What other leaders are asking
Every serious conversation we have with a bank or health system leader opens with some version of the same handful of questions. Here's what's actually on their minds right now — and how it's getting solved.
Chief Risk Officer · regional bank
"Is this actually different from the agentic AI our board keeps reading warnings about?"
How it's getting solved
Yes, structurally. The model never acts on its own — it explains a decision a deterministic system already made. That's the architecture regulators are already steering the industry toward, not a workaround for it.
Chief Revenue Officer · community bank
"We know we're leaving revenue on the table in our commercial book — how do we find it without hiring a data science team?"
How it's getting solved
Cohort-detection scoring runs against transaction data you already have, and hands a relationship manager a ranked, sized list instead of a raw report. No new team required.
Chief Operating Officer · multi-site health system
"Our patients are frustrated waiting on hold — can AI actually fix that without touching PHI?"
How it's getting solved
Yes — de-identification happens before anything reaches the model, so the same self-service experience that cuts hold times never puts patient data at risk.
Head of Digital · national bank
"Do we need to rebuild our app, or can this just plug into what we already have?"
How it's getting solved
Both paths exist. Embed integrates directly into an existing app; Concierge App or Full Experience builds the surface for institutions that don't have one worth extending yet.
Chief Medical Officer · regional lab network
"Will this ever explain a result to a patient without a clinician reviewing it first?"
How it's getting solved
No. Anything outside a normal range is flagged for clinical review rather than explained unsupervised — the ceiling on what the model can say is set deliberately low in healthcare.
Chief Financial Officer · credit union
"How fast do we see something real, not just a pilot deck?"
How it's getting solved
Two weeks from data connection to a ranked, sized list of opportunities — run against a de-identified sample before any commitment is made.
None of these questions are really about the model. They're about whether the system around it can prove what happened and why — which is exactly the instinct regulators are converging on too. The FCA has warned UK banks that agentic AI's autonomous decision-making could expose consumers to new risk as live trials expand, and the EU AI Act's high-risk obligations reach full enforcement in August 2026. Leaders asking these questions now aren't being cautious for its own sake — they're reading the same signals we are.
Curious where your own question fits? The full due-diligence playbook has the technical version of these answers, question by question.
The due-diligence playbook
The questions banking and healthcare leaders ask before trusting conversational AI — answered in advance, so your risk and compliance team doesn't have to start from a blank page.
01
Who actually holds authority
The AI interprets and converses. Something else has to decide.
Does the AI ever validate, authorize, or execute on the institution's behalf?
No. The conversational layer interprets intent and manages dialogue; validation, authorization, execution, and recordkeeping stay inside the core banking or clinical system, independent of what the model produces.
What stays inside the core system regardless of how the AI is deployed?
Transaction state, account or patient authority, and the system of record itself. Nothing about the delivery model — embedded, app, or fully managed — changes who holds that authority.
Who resolves disagreement about what the AI told a customer?
Every response is generated from, and traceable to, a decision the core system already made — so a dispute resolves against that record, not against the model's own account of itself.
02
What the model never sees
Sensitive values are removed before they ever reach a prompt.
Does the model ever see access tokens, credentials, PINs, OTPs, or biometric data?
No. Identity, authentication factors, and entitlements remain in a trusted channel and banking or clinical context. The model receives sanitized text only — guardrails redact sensitive values before the model and log boundaries.
Does the model ever see raw patient identifiers or clinical records?
No. The same pattern applies in healthcare — de-identification or tokenization happens before anything reaches the model, and re-identification happens only inside the covered entity's own secure environment.
What's redacted before the model, versus before logging?
Both boundaries are enforced separately and explicitly — sensitive values are stripped before the model sees them, and a second redaction pass applies before anything is written to a log, so neither surface accumulates raw sensitive data over time.
03
How model output is treated on the way to execution
The model proposes. It never gets to dispose.
Is LLM-generated output treated as trusted input to downstream systems?
Never. LLM-generated arguments are treated as untrusted input, the same way you'd treat a value submitted from a public web form.
How are tool calls validated before they execute?
The tool boundary resolves references server-side and applies schema validation, entitlement and limit checks, explicit confirmation, idempotency, and audit — before anything executes.
What happens if the model produces a schema-invalid or unauthorized request?
It's rejected at the boundary before execution. A rejected call is logged like any other event in the audit trail — it never silently retries with elevated privileges.
04
Infrastructure and data residency
Where this runs, and where the data actually lives.
Cloud, on-prem, or hybrid — how is that decided?
We recommend starting with a cloud-hosted pilot to validate model choice, concurrency, latency, and recovery objectives before committing to on-prem hardware, since infrastructure sizing depends on workload data you don't have on day one.
Is customer or patient data stored in a vector database?
No. Transactions, balances, statements, and patient records are never stored in or retrieved from a vector database — a vector store isn't part of that flow. The backend returns only explicit, masked responses scoped to the current interaction.
