Sensitive data risk
Teams want AI productivity, but public tools can create uncertainty around how confidential data is handled.
AI privacy readiness for regulated Canadian teams
Before your team uses AI with sensitive data, know the risks.
Narpi helps law firms, clinics, financial teams, and professional services organizations assess where AI is safe to use, where it creates privacy exposure, and what a controlled rollout should look like.
Practical first step
Privacy and workflow review
Private AI rollout plan
Built for organizations handling confidential client, patient, financial, or operational data.
Legal
Healthcare
Finance
Insurance
Professional services
The problem
AI adoption is happening before governance catches up.
Staff already want the productivity benefits of AI. The risk is letting every team choose its own path before security, privacy, and leadership can define the rules.
Privacy pressure
Quebec Law 25, healthcare privacy, and client confidentiality expectations make leaders ask where data goes, who can access it, and what gets logged.
No approved workflow
Without a sanctioned tool, teams may improvise with consumer AI services and no central audit trail.
The assessment
A practical first step before buying or building an AI platform.
Understand current AI usage
We identify where staff want to use AI, where they may already be experimenting, and which workflows carry sensitive data.
Clarify privacy and governance gaps
We look at confidentiality expectations, approval paths, logging concerns, vendor exposure, and policy readiness.
Define a safer rollout plan
You get recommended use cases, control priorities, and a path toward training, policy, or private AI infrastructure.
Why it matters
Most teams need guidance before they need infrastructure.
The assessment gives leadership a way to act quickly without guessing. It separates safe early use cases from workflows that need stronger controls, policy, training, or a private deployment.
- Identify which AI use cases are low risk, high value, or not ready yet.
- Create a clearer internal policy direction before staff improvise with public tools.
- Give partners, managers, or privacy leads a plain-language risk summary.
- Decide whether training, vendor controls, or private AI infrastructure should come next.
- Prepare evidence for leadership, privacy, security, and vendor-risk conversations.
Technical approach
Private infrastructure for teams that need stronger control.
Approved app or user
→
Private access layer
→
Narpi policy router
→
AWS-hosted model service
If the assessment shows that a private deployment makes sense, Narpi can be deployed in the client AWS environment with private networking, model allow-listing, least-privilege permissions, operational metrics, and metadata-only usage records.
Service packages
Start with readiness. Roll out private AI when the business case is clear.
AI Privacy Readiness
Current-state review, risk summary, recommended use cases, and a practical AI adoption roadmap.
Private AI Foundation
Private AI environment setup, approved access path, initial controls, and basic operating guide.
Ongoing Governance
Usage reporting, policy updates, model review, evidence packs, and recurring leadership check-ins.
Next step
Book a 20-minute AI privacy readiness call.
We’ll discuss how your team is thinking about AI, where sensitive data creates concern, and whether a readiness assessment would be useful.
Request a call