An architecture audit should produce decisions, not a diagram inventory. The useful output is a ranked list of risks tied to business impact, supporting evidence, and a remediation sequence the team can execute without stopping the roadmap.
1. Business criticality and failure policy
- Which user or business action depends on each AI output?
- What is the cost of a false positive, false negative, unsupported answer, or outage?
- Which operations require review, abstention, or a deterministic fallback?
- Are service objectives defined for quality, latency, availability, and cost?
2. Data provenance and boundaries
- Can every source be traced to an owner, permitted use, freshness expectation, and deletion policy?
- Are tenant and role restrictions enforced before retrieval?
- Do logs, caches, queues, backups, and analytics create unreviewed copies?
- How are malformed, duplicate, stale, or adversarial documents handled?
3. Evaluation and change control
Review the evaluation dataset, scoring logic, edge cases, baseline, and release gate. Confirm that prompt, model, embedding, chunking, reranking, and parser changes are versioned and can be compared before deployment. A thumbs-up widget is feedback, not a release-quality evaluation system.
4. Security and privacy
- Threat-model prompt injection, data exfiltration, insecure tool use, and cross-tenant retrieval.
- Inventory model and data vendors, retention settings, contracts, regions, and administrator access.
- Verify secret handling, least-privilege identities, encryption, audit events, and deletion workflows.
- Keep model output untrusted until it passes authorization, validation, and policy checks.
5. Reliability and operational behavior
Trace one request through APIs, retrieval, model calls, tools, queues, databases, and notifications. Look for missing timeouts, unbounded retries, non-idempotent writes, absent backpressure, and failure states that leave users waiting. Confirm a provider outage does not become data loss or duplicate work.
6. Observability and incident response
Operators need to correlate model version, prompt version, source identifiers, validation outcome, latency, token use, cost, user correction, and final action. Alerts should map to a runbook and an owner. Test rollback, provider disablement, and queue recovery rather than assuming they work.
7. Cost and scalability
- Measure cost per completed business operation, not only cost per token.
- Model peak concurrency, context growth, retrieval load, queue depth, and external rate limits.
- Identify work that can use caching, smaller models, batching, deterministic code, or offline processing.
- Set budgets and alerts before usage grows, with graceful behavior when limits are reached.
8. Delivery and ownership
Every critical component needs an owner, test strategy, deployment path, rollback path, and documented reason for its existence. Review concentration risk: one engineer, one opaque vendor, or one undocumented prompt should not be able to stop the product.
Turn findings into a remediation sequence
Rank each finding by likelihood, impact, evidence, effort, and dependency. Fix immediate security and data-loss risks first, then observability and tests that make later changes safer, then structural improvements. Separate facts from assumptions so leadership knows where more evidence is required.
ElegantMind’s fixed-scope AI and architecture audit follows this process and delivers a written risk register and remediation plan. You can also use the free AI feasibility checklist before committing to a build.