AI Governance, Policy & Data Safety Review
Buyer outcome
A practical AI usage rules and data safety review for Taiwan engineering and product teams — written governance covering which AI tools are approved, what data employees may share with model-connected systems, and what approval controls apply. Delivered as a usable internal guide. Not legal advice and not a 個資法 compliance certification.
Why governance-first AI rules matter for Taiwan teams
When engineers, product managers, or support staff use AI tools without clear rules, sensitive data can leave the business undetected — source code, customer records, API credentials, internal architecture documents, or business logic shared with public AI services that may retain or train on inputs.
This review focuses on uncontrolled use of AI coding assistants with source code and customer data, sensitive data exposure through public AI tools and Copilot-style integrations, unclear approval rules for AI-generated code or content that enters production, vendor and third-party AI tool risk where data region and retention are unclear, and absence of written usage rules for model-connected development workflows. Taiwan engineering teams commonly use Cursor, GitHub Copilot, and ChatGPT with internal codebases — this review establishes written boundaries for that practice.
BilgeQor's AI services are built around written scope, confirmed data boundaries, permission-limited tool access, human approval nodes, and documented oversight. We do not provide legal advice or 個資法 compliance certification.
Scope drivers
Ideal for
- Taiwan product and engineering teams where developers or staff already use AI tools
- SaaS companies handling customer data, source code, internal documents, or API credentials
- Teams preparing for client security questionnaires or enterprise procurement reviews
- Management and security leads who need practical written AI usage rules
- Organisations that want controlled adoption with clear permission and data boundaries
What is included
- AI usage pattern review
- Approved, restricted, and prohibited use-case guidance
- Sensitive data and source code handling guidance
- Human approval and escalation matrix
- Vendor and AI tool risk checklist
- Employee-facing AI usage rules
- Misuse escalation path
- Written AI governance summary
What is not included
- Legal advice
- Formal compliance certification or audit
- 個資法 compliance certification or regulator filing
- ISO, SOC, or regulatory certification
- Employee surveillance programme
- DLP, IAM, or monitoring tool deployment
- Full enterprise risk management programme
Delivery process
Current AI usage intake
Confirm departments, tools in use, data exposure types, and stakeholders in writing before review work begins.
Data and workflow risk mapping
Map sensitive data types, current AI usage patterns, approval gaps, and model-connected workflow risk across in-scope teams.
Policy and control drafting
Draft approved, restricted, and prohibited use cases, plus the approval matrix and employee-facing AI usage rules.
Walkthrough and adoption guidance
Written governance summary and a walkthrough call covering rollout, escalation path, and review cadence.
Representative deliverable
AI Usage Policy + Data Safety Checklist
All AI services are request-first. Scope is confirmed in writing before any payment, deposit, or implementation commitment.