CyberOwl: AI-Assisted File Upload for Cleaner, More Complete Data

CyberOwl is a Cyber Security company headquartered in the UK, offering cyber risk monitoring and resolution for the maritime industry globally.

I led UX for a new file upload feature that helps users import large Excel-based inventories into the platform. The design focused on catching errors early and using AI to offer helpful, contextual suggestions that reduce manual effort.

Adoption of feature increased by 25% and reduced manual onboarding time for larger enterprise clients.

The Project

The Brief: Designing a smarter way to upload asset data — with early error detection and AI-powered recommendations.

For: CyberOwl

My Role: Product Designer

Tools: Figma
Quantitative Surveys; User Interviews; Wireframes; Prototyping; Iterations; UI

The challenge:

Most users maintain their OT inventory in Excel — often hundreds of assets with 15+ data fields each. Uploading this data into the platform risked surfacing dozens of confusing errors at once, overwhelming users and reducing trust in the system. We needed a way to make the upload process feel reliable, forgiving, and time-saving — not frustrating.

My role:

I led the project end-to-end, from initial user research to final interaction design. I worked closely with developers, the AI engineering lead, and our PM to ensure technical feasibility and smooth integration into the platform’s existing inventory workflows.

The research:

Interviewed users to understand how they currently manage OT asset data

  1. Identified Excel as the primary tool used to build and maintain inventories

  2. Mapped out common issues with upload attempts — including naming mismatches, missing required fields, and unfamiliar terminology

The solution:

Created an upload flow with early error detection, flagging issues like missing values or duplicate asset names before the full file is processed

  1. Introduced an AI-powered recommendation layer to:
    – Suggest likely renames for duplicate or ambiguous asset names
    – Pre-fill fields with smart inferences like manufacturer based on MAC address
    – Offer context-aware predictions (e.g. "Navigation system" → "Navigation zone")

  2. Designed a clear, focused UI for error review and field validation, guiding users to what needs attention without overwhelming them

The Solution

 
 
 
Next

CyberOwl: Customisable dashboard for threat visibility