Dental payer LLM governance: enforcing policy at runtime on clinical narratives and claim attachments

What governance has to cover in this workflow

Dental attachments are mostly unstructured: provider narratives, notes that accompany radiographs, periodontal charting, and similar documents. Unstructured attachments do not hand a payer the structured data needed to auto-adjudicate a claim, which is the practical reason payers evaluate LLMs to interpret them. The same property creates the governance problem. The content is full of PHI, it varies from provider to provider, and the model reads all of it as text, including text that was never meant for the review.

Policy documents and pre-deployment reviews describe intent. They do not decide, at the moment of a given interaction, what the model may ingest, what it may return, or what it may trigger. NIST AI RMF Govern 1 asks for policies, processes and practices that are in place, transparent and implemented effectively, and Govern 1.5 asks for ongoing monitoring and periodic review with defined roles and review frequency. Runtime enforcement is the way to make those expectations operational for a model that touches PHI on every claim.

Minimum necessary and PHI redaction before the model call

HHS describes the minimum necessary standard as a key Privacy Rule protection: PHI should not be used or disclosed when it is not necessary for a particular purpose. Applying it to an LLM is an interpretation, but a reasonable one. Controlling what the model may ingest is the technical form of that principle. If the review question is whether a periodontal procedure is supported by the documented findings, identifiers and unrelated clinical history in the attachment usually do not need to reach the model.

Define, per use case, which fields are required and redact or filter the rest before the call. Tie each use of PHI to a documented basis as well. Under 45 CFR 164.506, consent under paragraph (b) does not permit a use or disclosure where a 164.508 authorization is required or another condition must be met, so the permitted purpose should be recorded rather than assumed.

Redaction has a tradeoff. Aggressive redaction can remove context the reviewer needs and degrade output quality, while permissive redaction widens exposure. Set the policy per document class, measure the effect on review outcomes, and revisit it on a defined schedule.

Setting thresholds for human approval

Escalation rules are where model output meets coverage decisions. Route to a human reviewer any case where the output could support a denial or other adverse determination, where the output cannot be tied to content in the attachment, or where earlier controls flagged the input, for example because instruction-like text was found in a narrative. A reviewer approval should be a recorded step that gates the downstream action, not a note added afterward.

No single numeric threshold applies to every payer. NIST AI RMF says that where established guidelines do not exist, organizations should define a reasonable risk tolerance. Document that tolerance explicitly: who set each escalation threshold, on what basis, and who owns its review. Govern 1.1 supports keeping a register of the legal and regulatory requirements that apply to each LLM use case, including HIPAA, NAIC expectations and applicable state rules, so thresholds can be traced to a stated requirement or a stated risk decision.

Start conservative. Escalating more cases at launch and relaxing the threshold with evidence is easier to defend than the reverse.

Where runtime enforcement tooling fits

The model above does not depend on any one product. It requires an enforcement point in the path between claim systems, models and tools, with policy that can be versioned and evidence that persists. HHS published a proposed rule in January 2025 to strengthen the Security Rule's safeguards for ePHI. Controls built against the existing safeguard standard remain useful however that proposal develops.

Trussed is one way to enforce policy in the execution path. It applies allow, redact, block, rate limit and escalate-for-human-approval decisions to individual interactions and tool calls across models, agents, tools and MCP servers, and generates audit-ready evidence from that enforcement, mapped to HIPAA, NAIC, NYDFS, the EU AI Act and NIST. It can be self-managed in the customer cloud, with configurable retention and data residency. Trussed runs in production with healthcare organizations in regulated environments.

When evaluating any option, ask which decisions are made per interaction, whether each one is recorded, how approvals gate actions, and what is shipped today versus planned.