${AZURE_ACCOUNT_KEY}
Prefixlogs/{year}/{month}/{day}/
FieldExample
Accountyour-account.snowflakecomputing.com
DatabaseDEEPINTSHIELD_LOGS
SchemaPUBLIC
Tablerequest_logs
WarehouseCOMPUTE_WH
Username${SNOWFLAKE_USERNAME}
Password${SNOWFLAKE_PASSWORD}
FieldExample
Hostdeepintshield-cluster.xxxxxx.us-west-2.redshift.amazonaws.com:5439
Databasedeepintshield_logs
Schemapublic
Tablerequest_logs
Username${REDSHIFT_USERNAME}
Password${REDSHIFT_PASSWORD}
FieldExample
Project IDyour-project-id
Datasetdeepintshield_logs
Tablerequest_logs
Service Account Key${GCP_SERVICE_ACCOUNT_KEY}

Enable a schedule on the export to run it automatically. Set the frequency, time, timezone, and (for weekly/monthly) the day. Leave the schedule off to run the export on demand.

FrequencySettings
DailyTime (e.g. 02:00) and timezone (e.g. UTC).
WeeklyDay of week (e.g. sunday), time, and timezone.
MonthlyDay of month (e.g. 1), time, and timezone.

Choose JSON or CSV as the output format and, optionally, gzip compression. Each export can include request logs, response logs, error logs, traces, metrics, and MCP tool logs.

{
"timestamp": "2024-01-15T10:30:00Z",
"request_id": "req_123456789",
"customer_id": "cust_abc123",
"provider": "openai",
"model": "gpt-4",
"endpoint": "/v1/chat/completions",
"method": "POST",
"status_code": 200,
"latency_ms": 1250,
"input_tokens": 100,
"output_tokens": 150,
"cost_usd": 0.0045
}
timestamp,request_id,customer_id,provider,model,endpoint,method,status_code,latency_ms,input_tokens,output_tokens,cost_usd
2024-01-15T10:30:00Z,req_123456789,cust_abc123,openai,gpt-4,/v1/chat/completions,POST,200,1250,100,150,0.0045

Narrow the exported data and reshape it directly in the export’s Data configuration.

Restrict the export to a subset of logs by date range, provider, model, status code, customer, latency bounds, or error state:

{
"filters": {
"date_range": {
"start": "2024-01-01T00:00:00Z",
"end": "2024-01-31T23:59:59Z"
},
"providers": ["openai", "anthropic", "azure"],
"models": ["gpt-4", "claude-3-sonnet"],
"status_codes": [200, 201, 400, 401, 403, 404, 500],
"customers": ["cust_123", "cust_456"],
"min_latency_ms": 100,
"max_latency_ms": 10000,
"has_errors": true
}
}

Aggregate, anonymize, or enrich records before they leave DeepIntShield:

{
"transformations": [
{
"type": "aggregate",
"group_by": ["provider", "model", "date"],
"metrics": [
"count",
"avg_latency",
"p95_latency",
"total_tokens",
"total_cost",
"error_rate"
]
},
{
"type": "anonymize",
"fields": ["customer_id", "request_id"],
"method": "hash"
},
{
"type": "enrich",
"add_fields": {
"export_timestamp": "${EXPORT_TIMESTAMP}",
"export_version": "${EXPORT_VERSION}"
}
}
]
}