How to Review LLM Applications for Security and Compliance

A security and compliance review for a large-model application must follow the product's actual users, data, capabilities, market, and operating model. A private internal assistant, a public generative service, a regulated-industry tool, and an agent that can execute transactions do not pass through the same gate.

This guide is an engineering review framework, not legal advice. Laws, filings, platform rules, and model terms change; confirm current requirements with the relevant authorities, providers, and qualified advisers before launch.

1. Define the review boundary

Record the legal entity, target users, launch regions, access model, business sector, output types, model providers, deployment location, and whether the system only answers or can also call tools and change records. Identify public-facing generation, material influence on users, and regulated decisions early because they can change the entire review path.

2. Build a data and capability map

Trace every input, retrieval source, prompt, uploaded file, model request, output, log, evaluation dataset, and human-review step. For each item, state owner, purpose, legal or contractual basis, sensitivity, region, retention, recipients, deletion path, and access roles.

Map every tool the model can call: system, action, permission, approval, rate limit, rollback, and audit evidence. A diagram is useful only when it corresponds to the deployed system.

3. Review seven control areas

Entity, registration, filing, and assessment

Determine which entity operates the service and which current rules apply to its users and market. Confirm whether licenses, algorithm or service filings, security assessments, contractual transfer mechanisms, or sector approvals are needed. Do not claim approval before the competent party has issued it.

Data sources and intellectual property

Document the origin and permitted use of training, fine-tuning, retrieval, prompt, and evaluation material. Respect licenses, confidentiality, database rights, copyright, and provider restrictions. Create removal and source-correction paths; do not treat publicly reachable data as automatically reusable.

Personal, sensitive, and cross-border data

Minimize collection, provide the required notice and choices, protect sensitive data, and support access, correction, deletion, and consent withdrawal where applicable. Identify subprocessors and international transfers. Masking a field does not by itself remove every privacy or security obligation.

Content safety and user governance

Define prohibited use, moderation stages, generation labels where required, reporting and appeal routes, age controls, abuse detection, and incident handling. Test multilingual and multimodal inputs, prompt injection, encoded requests, and indirect content—not only obvious keywords.

Models, suppliers, and third-party components

Review provider terms, data-use settings, retention, regions, service continuity, model changes, subprocessors, security commitments, exit options, and license compatibility. Keep a component and model inventory with owners and update triggers.

Permissions, security engineering, and agent behavior

Use least privilege, tenant isolation, secret management, input and output validation, retrieval authorization, sandboxing where appropriate, confirmation for consequential actions, rate limits, idempotency, rollback, and tamper-resistant audit logs. Treat retrieved text and tool output as untrusted instructions.

User rights, human review, and sector controls

Explain material limitations and when AI is used. Provide human review for high-impact decisions and a way to challenge or correct outcomes. Medical, financial, employment, education, public-service, and safety uses may require specialist controls beyond a general AI checklist.

4. Make the review an executable launch gate

Red: do not launch with an unresolved legal requirement, unknown data source, excessive permission, exposed secret, missing tenant isolation, unsafe irreversible action, or no response path for a foreseeable severe incident.

Conditional: launch only with a named owner and deadline for lower-risk evidence, monitoring, documentation, or restricted rollout work. State the compensating control and affected users.

Improvement: track optimizations that do not invalidate the current risk decision, such as better evaluation coverage or operational efficiency.

Every finding needs evidence, owner, due date, and verification result. A checklist marked “done” without an artifact is not a review.

5. Evidence to retain before launch

• system, data-flow, and tool-permission maps;
• data and model inventories with source and terms;
• threat model and security test results;
• privacy, transfer, retention, and deletion records;
• content-safety policy and adversarial evaluation results;
• human-review, incident, rollback, and supplier-exit procedures;
• user notices, agreements, labels, reporting, and appeal paths;
• applicable filings, approvals, licenses, and client confirmations;
• signed launch decision with accepted residual risks.

6. Continue after launch

Monitor safety events, model and prompt changes, permissions, cost, latency, quality, complaints, provider policy, and legal developments. Re-run the gate for a new model, data source, tool, market, user group, material workflow, or high-impact incident. Maintain a regression set from real failures without copying personal or confidential data into an uncontrolled test corpus.

Wavesteam owns technical mapping, threat analysis, control design, implementation evidence, and recommendations within the engagement. The client confirms business facts, lawful data and qualification sources, acceptable risk, and decisions requiring legal or sector authority.