How to verify Underdog 1.3.8 keeps data on-device

Spotlight

PAGE: https://alison.com/course/computer-networking-local-area-networks-and-the-osi-model-revised

PROMISE: Clarify how local data stays on the device using OSI and TCP layer definitions.

WHO: Network beginners verifying local boundary claims in Underdog software.

WHYNOW: Before installing Underdog 1.3.8, ensure the local model claim holds technical weight.

DESC: Browse now online local area networks OSI model training course that includes LAN networking key features, ethernet functions and seven layers of the OSI Model.

BEFORE: Assume all routing happens locally without proof.

BEFORE: Cannot map specific hardware to data flow layers.

BEFORE: Unclear if TCP handshakes trigger external calls.

AFTER: Verify routing boundaries using defined layer functions.

AFTER: Map hardware devices to their specific network roles.

AFTER: Confirm transport behavior remains internal to the system.

GAIN: Define what Local Area Network (LAN) means to set scope limits.

GAIN: Describe how Ethernet functions to understand physical medium.

GAIN: Describe the structure and function of IP Addresses for identification.

STEP: You are unsure if packet inspection happens at the network edge. || Describe the functions of each layer of the OSI Model || Map each OSI layer to specific processing points within the local stack.

STEP: You need to verify the transport mechanism stays internal. || Describe the functions of each layer of the TCP Model || Check transport layer behaviors to ensure no external handshake occurs.

STEP: You struggle to differentiate local vs wide area routing logic. || Explain the differences between the OSI Model and the TCP Model || Identify which model applies to the specific local network topology in use.

NOTFOR: Users needing deep hardware debugging or binary packet analysis.

NOTFOR: Those seeking encryption protocol specifics beyond layer logic.

FACT: Average duration: 1.5-3 Hours (Contains Video)

FACT: CPD-accredited certificate available

FACT: Beginner level

COVERS: Having completed this course you will be able to:

COVERS: Define what Local Area Network (LAN) means

COVERS: Describe the key devices found on a LAN

COVERS: Explain how Hubs, Switches and Routers function

COVERS: Describe how Ethernet functions

COVERS: Describe the structure and function of IP Addresses

COVERS: List the seven layers of the OSI Model

COVERS: Describe the functions of each layer of the OSI Model

COVERS: List the four layers of the TCP Model

COVERS: Describe the functions of each layer of the TCP Model

COVERS: Explain the differences between the OSI Model and the TCP Model

COVERS: Computer Networking

MODULE: Introduction to Local Area Networks || Introduction to Local Area Networks | Network Terminology | Serial Data Transfer and IP Addresses | Types of Networks | Types of Computing

MODULE: Defining Networks with Osi Model || Introduction to OSI Model | OSI Layer 1 and Layer 2 | OSI Layer 3 and Layer 4 | OSI Layer 5, Layer 6 and Layer 7

What you are probably seeing

You see a list of personal emails and client files but worry that the new categorization tool might upload this content to a server.

The app claims local processing, yet you cannot confirm whether background sync or telemetry sends your inbox data outside the device.

Why it happens

Local AI tools often use background updates or cloud-based model management, which can create confusion about what data stays on the hard drive versus what moves to the cloud.

Users need explicit proof that no external connections occur during inference to trust the privacy claim for sensitive inboxes.

What changed recently

2026-10-07 · Sigil Wen (@0xSigil) Underdog 1.3.8에 이메일을 자동 분류하는 Underdog-Sort 기능이 추가됐다. 자동 답장 전에 기기 내 로컬의 빠른 결정 모델로 메일을 카테고리화하며, 개인정보를 외부로 전송하지 않는다고 설명한다. 개인 AI 에이전트의 로컬 분류·프라이버시 우선 워크플로 사례다. https://x.com/0xSigil/
Underdog-Sort keeps processing on-device for that gap:
• Fast local model categorizes emails.
• No personal data leaves the device.
• Added in Underdog version 1.3.8.
Source: bluesky

Step by step

1. Use a network monitoring tool to block all outgoing traffic and confirm the app runs without errors when offline.

2. Review the application logs to ensure no entries indicate API calls or data uploads during email processing.

3. Check the app directory for local model files to confirm the AI weights are stored on the device rather than streamed.

4. Examine the software source code or documentation to verify that no cloud dependencies are required for the categorization feature.

5. Test the installation on a virtual machine with no network access to prove the tool functions independently.

Ways to go further

Start here: Computer Networking – Local Area Networks and the OSI Model For when you understand network boundaries. Open the free course on Alison ↗

Better fit: UK GDPR Compliance: Protecting Personal Data For when you study data protection rules. Open the free course on Alison ↗

Affiliate links: CHEAPUNO may earn a commission if you buy through them, at no extra cost to you.

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