Authoring Clear Governance Language: How to Phrase Human Review for LLM Outputs in Financial Controls

1) Framing the Function of Human Review for LLM Outputs in Financial Control Environments

Human review is the intentional insertion of accountable human judgment into the lifecycle of Large Language Model (LLM) use. In financial institutions, human review is not merely a quality check; it is a formal control activity that mitigates identifiable risks, ensures regulatory alignment, and creates an auditable trail. To author governance language that is clear and enforceable, begin by placing human review precisely within the control environment. Use terms that map to established control frameworks and model risk policies, and make it explicit where the review sits in relation to LLM activity.

• Pre-use review: This review happens before an LLM is deployed or before a specific prompt configuration is approved for production use. Its purpose is risk-informed authorization. It evaluates model risk tiering, intended use, prohibited use cases, data flow, privacy safeguards, and segregation of duties. Pre-use review confirms that the use case has defined boundaries and that downstream reviews are not the first line of defense but part of a layered model.

• In-flight review: This review occurs while the LLM is generating or about to generate outputs that have direct impact on financial decisions, client communications, or regulatory obligations. The purpose is real-time containment. It ensures that sensitive content, policy-sensitive actions, or higher materiality outputs do not proceed without human confirmation. In-flight review often uses gating logic—machine-detectable triggers that pause workflow and route to a designated reviewer.

• Post-output review: This review happens after the LLM has produced outputs. The purpose is validation and evidence creation. Post-output review verifies that outputs meet defined criteria before being used in downstream systems or communicated externally. It also captures artifacts for recordkeeping, feeds risk monitoring, and supports incident response if deviations are detected.

Placing human review across these phases shows that it is not a single checkpoint but a distributed control strategy. It also clarifies that the scope of review is not uniform: pre-use review is expansive and policy-oriented, in-flight review is transactional and time-sensitive, and post-output review is confirmatory and evidence-focused. Your governance language should anchor each review to its function, explicitly connect it to model risk management (MRM) expectations, and state how it aligns with privacy, conduct, and financial reporting controls.

When framing the function, embed risk-alignment signals explicitly. Reference model risk tiers (e.g., Tier 1 high risk vs. Tier 3 low risk), materiality thresholds (e.g., potential impact on financial statements, client treatment, or regulatory disclosure), and the need for segregation of duties (the reviewer is not the requestor or system owner for the same transaction). Make recordkeeping part of the function, not an afterthought; specify what evidence is preserved and how it ties to audit tests. Avoid generic phrasing like “as needed” or “when appropriate.” Instead, use measurable conditions: “when PII is detected,” “when the confidence score is below threshold,” or “when the monetary value exceeds stated limit.”

2) Specifying Review Tiers, Triggers, and Roles with Unambiguous Language

Effective governance language makes three commitments: it defines the level of review, it defines what triggers the review, and it assigns accountable roles with clear boundaries. Vagueness invites inconsistent application and undermines auditability.

• Review levels: Use three clear categories.

  • Mandatory review: Required in all cases meeting defined criteria. There is no discretion to bypass without documented, pre-approved exception. Mandatory review should be tied to high materiality, client-facing communications, regulatory submissions, or Tier 1 model outputs.
  • Risk-based review: Required when risk indicators are present. Risk indicators must be stated in advance and be machine-detectable or objectively assessable (e.g., the presence of confidential data, complex legal topics, or model confidence below 0.7). This level balances efficiency with protection.
  • Exception-based review: Required when outputs deviate from expected patterns or controls are breached (e.g., policy violations, out-of-distribution prompts, or flagged anomalies). Exception-based review is reactive but still formal, with documented thresholds and routing.

• Triggers: Triggers translate risk signals into operational action. To achieve clarity, write triggers that include:

  • A measurable condition: data element, score, threshold, or classification.
  • The scope: which output types, channels, or products the trigger applies to.
  • The immediacy: whether the trigger halts processing or permits provisional use pending review.

