Red Sift Radar

Radar is a skilled up LLM, that transforms complex security tasks into simple, natural language conversations. It is integrated with Red Sift tools and being embedded into existing workflows, it reduces manual investigation time by up to 10x.

The projet is under NDA agreement
I’m unable to share specific details about my work on this project due to a non-disclosure agreement (NDA).

Overview

The Users

Cybersecurity teams, Security analysts,
IT administrators, Incident response teams, etc

My role and team

Product design

Tools used

Collaboration: Slack
Prototyping: Figma

Focus areas:

Problem

Security teams often face overwhelm from managing threat detection, preventing configuration drift, and classifying assets, all while struggling with limited resources and time.

Solution

Red Sift Radar was designed to bring clarity and context into the workflows teams already use. My role was to translate complex technical capabilities—like an upskilled LLM—into a focused, human-first interface. Working closely with security analysts and domain experts, I shaped interactions that surface the “so what” behind the data: clear explanations, prioritized actions, and a sense of control.
The product sits inside existing tools, using natural language to help users interpret risk, investigate faster, and catch misconfigurations early—without switching context or chasing information across tabs.

Impact and results

For Red Sift users, integrating Radar has reshaped how security work gets done. Tasks that previously required deep investigation—like tracking down email misconfigurations or validating DNS records—are now surfaced clearly, with relevant context and next steps already mapped out.

Users have reported up to a 10x reduction in manual investigation time. For instance, Jose Gomez, IT Director at General Catalyst, noted that assessing a domain's security posture now takes just two minutes, down from twenty. ​

Next case study:

PareIT

AI-driven platform that automates the extraction of critical information from medical records for medicolegal cases.

Let’s work together
Tia.sudd@gmail.com
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