[Case 01]
Making Subsea Cables safer with Vibrations and AI
Security & AI
Designing a Proactive Security System for Subsea Cable Protection
Leveraging Distributed Acoustic Sensing (DAS), Real-Time Data Integration and AI to Safeguard Global Communication Networks
[Project Overview]
A prototype system for real-time subsea cable intrusion detection using DAS, AIS, satellite data and AI to help operators protect global communication infrastructure.
[Problem Statement]
Internet cable management companies are facing increasing number of attacks on subsea cables.
Cost to verify cable damage is high and it is hard to pinpoint the precise reason for damage and damage location
Current systems are more reactive then proactive, resulting in millions of dollars of avoidable damage.
[Opportunities]
Utilize new DAS technology to analise movement near the cables and pinpoint exact location of movement or damage.
Design a tool that alerts about worrying movements close to the cables allowing for a more proactive approach to avoid damage.
Utilize machine learning to understand patterns and connect Das reading to in-live behavior.
[Industry]
Security & AI
[My Role]
Senior Product Designer
[Platforms]
Desktop
[Timeline]
January 2025 - February 2025
[Persona]

Alex Carter
Network Operations Manager
I want to be able to be more proactive and prevent accidents.
Age: 38
Location: Los Angeles
Tech Proficiency: High
Gender: Male
[Goal]
Quickly identify and respond to potential threats to subsea cables before damage occurs.
Receive clear, real-time alerts with actionable information to prioritize responses.
Use stored data to analyze trends, improve future threat detection, and support liability assessments.
[Frustrations]
Uses multiple disconnected systems, making it hard to correlate data and identify threats.
Receives delayed or unclear alerts, leading to slower responses and higher repair costs.
Struggles to differentiate between false alarms and real threats.
[Process]
[Outcome]
Project green-lighted by company executives.
100% Aprooval in internal testing.
[Key Learnings]