What's the retention and encryption policy?
Retention, masking, and encryption are documented per data class, with KMS-backed key management and residency rules agreed before production deployment — not inferred after the fact.
05
Confidence and escalation
What happens when the model isn't sure.
What happens when the model's confidence is low?
Safety is enforced through confidence-aware, scoped intent routing. Low-confidence requests trigger a clarification question or present the most likely options instead of guessing.
Can a user jump between workflows mid-session?
No. Once a workflow starts — a funds transfer, a care-plan change — the user can't jump into another workflow, preventing intent mixing and preserving the execution sequence a reviewer would need to trace.
When does a human take over?
If a request can't be resolved safely within the scoped intent set, it escalates to a human operator rather than being force-fit into an existing workflow.
06
Delivery and commercial model
A separate decision from the technical one — treated as such.
Is this a full platform build, a framework we extend, or a managed service?
Treat the delivery model as a separate, explicit decision. Define scope, capability ownership, responsibilities, service boundaries, and acceptance criteria up front, for whichever of the three paths fits.
Who owns what after go-live?
Ownership is named per component — model tuning, integration maintenance, and incident response each have a named owner, rather than an assumed default.
What are the acceptance criteria for a pilot?
Defined success criteria are agreed before the pilot starts — typically a measurable outcome within a set window — so scope generalizes from evidence, not from a demo.
07
Auditability and governance
Controls that demonstrate themselves, not just claim to exist.
Can a regulator reconstruct a single interaction end-to-end?
For any prompt, a regulator can reconstruct who asked, what was redacted, which intent fired, which handler ran, and what the system returned — in one query.
What does each control map to?
Every requirement maps to a concrete artifact a reviewer can inspect — a deployment manifest, a model registry entry, a signed turn-log — not a line in a policy document. See the framework mapping tool for a jurisdiction-specific view.
What's the incident response process?
A per-module kill switch and a documented escalation runbook, tested on a regular drill cadence rather than assumed to work when needed.
RFP checklist
The same 21 questions, condensed — paste directly into your own vendor review.
- Does the AI ever validate, authorize, or execute?
- What stays inside the core system?
- Who resolves disputes about AI responses?
- Does the model see credentials or tokens?
- Does the model see raw PHI/PII?
- What's redacted before vs. after logging?
- Is model output trusted by default?
- How are tool calls validated pre-execution?
- What happens on an invalid model request?
- Cloud, on-prem, or hybrid — decided how?
- Is data stored in a vector database?
- What's the retention/encryption policy?
- What happens on low model confidence?
- Can workflows mix mid-session?
- When does a human take over?
- Full build, framework, or managed service?
- Who owns what post-launch?
- What are pilot acceptance criteria?
- Can a single interaction be reconstructed?
- What does each control map to?
- What's the incident response process?
Where this maps to your regulator
A representative spread — see the interactive framework mapping tool for a view scoped to your own jurisdiction and vertical.
| Domain | Representative frameworks |
|---|---|
| Data protection & privacy | GDPR · CCPA/CPRA · HIPAA |
| Banking & tech risk | DORA · FFIEC · PRA/FCA · MAS TRM · OSFI · APRA |
| AI-specific regulation | EU AI Act |
| Payments & data security | PCI DSS |
| Healthcare | HHS/OCR · HITRUST |
Regulatory framework mapping
Pick your vertical and jurisdiction. See exactly which controls apply, how Palladium implements them, and what evidence artifact backs each one.
Data protection & privacy
GDPR · CCPA/CPRA · HIPAA
Consent management, PII/PHI redaction before model access, data residency controls.
Deployment manifest + consent registry
Banking & technology risk
DORA · FFIEC · PRA/FCA · MAS TRM · OSFI · APRA
Model risk management, operational resilience, third-party risk documentation.
Model registry + change log
AI-specific regulation
EU AI Act
Risk classification, explainability, human-oversight requirements for high-risk use.
Audit ledger query
Payments & data security
PCI DSS
Segmentation and tokenisation of payment data anywhere it touches the model layer.
IAM policy export
Healthcare privacy
HHS/OCR · HITRUST
Business associate agreements, de-identification standards, patient-data safeguards.
BAA registry + de-identification validation report
Regional data residency
SAMA · PDPL · regional banking regs
In-region hosting, staged model rollout, versioned refresh and rollback drill.
Deployment manifest
The view above is scoped to your selection. The full cross-jurisdiction matrix — every domain, framework, and evidence artifact — is available as a spreadsheet.
Download full matrixArchitecture & data-flow diagrams
The trust architecture, diagrammed. Forward this to your own security or infrastructure review — no sales conversation required first.
Diagram 01
Trusted session flow
Diagram 02
Confidence-aware intent routing
Diagram 03
Controlled data access
Diagram 04
Cloud-first validation path
Which delivery model fits?
Three questions. One recommendation — Embed, Concierge App, or Full Experience.
Question 1 of 3