Designed DeepScan, an AI-based pentesting tool that helps teams find security vulnerabilities through prompt-driven testing. It reduces dependency on manual workflows and makes security testing more accessible across products and teams.
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The challenge was to design a natural, prompt-based interaction that allows users to test systems using plain language, while still reflecting the seriousness and depth of real pentesting.
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The challenge was to surface this agent-driven activity in a way that feels transparent, structured, and reassuring, so users understand what’s happening without needing to know how it’s built.
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The challenge was to present findings in a way that feels credible, actionable, and trustworthy for teams making critical security decisions.
The objective was to design DeepScan as an easy-to-use AI security platform that helps teams find vulnerabilities faster using simple prompts. The experience was focused on showing what the system is doing, explaining results clearly, and enabling users to take confident security actions without needing deep security expertise.
Designed a cohesive brand experience for DeepScan, spanning its visual language and digital presence. The goal was to reflect trust, precision, and technical depth while ensuring the brand feels modern and confident in an AI-driven security space.
The identity system includes a distinctive dark theme, icons, and a logo designed specifically for DeepScan.
This brand foundation sets a consistent tone across the dashboard and website, reinforcing credibility while remaining approachable.
DeepScan is used by developers, product teams, security professionals, and founders each with different levels of security expertise. While their technical depth varies, their expectations are similar: fast access to insights, confidence in results, and clarity around system behavior. This required an experience that speaks a human language while still delivering the rigor expected from a security product.
The DeepScan website plays a critical role in building understanding and trust. Rather than acting as a promotional surface, it introduces users to how the platform operates and what to expect from AI-led security testing. The website explains how prompt-based workflows translate into scans,helping users form a mental model of the system before entering the dashboard.
Users initiate security tests by describing their intent in natural language. The system interprets these inputs and converts them into structured security actions, removing the need for scripts or complex setup while maintaining depth and accuracy.
DeepScan makes automated activity visible through clear progress indicators and structured feedback. Users can follow what is being tested, understand execution stages, and see how findings are generated—building confidence through clarity.
The live browser was designed to make automated security actions observable and understandable. By visually surfacing form interactions, payload execution, redirects, captured screenshots, and reproduced errors, users can follow how a vulnerability unfolds in real time.
Findings were designed as structured, scannable units that support quick understanding and action. Severity, proof of concept, visual evidence, payload details, reproduction steps, fix guidance, and compliance references are grouped together to reduce cognitive load and help teams move from identification to resolution without friction.
Security testing was designed to feel approachable without oversimplifying the problem. By enabling natural language inputs and guiding users through visible system actions, the experience supports both first-time users and security experts within the same flow.
Findings were structured to prioritize understanding and action. Grouping evidence and fix guidance together helped teams move from detection to resolution without navigating scattered.
Users are more confident when they can see what the system is doing. Making AI agent activity, execution steps, and real-world validation visible reduced uncertainty and helped users understand how conclusions were reached.
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