SR 11-7 Model Risk for LLM Steps on Make.com

1. Problem / Context

Mid-market organizations in regulated industries are increasingly using Make.com to orchestrate business workflows—connecting core systems, SaaS tools, and now Large Language Model (LLM) steps that classify, summarize, or draft communications. The upside is real: faster case handling, fewer manual touches, and improved customer responsiveness. The risk is equally real: unbounded model outputs can drive biased or incorrect actions; prompts and responses might not be logged; and there may be no systematic detection of drift or hallucinations. Under SR 11-7 (and related guidance like OCC 2011-12 and NAIC model governance), these are model risk issues that must be governed with the same rigor as traditional statistical models.

The challenge for $50M–$300M companies is to enforce “big-bank-grade” controls without big-bank-scale teams. That means designing Make.com LLM steps and surrounding workflows with guardrails, evidence, and human-in-the-loop (HITL) checkpoints—practical controls that stand up to audit while preserving speed.

2. Key Definitions & Concepts

• Model Risk (SR 11-7): The risk of adverse outcomes due to model errors, misuse, or misinterpretation. For LLMs, this includes hallucinations, bias, data leakage, and logic drift over time.
• LLM Step (on Make.com): Any Make.com module or custom app action that sends text to an LLM (e.g., for classification, extraction, or drafting) and consumes the response to drive downstream actions.
• Intended Use: A documented description of what the LLM step is allowed to do, the data it may process, and the decisions it may influence.
• Guardrails: Safety measures such as prompt templates, safety filters, connector allowlists, output schemas, and policy checks that constrain model behavior.
• Evaluations (Evals): Test sets and acceptance thresholds (precision/recall, agreement with ground truth, toxicity filters, bias probes) to validate that an LLM step is fit for purpose.
• HITL: Human approval or review at checkpoints, especially for high-risk or low-confidence situations.
• Lineage & Logging: Traceability from prompt to response to decision, with retention time-to-live (TTL) aligned to policy.

3. Why This Matters for Mid-Market Regulated Firms

Regulated mid-market companies face the same examiners and liability as larger peers but with lean teams and constrained budgets. LLM steps embedded in Make.com can quietly become decision engines—classifying loan intents, summarizing claims, prioritizing alerts—without the visible scaffolding auditors expect. SR 11-7 and OCC 2011-12 require management to understand, approve, monitor, and control models and third-party technology. NAIC model governance expectations add pressure for insurers to document intended use, test for bias, preserve evidence, and ensure explainability.

Without purposeful controls, an automation that seemed “just a helper” can evolve into a model-driven decision process with untracked changes and opaque outcomes.

4. Practical Implementation Steps / Roadmap

1. Create a Model Inventory for Make.com LLM steps. Register every scenario where an LLM is invoked (flow name, owner, data categories, systems touched, risk tier). Capture intended use, expected input/output formats, and business impact.
2. Approval Workflow for Intended Use. Require sign-off from business, risk/compliance, and data owners before first run. Define the decision boundary: what the LLM can do autonomously vs. what needs HITL.
3. Prompt Template Versioning. Store prompt templates in a versioned repository (or Make.com variable store with version tags). Stamp each run with prompt version, model version, and configuration hash.
4. Guardrails & Connector Allowlists. Enforce safety filters and constrained output schemas (e.g., JSON with required fields). Maintain an allowlist of approved Make.com connectors for LLM interactions; block unapproved external endpoints.
5. Evals with Acceptance Thresholds. Build offline eval sets that reflect real edge cases; include bias and toxicity probes. Define minimum acceptance thresholds (e.g., ≥95% agreement on classification) and “fail closed” behavior.
6. Observability & Retention. Automatically capture prompts, responses, confidence scores, and downstream actions; assign TTL and access controls. Maintain lineage from prompt → response → decision to enable audit and root-cause analysis.
7. HITL Checkpoints. Place approvals for high-risk decisions (e.g., loan routing, claim denial) and second-review for exceptions. Trigger manual review when confidence scores drop below thresholds or when policy checks flag issues.
8. Drift/Bias Monitoring and Safe Fallbacks. Continuously monitor performance vs. baseline, detect drift or bias, and route to deterministic rules when risk is elevated. Schedule red-teaming to probe failure modes, updating eval sets with new adversarial cases.
9. Example: Insurance Claims Intake on Make.com. The flow classifies inbound emails and documents, extracts key fields, and proposes claim category and next step. A policy-as-code gate validates that outputs match schema and confidence ≥ threshold; otherwise it routes to human triage or a deterministic template. Logs capture prompt, response, model, thresholds, and final action with TTL aligned to retention policy.

