Building Immutable Audit Trails for AI Systems

As AI systems make or influence decisions that affect customers, employees, and business outcomes, regulators and auditors increasingly require organizations to demonstrate accountability. This means maintaining comprehensive, tamper-proof records of what your AI systems did, when, and why. Building this capability into your infrastructure from the start is far easier than retrofitting it later.

What Auditors Want to See

When auditors examine AI systems, they typically look for:

• Complete capture: Every inference event logged with inputs, outputs, and metadata
• Immutability: Evidence that records cannot be modified or deleted after creation
• Timestamps: Accurate, tamper-evident timing information
• Chain of custody: Clear record of who accessed what data and when
• Retention compliance: Data kept for required periods, then properly disposed

Database-Level Immutability

Application-level controls can be bypassed. True immutability requires enforcement at the database level:

Trigger-Based Protection

Database triggers can prevent modifications to audit records:

• Block UPDATE operations on audit tables entirely
• Block DELETE operations except through controlled retention processes
• Log any attempted modifications as security events

Row-Level Security

PostgreSQL's Row Level Security (RLS) policies ensure that even database administrators cannot bypass audit controls without explicit policy changes that themselves get logged.

Append-Only Tables

Structuring audit tables as append-only with no UPDATE or DELETE permissions for application users provides a strong foundation for immutability.

Cryptographic Verification

For high-assurance environments, cryptographic techniques provide additional guarantees:

• Hash chains: Each record includes a hash of the previous record, making tampering detectable
• Digital signatures: Records signed with keys that application code cannot access
• Timestamping services: Third-party timestamp authorities provide independent verification

Retention and Disposal

Compliance isn't just about keeping data—it's about keeping it for the right amount of time:

• Configurable retention: Different data types may have different retention requirements
• Automated expiration: Data deleted on schedule without manual intervention
• Deletion certificates: Proof that data was properly disposed when required
• Legal hold support: Ability to suspend deletion for specific records under litigation

Export and Reporting

Audit data is only useful if it can be efficiently retrieved and presented:

• Structured exports: JSON, CSV, and PDF formats for different audiences
• Filtered queries: Search by time range, actor, action type, or resource
• Compliance reports: Pre-built reports aligned with SOC 2, GDPR, and HIPAA requirements

Framework Alignment

DriftRail's audit infrastructure is designed to support multiple compliance frameworks:

• SOC 2 Type II: Continuous monitoring and evidence collection for security controls
• GDPR Article 30: Records of processing activities for personal data
• HIPAA §164.312: Audit controls for electronic protected health information
• AI-specific regulations: Prepared for emerging requirements like the EU AI Act

Building immutable audit trails isn't just about checking compliance boxes—it's about creating the foundation for trustworthy AI systems. When you can prove what your AI did and demonstrate that those records haven't been tampered with, you build confidence with regulators, customers, and your own organization.