← Back to Maturity Map
Security & Compliance

Audit Trail & Logging

Track AI system activity for compliance and auditing. Comprehensive logging creates accountability, enables forensic analysis when issues occur, and satisfies regulatory requirements.

What Are Audit Trails & Logging?

Audit trails and logging systems capture a comprehensive record of activities within AI systems. This includes user interactions, model predictions, data access, configuration changes, and system events. Logs are timestamped, immutable, and stored securely to provide a reliable record for compliance, security investigations, and operational troubleshooting.

For AI systems, logging extends beyond traditional application logs to include model inputs and outputs, training events, bias metrics, and AI-specific activities required for accountability and transparency.

Why It Matters

Meet Compliance Requirements

Regulations like SOC 2, HIPAA, and GDPR require audit trails demonstrating who accessed what data and when.

Enable Forensic Analysis

When issues occur, logs provide the trail to understand what happened, when, and why.

Establish Accountability

Create clear responsibility by tracking who made decisions, approved changes, or accessed sensitive data.

Detect Anomalies

Identify unusual patterns that may indicate security threats, misuse, or system problems.

What to Log

User Activity

Login events, authentication attempts, user actions, and session information.

Data Access

Who accessed what data, when, and for what purpose including PII and sensitive information.

Model Interactions

Prompts, inputs, outputs, and model predictions with context about the user and use case.

Configuration Changes

System settings, security controls, access permissions, and model parameters modified.

Security Events

Failed authentication, access denied, suspicious activity, and potential threats.

Model Training Events

Training data used, hyperparameters, evaluation metrics, and deployment decisions.

Maturity Levels

Not Started / Planning

Minimal or no logging. No audit trail for AI system activities. Unable to answer who did what and when.

In Progress / Partial

Basic application logs captured. Some user activity tracked. Logs stored but not systematically reviewed or retained.

Mature / Complete

Comprehensive audit logging across all AI systems. Immutable logs with user activity, data access, model interactions, and security events. Centralized log management with search capabilities. Automated alerting on suspicious patterns. Long-term retention and archival meeting compliance requirements.

How to Get Started

  1. 1.
    Define Logging Requirements: Identify what events need to be logged based on compliance, security, and operational needs.
  2. 2.
    Implement Structured Logging: Use consistent log formats with timestamps, user IDs, event types, and relevant context.
  3. 3.
    Deploy Centralized Log Management: Aggregate logs from all AI systems into a searchable, secure platform.
  4. 4.
    Set Retention Policies: Define how long logs are kept based on regulatory requirements and storage capacity.
  5. 5.
    Enable Log Analysis: Set up dashboards, alerts, and periodic reviews to derive value from logged data.

Ready to Implement Comprehensive Audit Logging?

Get expert help building audit trails and logging systems that provide accountability, enable forensics, and meet compliance requirements.