Complete Visibility into AI Usage

When the auditor asks “who used AI to access this data?” you need an answer. Not a guess. An answer.

Audit logging captures every AI interaction for compliance, security, and operational visibility.

What Gets Logged

User information:

• Who made the request
• Their role and permissions
• Session and authentication details

Request details:

• What was asked (prompt)
• When it was asked (timestamp)
• Where it was asked from (client info)

Response details:

• What AI returned
• Which model was used
• How long it took
• Cost of the request

Policy events:

• Policies evaluated
• Blocks or modifications applied
• Warnings generated

Why Audit Logging Matters

Compliance: Regulations require knowing who accessed what data.

Security: Detect unusual patterns that might indicate compromise.

Incident response: Investigate when something goes wrong.

Cost allocation: Attribute AI usage to teams and projects.

Quality assurance: Understand how AI is being used and performing.

Compliance Requirements

Various frameworks require audit capabilities:

SOC 2: Security monitoring and logging controls HIPAA: PHI access logging requirements GDPR: Processing activity records PCI DSS: Access tracking for cardholder data Internal policies: Your specific requirements

Audit logs provide the evidence.

Log Retention

How long to keep logs:

Regulatory requirements: Some require specific retention periods (7 years for some financial data).

Legal considerations: Litigation hold requirements may extend retention.

Operational needs: Enough history to be useful for investigation.

Storage costs: Longer retention = more storage.

Configure retention policies appropriately.

Searching and Analysis

Logs are only useful if you can find what you need:

Search by user: “Show all AI usage by user X”

Search by time: “Show activity during the incident window”

Search by content: “Find queries mentioning customer data”

Search by model: “Show all GPT-4 usage”

Aggregations: “Usage by team over time”

SIEM Integration

Export logs to your security information system:

Common integrations:

• Splunk
• Datadog
• Elasticsearch
• Azure Sentinel
• Custom SIEM

Integration patterns:

• Real-time streaming
• Periodic export
• API pull

Correlate AI logs with other security events.

Privacy Considerations

Logs themselves contain sensitive data:

Prompt content: May include sensitive information

Response content: AI outputs stored in logs

User identification: Privacy of users

Balance:

• Log enough for compliance and security
• Redact or hash where possible
• Restrict log access
• Encrypt log storage

Alerting

Don’t just log—act on patterns:

Volume anomalies: Unusual usage spikes

Policy violations: Content blocks, permission denials

Cost thresholds: Approaching budget limits

Security indicators: Suspicious access patterns

Configure alerts for what matters.

The Audit Logging Checklist

Implementing audit logging:

• Define what to log (all requests recommended)
• Set retention periods per compliance requirements
• Configure log storage and encryption
• Set up search and analysis tools
• Integrate with SIEM if applicable
• Configure alerts for key events
• Test log retrieval and analysis
• Document for auditors

Know who did what, when. Always.

Configure audit logging with Zentinelle →