[IMAGE SLOT: agentic automation workflow diagram in Make.com showing LLM classification, policy-as-code gate, HITL approval, and deterministic fallback across CRM, claims system, and document storage]

5. Governance, Compliance & Risk Controls Needed

• Policy-as-Code Gating: Enforce business rules before any LLM output can trigger high-impact actions (payments, denials, customer communications).
• Access Control and Change Management: Separate development from production; require approvals for prompt or model changes; maintain change tickets.
• Auditability: Preserve lineage, prompts/responses, confidence scores, and evaluation reports; document intended use and acceptance thresholds.
• Third-Party Risk Alignment: Map Make.com and model providers to OCC 2011-12 style third-party oversight (SLAs, incident handling, data location, breach reporting).
• NAIC Model Governance Considerations: Track fairness and explainability where relevant; record human review rationale for exceptions.
• Data Protection: Minimize PII sent to LLMs; mask or tokenize; define retention TTLs and deletion processes.

Kriv AI, as a governed AI and agentic automation partner for the mid-market, commonly implements policy-as-code gates, connector allowlists, and automated lineage capture so teams can meet SR 11-7 expectations without heavy custom engineering.

[IMAGE SLOT: governance and compliance control map showing model inventory, approval workflow, connector allowlist, prompt versioning, eval reports, and audit trail across Make.com]

6. ROI & Metrics

You don’t have to choose between control and value. The same telemetry that powers governance also drives ROI visibility.

• Cycle Time Reduction: For claims intake, triage time can move from hours to minutes by automating classification and pre-fill, while keeping human approvals for high-risk cases.
• Error and Rework Rate: Track mismatch between LLM proposals and human final decisions; target a steady decline as prompts and evals improve.
• Accuracy/Quality: Use acceptance thresholds on internal benchmarks; report monthly accuracy and confidence distribution.
• Labor Savings: Measure manual touches avoided (e.g., 30–50% fewer first-pass reviews) and redeploy analysts to exceptions and analytics.
• Payback Period: With targeted workflows (triage, summarization, extraction), many mid-market teams see payback within two to four quarters when paired with strong governance that prevents costly errors.

Example metrics from an insurance triage pilot: 55% reduction in first-touch handling time, 20% fewer misroutes, and a 35% increase in straight-through processing for low-risk categories—while maintaining HITL for denials and complex claims.

[IMAGE SLOT: ROI dashboard with cycle-time reduction, accuracy vs. threshold, error-rate trend, and HITL volume over time]

7. Common Pitfalls & How to Avoid Them

• Missing Logs and Lineage: If prompts, responses, and decisions aren’t captured, you can’t explain outcomes—enforce automatic logging with TTL.
• Uncontrolled Prompt Changes: Treat prompts as code; version them; require approvals.
• Overbroad Permissions: Use connector allowlists and scoped credentials; block unapproved endpoints.
• Skipping Evals: Validate with real-world edge cases and bias probes; define acceptance thresholds and fail-closed behavior.
• Automating Everything: Keep HITL for high-impact decisions; route low-confidence cases to humans.
• No Fallback Path: Always include deterministic fallbacks to preserve continuity during drift or incidents.

30/60/90-Day Start Plan

First 30 Days

• Inventory existing and planned Make.com flows using LLM steps; assign risk tiers and owners.
• Define intended uses, decision boundaries, and data categories for each flow.
• Stand up prompt template repositories with versioning and approval workflow.
• Establish connector allowlists, access controls, and data minimization patterns (masking/tokenization).
• Draft initial eval sets and acceptance thresholds; define logging schema and TTL.

Days 31–60

• Pilot 1–2 high-value workflows (e.g., claims triage, customer email classification) with policy-as-code gates.
• Implement HITL checkpoints and confidence thresholds; route low-confidence to humans.
• Run offline and online evals; capture metrics; iterate on prompt versions.
• Enable automated lineage capture (prompt → response → decision) and monitoring dashboards.
• Conduct initial red-team exercises; update eval sets with adversarial cases.

Days 61–90

• Scale to additional workflows; templatize guardrails, evals, and HITL patterns.
• Add drift and bias alerts with deterministic fallbacks; document runbooks.
• Formalize change management and periodic model reviews; schedule quarterly red-team.
• Align stakeholders (Ops, Risk, Compliance, IT) on metrics and reporting cadence; prepare audit-ready packets.

Kriv AI helps mid-market teams operationalize this 30/60/90 plan by providing data readiness support, MLOps practices, and governance tooling tailored to Make.com and regulated workflows—so small teams can deliver safely at scale.

9. Industry-Specific Considerations

• Fintech & Lending: Maintain adverse action rationale and audit trails; ensure fair lending tests for classification steps; restrict LLMs from generating customer-facing credit decisions without HITL.
• Insurance: Align with NAIC expectations; preserve artifacts for claim determinations; monitor for bias in claim categorization and fraud flags.
• Regulated SaaS: Treat model-driven features as governed components; document customer data handling, retention, and model change logs for enterprise clients.

10. Conclusion / Next Steps

LLM steps in Make.com can unlock meaningful efficiency in regulated operations—but only when governed as models under SR 11-7-style controls. By treating prompts as code, enforcing policy gates, validating with evals, logging lineage, and keeping humans in the loop, teams achieve speed without sacrificing trust.

If you’re exploring governed Agentic AI for your mid-market organization, Kriv AI can serve as your operational and governance backbone.