Email Scam Explainer Concept
Overview
Problem
Scam emails are getting harder to detect, especially as AI makes them more convincing. Most users don’t have the tools or confidence to identify what’s safe and what isn’t.
Solution
Designed a concept where McAfee scans incoming emails, flags suspicious messages, and explains why something might be a scam in plain language. The goal was to not just block threats, but help users understand them.
Impact
Developed as a concept for CES 2024, this project focused on demonstrating how AI could proactively protect users while also building trust through transparency and explanation.
Approach
The goal was to make scam detection feel understandable and trustworthy, not just automated.
Making scams easier to understand
Most tools either block emails or label them as spam without much explanation.
I focused on showing why an email is suspicious. Each flagged message includes a breakdown of the signals that triggered it, helping users understand what to look for instead of relying on a black box.
Designing for trust, not just detection
Simply flagging something as “scam” isn’t always enough.
The experience was designed to feel transparent and supportive, giving users context around each decision so they could feel more confident taking action.
Structuring the experience around user flow
The concept follows a simple flow:
- User connects their email
- System scans incoming messages
- Suspicious emails are flagged
- User sees a breakdown of why
- User decides how to handle it
Breaking it down this way keeps the experience easy to follow and avoids overwhelming the user.
Working within a fast timeline
This was a rapid concept built for CES, so speed mattered.
I worked closely with product and leadership to quickly iterate on both the experience and the story. The focus was on landing a direction that clearly communicated the value of the feature in a short amount of time.
Results
Results
This project was developed as a concept to showcase at CES 2024.
The main goal wasn’t shipping a final product, but demonstrating how AI could be used to proactively protect users while also helping them understand potential threats.
By focusing on explanation instead of just detection, the concept highlighted a more transparent approach to security and positioned the feature as both protective and educational.
