AI Architecture Audit Checklist

Clarify the business capability, users, decisions, systems involved, and the specific AI role before choosing tools.

• Target workflow and user groups
• Current manual steps and decision points
• Expected business or operational signal

List the source systems, ownership boundaries, freshness needs, access rules, and records that must never be exposed.

• Documents, databases, APIs, events, and files
• Tenant, role, and record-level permissions
• Refresh cadence and lineage expectations

Identify where AI output can affect customers, money, data integrity, compliance, or internal decisions.

• High-risk actions and write paths
• Exception handling and fallback owners
• Review queues and escalation paths

Define the checks that must pass before retrieval, agent actions, or AI-generated recommendations reach users.

• Grounding and source coverage tests
• Policy, privacy, and permission checks
• Release thresholds for quality, latency, and cost

Make ownership visible across product, engineering, data, security, operations, and business teams.

• Run ownership and incident response
• Monitoring and review cadence
• Change approval and rollback paths

Turn findings into a practical sequence: diagnostic, prototype, controlled release, production hardening, and operation.

• First useful production slice
• Dependencies and sequencing
• Milestones with acceptance criteria

Track the known failure modes so leadership can decide what to build now, delay, or design around.

• Data leakage and permission gaps
• Hallucination and unsupported claims
• Cost, latency, adoption, and support risks

Create the ongoing measurement plan for AI behavior after prompts, models, data, or workflow rules change.

• Golden datasets and negative tests
• Trace review and feedback loops
• Regression checks before release