• Roles and accountability: Assign concrete roles using organizational titles or control functions, not ambiguous labels. Each role should have authority boundaries and time commitments. Include segregation of duties explicitly: the reviewer cannot be the originator of the request or the system administrator. Define an accountable owner for the review process (e.g., First Line Control Owner) and a separate oversight function (e.g., Second Line Compliance or Model Risk). Document who may approve exceptions and under what conditions.

Unambiguous language avoids weak verbs (e.g., “should,” “may,” “consider”) for core requirements. Use “must” for mandatory actions. Replace vague modifiers (“advanced,” “significant,” “reasonable”) with specific qualifiers (“Tier 1,” “≥ $10,000 impact,” “confidence score < 0.7,” “contains PII as classified by the Data Inventory”). Avoid passive voice when assigning responsibility; name the accountable role. State timeframes precisely (“within 2 business days,” “prior to external release,” “immediately upon detection”).

This structure creates testable controls. Auditors can verify whether triggers were met, whether the assigned role performed the review within the timeframe, and whether evidence was recorded. Regulators can see that model risk tiering and materiality influence review rigor, not generic caution. Operational teams gain clarity on what to do and when to escalate.

3) Reusable Sentence Templates and Examples for Common Banking Artifacts

To support consistent governance writing, use sentence patterns that can be inserted directly into policy documents, standards, and procedures. Each sentence should specify triggers, scope, criteria, evidence, and escalation. While every institution will tailor thresholds and titles, the structure remains stable.

• Usage policies (purpose and placement):

  • “For Tier 1 and Tier 2 use cases, a pre-use human review by [Model Risk Management] must approve intended use, input data classes, prohibited prompts, and control boundaries prior to production deployment.”
  • “All client-facing communications generated or assisted by an LLM are subject to mandatory human pre-release review by [Designated Approver – Communications] to verify accuracy, completeness, and fair treatment criteria.”

• Guardrails (in-flight gating and content controls):

  • “If the LLM detects content classified as Confidential or PII per the Data Classification Standard, workflow is paused and routed for mandatory human approval by [Data Steward] prior to transmission.”
  • “If the monetized impact of the proposed action is ≥ [$X threshold] or relates to a regulated disclosure, the system must block auto-execution and require human confirmation by [Control Owner] with evidence of verification checks.”

• Risk assessments (tiering and criteria):

  • “Use cases are assigned a model risk tier based on impact, complexity, and reliance. Tier 1 outputs require mandatory in-flight and post-output human reviews; Tier 2 require risk-based in-flight review; Tier 3 require exception-based review only.”
  • “Triggers for risk-based review include confidence score < [0.7], detection of legal or regulatory subject matter tags, data lineage flags, and novelty indicators > [threshold].”

• Review workflows (roles, scope, and timeframes):

  • “The [First Line Control Owner] must complete post-output review for all LLM-generated financial analyses before they are booked or reported; review must occur within [1 business day] and include verification against source records.”
  • “The [Second Line Compliance Reviewer] must approve any exception to mandatory review requirements prior to use; approvals expire after [90 days] and are stored with the exception rationale.”

• Incident response (escalation and recordkeeping):

  • “If a human reviewer identifies a policy violation or material misstatement, the reviewer must halt use, log the incident within [24 hours] in the Risk Event Register, and escalate to [Compliance] and [Model Risk] for root-cause analysis.”
  • “Remediation plans must define corrective actions, responsible owners, due dates, and verification steps; closure requires evidence review by [Internal Audit] for incidents classified as High.”

Within these templates, include the necessary elements explicitly:

• Triggers: Use objective, machine-detectable signals (classification labels, thresholds, tags, scores) where possible.
• Scope: Clarify which outputs and channels (e.g., emails to clients, regulatory filings, internal forecasts) are covered.
• Criteria: State what the reviewer verifies (accuracy, completeness, policy alignment, conflict checks). Tie criteria to documented standards.
• Evidence: Specify the artifacts (screenshots, comparison to source data, checklists, approval stamps) and where they are stored (system, repository, ticketing tool). Include retention periods.
• Escalation: Define when and to whom escalations occur, timeframes, and interim controls (halt use, quarantine content, notify stakeholders).

