Data Privacy (Local Hosting)

Written By Christopher Lee

Last updated 6 months ago

Knobase is designed from the ground up to protect student data and institutional integrity. Unlike commercial AI platforms that transmit user data to external servers, Knobase operates entirely within a locally hosted infrastructure, giving schools full control over their AI environment. This ensures that all interactions—whether academic, administrative, or behavioral—are processed securely and ethically, without exposure to third-party systems.

Knobase’s commitment to data sovereignty means your school owns every byte of information, and educators have full visibility into how AI is used.

🧠 Why It Matters
✅ Keeps student data within school jurisdiction
✅ Prevents third-party access or misuse
✅ Aligns with local privacy laws and school policies
✅ Builds trust with educators, students, and families

⚙️ How It Works

🏫 Locally Hosted Infrastructure

  • All AI models and services are deployed on regional servers (e.g., Hong Kong)

  • No data is transmitted to external providers like OpenAI, Google, or Meta

  • Built on open-source technologies such as Supabase and DeepSeek

  • PostgreSQL backend ensures secure, scalable, and offline-capable data storage

🔐 School-Controlled Access

  • Teachers and admins have full access to student interactions, reports, and analytics

  • All AI processing happens within the school’s private cloud

  • No external APIs or analytics tools are used unless explicitly approved

📊 Secure AI Operations

  • AI agents analyze student queries, documents, and feedback without exporting data

  • Reports, dashboards, and insights are generated and stored locally

  • All outputs are editable and reviewable by educators before sharing

🧑‍🏫 Ethical Oversight

  • Teachers configure tone, vocabulary level, and response style

  • Flagging system detects sensitive prompts (e.g., distress, bullying, inappropriate content)

  • Escalation protocols ensure student safety and responsible intervention

🌐 Optional Hybrid Features

  • For advanced tasks (e.g., translation, voice synthesis), closed-source models may be used

  • These are strictly anonymized and governed by ethical review protocols

  • Schools can opt-in or disable hybrid features based on privacy preferences

📌 Use Case Highlights

Feature

Benefit

Local hosting

Full data sovereignty and legal safety

School-controlled access

Ensures transparency and accountability

No third-party exposure

Prevents data leakage or misuse

Ethical flagging system

Supports student well-being and safety

Would you like this section formatted into a school-wide AI policy or included in your onboarding materials for staff and parents? I can also help draft a compliance checklist or privacy FAQ.