Same control plane. Different regulatory boundaries.
Every regulated industry draws its line in a different place: PHI, material nonpublic information, privilege, controlled unclassified information. Data.co Control runs inside your infrastructure and enforces that line on every request from a user, agent, or model.
The model is not the risk. Uncontrolled access is.
Across industries, AI projects stall at the same review: security and compliance teams cannot say which data a model or agent will see, where that data will travel, or how they would prove what happened after the fact. Sending regulated records to a third-party API makes those questions harder, not easier.
Data.co starts from the boundary. Models run on hardware you control, data stays in your environment, and a deterministic policy engine decides what each identity may read, search, export, or execute before anything moves. The industry pages below describe where that boundary sits for each sector and the workflows we design around it.
The regulatory boundary, by industry.
A summary of the sensitive data class and the control pattern we lead with. Each industry page goes deeper.
| Industry | Sensitive data | Primary control pattern |
|---|---|---|
| Healthcare | PHI in EHR, imaging, notes | Minimum-necessary access per role and agent |
| Revenue cycle | Billing, claims, payer data | Agents read billing records, never clinical notes |
| Financial services | Customer PII, account and trading data | Information barriers and model risk records |
| Private equity | Deal data, LP information, MNPI | Walls by fund, deal, and team |
| Legal | Privileged and client-confidential files | Matter-level ethical walls |
| Manufacturing | Process IP, quality, supplier data | Plant-level segmentation, OT/IT separation |
| Commercial real estate | Leases, rent rolls, tenant financials | Portfolio and entity scoping |
| Government | CUI and mission data | Disconnected, sovereign operation |
One architecture, configured per industry.
Runs where your data runs
On-prem, private cloud, hybrid, or air-gapped. Prompts, documents, embeddings, and records never go to app.data.co.
Labels, not guesses
PHI, PII, financial, privileged, and custom labels are applied at ingest and used in policy conditions.
Identities for machines
Agents, models, and service accounts get their own identities, so policies can treat them differently from the people they act for.
Evidence on demand
An append-only local audit log records every request and decision, exportable for auditors and internal review.
Pick the boundary closest to yours.
Each guide covers the obligations that shape the boundary, governed workflows with example policies, typical data sources, a deployment recommendation, and the questions to ask any vendor.
Healthcare
Minimum-necessary access to PHI for clinicians, operations staff, and agents, with business associate questions answered by the deployment model.
02Revenue cycle management
Denial, appeal, and claim-status agents scoped to billing data and work queues, with clinical narrative held back.
03Financial services
Information barriers, customer-data safeguards, model inventory, and records an examiner can sample.
04Private equity
Walls by fund, deal, and clean team, plus NDA return-or-destroy handling for documents, embeddings, and summaries.
05Legal services
Matter-level ethical walls synced from conflicts systems, privilege labels, and client restrictions on reuse.
06Manufacturing
Plant and program segmentation, export-control labels, and a strict read-only posture toward operational technology.
07Commercial real estate
Scoping by property, owning entity, joint venture, and investor across leases, rent rolls, and tenant financials.
08Government
Disconnected operation, attribute-based need-to-know, signed offline updates, and evidence that stays inside the boundary.
From boundary map to a governed workflow in production.
The sequence is the same in every industry. What changes is who signs off at each step.
- 1
Map the boundary
We inventory the systems involved, the data classes they hold, and the obligations attached to each class: contracts, regulations, professional rules, and internal policy.
- 2
Choose one workflow
Pick a workflow with clear value and a clear owner, such as appeal drafting or data-room Q&A. Narrow scope keeps the first policy review short.
- 3
Write and test policy
Your team and ours write the policies in YAML, with test cases for the requests that must be allowed and the ones that must be denied. Tests gate activation.
- 4
Run a supervised pilot
Real users run the workflow with approvals on every consequential action. Security and compliance review the audit log as it fills.
- 5
Expand deliberately
New datasets, agents, and teams are added through the same pull-request process, so every widening of access has an author, a reviewer, and a test.
Questions that apply in every regulated industry.
- Where are prompts, documents, and embeddings processed and stored?
- Does any access decision depend on a language model's judgment?
- What happens to requests when the policy service is unavailable?
- Can agents be given narrower access than the users they act for?
- Can the audit trail be verified as complete and unaltered?
- Does the system run with no outbound internet connection?
- Which obligations does the vendor take on in writing, and which stay with you?
Your industry draws its line somewhere else.
Insurance, education, energy, and life sciences teams face the same questions with different labels. Tell us where your boundary sits and we will map the controls to it.
Questions buyers ask
Is each industry a different product?
No. Every solution uses the same Data.co Control platform. What changes is the classification scheme, the policy library we start from, the connectors involved, and the deployment pattern.
Does Data.co satisfy our industry's regulations on its own?
No product can do that on its own. Data.co is designed to support the access, minimization, and audit requirements common to regulated industries. Compliance depends on your policies, processes, and how the system is configured and operated.
Do you have prebuilt policies for our industry?
We provide starting-point policy templates for common patterns, such as separating billing from clinical data or walling deals by fund. They are YAML in your repository and are expected to be reviewed and adapted by your team.
Can we start with one department before rolling out further?
Yes. Most deployments begin with one governed workflow, such as a revenue cycle agent or a deal-room search assistant, and expand once the policies and audit trail have been reviewed.
We operate in more than one regulated industry. Do we need separate deployments?
Not necessarily. One deployment can carry several label schemes and policy sets. Some organizations still separate deployments by legal entity or region when contracts or regulators expect physical separation.
Who writes the policies, your team or ours?
Usually both at first. We help translate your obligations into rules and tests during the pilot; your team owns the repository afterward and approves every change through your normal review process.
See it against your own use case.
A 30-minute technical walkthrough, policy simulation on your scenarios, and a deployment recommendation for on-prem, private cloud, or hybrid.