When authoring sentences for different artifacts, maintain consistent terminology. For example, use the same titles for roles across policy, standard, and procedure documents. Align thresholds and tiers across the documents so that a trigger in a guardrail matches the risk assessment definition. Consistency reduces interpretation risk and speeds audits.

4) Rapid Audit-Check and Common Pitfalls to Ensure Clarity and Compliance

A rapid audit-check helps confirm that governance language is precise, testable, and aligned with regulatory expectations. Build a short, repeatable checklist that policy authors can apply before publication.

• Control purpose is explicit: The language states why the review exists (risk control, regulatory alignment) and where it sits (pre-use, in-flight, post-output).
• Review level is clear: Mandatory vs. risk-based vs. exception-based is specified. No ambiguous “should” language for core requirements.
• Triggers are measurable: Each trigger uses objective thresholds, classifications, or scores. Avoid subjective criteria without calibration.
• Roles are named and segregated: A specific role is accountable for the review. Segregation of duties is explicit. Reviewer authorization is documented.
• Criteria are verifiable: The reviewer’s tasks are defined (e.g., verify against source data, check regulatory citations, confirm client suitability language). Criteria map to existing policies and standards.
• Evidence is auditable: Required artifacts, storage location, retention period, and linkage to records management are stated. Audit can locate and test samples.
• Timeframes are defined: Reviews happen before specific events (pre-release, pre-booking) or within set business days. Escalation time limits are set.
• Escalation paths exist: Incidents are logged, routed, and tracked to closure with accountable owners and oversight sign-off.
• Alignment with model risk management: Tiering informs review rigor. High-tier outputs have stricter review. Changes to models or prompts trigger re-approval.
• Regulatory signals are embedded: Materiality thresholds, client treatment, data privacy classifications, fair disclosure, and recordkeeping requirements are referenced where relevant.

Common pitfalls often stem from soft language and missing specifics.

• Vague verbs and modifiers: “Review as appropriate,” “ensure adequate quality,” “significant risk,” “complex outputs.” Replace with “must review when [condition],” “verify against [source],” “Tier 1 risk,” “multi-jurisdictional regulatory content tag present.”
• Undefined roles: “A reviewer will check.” Replace with “The [Designated Approver – Product Control] must check.”
• No triggers: “Outputs may be reviewed.” Replace with listed, testable triggers tied to thresholds and tags.
• Weak criteria: “Check correctness.” Replace with “Reconcile figures to [system of record]; confirm calculation method matches [policy section].”
• Missing evidence: “Reviewed by John.” Replace with artifacts, timestamp, repository path, and a checklist with pass/fail outcomes.
• Broken segregation of duties: Requestor and approver are the same or within the same reporting line without mitigation. Specify independent roles.
• Unbounded exceptions: “Unless urgent.” Replace with defined emergency procedures, temporary controls, and time-limited approvals with retrospective review.
• Static thresholds: Ignoring model or business changes. Include periodic calibration and change-control triggers that re-open reviews when risk profile shifts.

To reinforce clarity and compliance, tie review language to established functions and systems. Name the systems that store evidence, the data classification taxonomy that drives triggers, and the model inventory identifiers that link use cases to risk tiers. Reference record retention requirements by policy name and duration. State that training is required for reviewers, with competency tracked and re-certified on a schedule. Explicitly prohibit self-approval and any auto-approval that bypasses defined triggers.

Finally, keep the governance language test-focused. Ask: Can an auditor select a sample of outputs, reconstruct the triggers, verify the assigned reviewer acted within the timeframe, and locate the evidence easily? If any step is ambiguous, rephrase until the action, threshold, role, and proof are unambiguous. In LLM-enabled financial controls, human review is effective when it is measurable, attributable, and preserved in records. Your authoring should make each of these characteristics visible and enforceable, so that the institution can demonstrate disciplined oversight of AI outputs under scrutiny from risk, compliance, and regulators.