Your HR copilot knows who's on medical leave. Claude is genuinely good at handling onboarding paperwork, tracking leave cases, and answering manager questions. The question is what happens the moment "answer the question" means disclosing something the law says only certain people may know — a medical reason, a disability accommodation, a disciplinary record. That's where "Claude wrote a helpful HR bot" and "a runtime enforces who's allowed to know what" stop being the same claim.
A support ticket is occasionally hostile. A sales negotiation is persuasive by design. An HR case is different again: the person asking usually has a completely legitimate reason to ask — and the law still says the agent may not tell them everything it knows. Nobody has to trick an HR copilot into an ADA or FMLA violation. A manager just has to ask a normal management question. Neither approach assumes the manager has bad intent. The real question is what stands between that Slack message and the diagnosis actually being disclosed.
The case-summary logic pulls from whatever fields exist on the record and paraphrases them for whoever asks. Nothing in a prompt reliably distinguishes "a manager's legitimate need to plan coverage" from "protected medical information the ADA and FMLA say only HR and the employee are entitled to see."
Claude can draft a helpful-sounding answer. The cloudlet checks the manager's role against the case's data classification — medical and accommodation details aren't in a manager's capability grant. The response is scoped to what a manager may know: approved leave through a date, coverage needed.
Ask any HR-AI vendor this and the marketing sounds identical — "role-based," "privacy-aware," "compliant by design." The architecture underneath does not. There are, in practice, exactly two answers.
A Magic Cloudlet exposes your HRIS and case-management system to Claude as a set of generated, role-gated endpoints — with each data field carrying a classification (operational, medical, disciplinary, compensation) rather than sitting in one undifferentiated case record. The agent's token carries a role, the same way a badge does. A manager role sees dates and coverage needs; an HR generalist role sees more; medical detail is scoped to HR and the employee, regardless of who's asking or how reasonably.
Trust boundary, left: the prompt and your guardrail code. Right: the runtime. Attack surface shrinks from every field your code can pull to only the fields this role's grant allows.
The checklist a security-conscious HR leader should put in front of a CFO before letting an AI agent answer questions against live employee records.
| # | Requirement | Claude alone, free-form | Magic Cloudlet + Claude |
|---|---|---|---|
| 1 | HRIS connection | ManualCustom integration per platform (Workday, BambooHR, ADP…). | HandledGenerated endpoints wrapping your existing system — zero migration. |
| 2 | New-hire provisioning workflow | ManualBuilt and maintained by your engineering team. | HandledGenerated as endpoints — accounts, equipment, paperwork routing. |
| 3 | Data classification (medical / disciplinary / comp) | ManualField-level tagging built and maintained by hand. | HandledA capability grant per data class, enforced at every call. |
| 4 | Role tiers (manager / HR / HRBP / legal) | ManualHand-rolled middleware, one implementation per build. | HandledRuntime-native roles — same object from token to admin screen. |
| 5 | Leave & accommodation case routing | ManualCustom workflow logic for FMLA / ADA processes. | HandledGenerated as an endpoint the correct role can call, not bypass. |
| 6 | Over-disclosure defense | ManualFilters and prompt hardening — a defense each new question tests. | HandledA reasonable-sounding question still can't exceed the capability grant. |
| 7 | Employee self-service scoping | ManualBuilt by hand — what an employee sees about their own case. | HandledA distinct role grant, same enforcement mechanism. |
| 8 | Audit trail of case access | ManualDesigned, instrumented, and wired to a log store. | HandledBuilt-in, filterable log — every access, every disclosure. |
| 9 | Onboarding document generation | ManualTemplate logic hand-built per document type. | HandledGenerated from the same validated employee data. |
| 10 | Security review before launch | ManualFull human review of every generated line. | PartialStructural checks handle validity; case-routing logic still reviewed. |
| 11 | Compliance scope (ADA, FMLA, GINA, EEOC, state leave law) | ManualOngoing, and specific to your jurisdictions. | PartialSelf-host / air-gap keeps infra in your boundary; scope mapping is still yours. |
| 12 | Hosting, deploy, rollback | ManualA DevOps project, built once a backend exists. | HandledIncluded with a managed cloudlet, or self-hosted / on-prem / air-gapped. |
| 13 | Ongoing patching & maintenance | ManualYour team, indefinitely, as each HRIS API changes. | PartialMaintained by Nuity (managed), or self-maintained under the MIT license. |
A security reviewer's real question isn't "is the model discreet." It's "what, mechanically, refuses to disclose the field this role isn't entitled to see" — no matter how legitimate the request sounds.
| Layer | Claude alone, free-form | Magic Cloudlet + Claude |
|---|---|---|
| Field vocabulary | Open — any field your integration code can read, reachable if the prompt gets there. | Closed — a finite, classified set of fields per role. |
| Pre-execution verification | Prompt hardening and application-level checks, written by hand. | A static proof against the live capability registry, before delivery. |
| Capability grants (data classification) | Not native — hand-built into the case-summary functions. | Argument-bound grants, checked by the evaluator at every dispatch. |
| Identity & role (manager vs HR vs legal) | A middleware pattern each new build may implement differently. | A runtime object — one role, from the caller's token to the admin screen. |
| Audit trail | Whatever your team designs, instruments, and maintains. | Built-in, severity-tagged log of every access and disclosure. |
Everything on the right is enforced at the moment of dispatch, by the evaluator, on every call — not by a prompt a well-meaning manager's reasonable question is free to talk past. That difference is architectural, not behavioral. It doesn't degrade as case volume, or good intentions, go up.
Self-hosted, the runtime is free and MIT-licensed. Managed, it's a flat $100/mo with one developer user included. The reference backend build runs roughly 80% fewer AI tokens than hand-building the same integration and disclosure logic from scratch — about 25,000 tokens versus 140,000. Set that against the figures below, which are the EEOC's, not ours.
Recovered by the EEOC for 17,680 workers in FY2025 — the agency's own enforcement total for the year.
Average out-of-court discrimination settlement, per the EEOC — before a case ever reaches trial.
Most common basis for EEOC lawsuits in FY2025 was disability discrimination — exactly the category a mishandled leave case falls into.
Source: U.S. Equal Employment Opportunity Commission, Fiscal Year 2025 enforcement data and public reporting on it. Figures are agency-wide totals and averages, not a prediction for any specific company, and not a Nuity AI estimate. Token and cost figures are Nuity AI's published estimates as of mid-2026, based on a reference build (two linked tables, eight CRUD endpoints, access locked to a single role). Full methodology: nuity.ai/savings-calculator. This page is not legal advice; ADA, FMLA, GINA, and state leave-law obligations vary by employer and fact pattern.
$100/month buys a hosted, role-gated HR runtime with enforced data classification and a full audit trail — a rounding error next to the $40,000 average cost of even one out-of-court discrimination settlement, before legal fees.
A case a budget approver can trust has to concede real points, not just make them.
Two pages — the checklist, the security model, the numbers, and the ask — built to forward. The ten-page version has the full walkthrough for anyone who wants the detail.
Nuity AI's engineers will scope it against your actual HRIS, your actual role structure, and this exact comparison — live, no sales deck. This use case is a proposed pattern, not yet a shipped case study; we'll show you the mechanism, not a slide.