What controls do we need to govern LLMs safely?

1. Secure LLM Gateway

The gateway is your control point. If LLM traffic is not centralized, you cannot monitor data exposure, enforce logging, or apply redaction consistently. A gateway routes all prompts and responses through a managed layer that can inspect, filter, and record interactions.

The minimum baseline is: prompt logging, response logging, redaction of sensitive fields (PII, contracts, financials), and DLP rules that block or mask regulated data. This is also where you implement rate limits, allowlists, and model selection policies.

If you already have a proxy or API gateway, reuse it. The goal is not new tooling; it is unified control. A lightweight gateway with basic redaction and logging often delivers 80% of the risk reduction in the first sprint.

A realistic target for mid‑market teams is to route 100% of business LLM traffic through the gateway within 90 days, while blocking personal accounts for regulated workflows. This is the single control that makes audit, incident response, and compliance possible. For board-level framing, see Shadow AI risk controls.

2. AI Acceptable Use Policy

The policy is not a PDF on a shelf. It must define which tools are approved, what data classes can be used with LLMs, and which workflows require human approval. A short, actionable policy is more effective than a legal document nobody reads.

The policy should include: an approved tools list, prohibited data classes (e.g., personal data, client contracts, trade secrets) unless routed through the gateway, and clear ownership for enforcement. It also defines how employees request exceptions and how to handle third‑party providers.

Make the policy operational by linking it to onboarding and tooling. If the only approved tool is hard to access, teams will ignore the policy. If exceptions take weeks, teams will route around it. Speed and clarity are part of compliance.

When the policy is published, it should be paired with enablement: a one‑page summary, a quick training, and a searchable FAQ. Adoption is measurable: 90% of teams acknowledge the policy in the first month, and exceptions are logged with owners. This is a core building block of a data strategy that is enforceable.

3. Role‑Based Access & Human Oversight

Not all prompts are equal. Finance, legal, HR, or security prompts are high‑risk by definition. Those prompts require role‑based access controls and, in many cases, human‑in‑the‑loop review before the output is used in production or client‑facing decisions.

A practical model is a two‑tier system: low‑risk prompts are self‑service under the policy; high‑risk prompts trigger an approval workflow or require a reviewer. This gives speed where it is safe and friction where it is necessary.

Define prompt categories (public, internal, confidential) and map them to allowed models and retention rules. For example: standard prompts are retained 90 days for audit; high‑risk prompts are retained 12 months and reviewed weekly. This turns oversight into a repeatable control instead of an ad‑hoc judgment.

Add evaluation checkpoints for high‑risk use cases: accuracy tests, bias checks, and a rollback plan. These controls map directly to AI Act expectations on transparency and human oversight for high‑risk systems.

4. Risk Register & Board Reporting

A risk register makes AI governance operational. Each AI use case is logged with its data exposure, model risk, owner, and mitigation plan. This avoids the “shadow AI” problem where usage is invisible to leadership.

The register should be reviewed monthly by the CDO/CIO, and quarterly by the board or audit committee. The board does not need every technical detail; it needs a clear summary of risk exposure, incidents, and mitigation status.

This is also where you document incidents, near‑misses, and remediation actions. Over time, the register becomes your audit trail and the backbone of compliance reporting.

Implementation checklist (90 days)

• Inventory all LLM use cases and owners across teams.
• Route 100% of business prompts through a gateway with logging and redaction.
• Publish an AI acceptable‑use policy and enforce approved tools.
• Implement role‑based access and a lightweight approval flow for high‑risk prompts.
• Launch a risk register with monthly reviews and quarterly board reporting.