AI security governance for every business scenario
Whether you need to protect employee privacy in HR or source code in engineering, there is a ready-made policy for it.
Scenario 01 · SMB
10–150 person companies: getting past “hard to adopt, hard to trust”
Pain point:Everybody wants the productivity boost, nobody wants the governance risk — so only a handful of enthusiasts end up using the tools you paid for.
- Ready-made templates for marketing, office and daily collaboration
- Plug into Feishu, DingTalk and Coze without changing any habits
- An ROI dashboard that shows usage, active teams and hours saved
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Scenario 02 · HR & admin
AI in HR: employee privacy and PIPL compliance
Pain point:Resume screening and compensation analysis carry sensitive personal data (PII). The moment AI carries it outside the company, you are in breach.
- Automatic PII masking: ID numbers, phone numbers, bank cards and addresses are redacted before the prompt leaves
- Scope limits for HR assistants: raw resumes stay internal and cannot be pasted into public AI
- Compliance audit: who exported which resume, when, and whether it was masked
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Scenario 03 · Engineering
Engineering: stop source code and keys leaking through Qode Worker and friends
Pain point:AI pair programming is daily work now — and with it, source code, schema and production keys can walk out of the building.
- Engineering DLP policies: block full code blocks, API keys and database connection strings
- Sandbox and gateway proxy: assistants only reach the internet through your gateway
- Development audit trail: every completion and conversation traced to a person, repo and commit
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