Case Study · 02 — Prevalent AI

Navigator —
AI Assistant

Navigator emerged as a response to the remaining cognitive gap after introducing relationship-based exposure views. An AI engine layered onto the platform to reduce reliance on complex navigation and manual investigation.

AI/UX Conversational Design Cybersecurity Mobile
Navigator AI — app on iPhone
ROLE Design Lead Product Designer
TIMELINE 6 Weeks Sep 2025 – Oct 2025
PLATFORM Responsive AI Assistant
INDUSTRY Cybersecurity Enterprise
01 What Is Navigator

Navigator is an AI engine layered onto the Exposure Management platform, designed to reduce reliance on complex navigation and manual investigation. Built on top of the underlying Knowledge Graph, it enables users to query, interpret, and act on exposure data through natural interactions.

The experience focuses on clarity, explainability, and trust, ensuring AI responses are grounded in verifiable platform data and connected relationships rather than opaque summaries.

The core risk was that opaque AI summaries could undermine trust in an already complex exposure model.

6 Weeks
4+ User types
0 Autonomous actions (by design)
02 Problem Statement

Even with a connected exposure model in place, users still struggled to translate complex relationships into clear answers and actions, especially under time pressure.

Even after Exposure Management

Interpretation was still hard

Dashboards still required expertise

Non-technical users couldn't extract answers without help

03 Objectives

Project Goals

  1. Reduce friction in accessing exposure insights

    Enable users to retrieve relevant exposure information without navigating multiple complex views or dashboards.

  2. Make AI outputs understandable and trustworthy

    Ensure responses are grounded in platform data and can be interpreted and validated by users.

  3. Support both technical and non-technical users

    Allow varied user profiles to engage with exposure data without lowering accuracy or oversimplifying outcomes.

Design Priorities

  1. Explainability over automation

    AI responses are designed to show context, sources, and relationships rather than delivering opaque conclusions.

  2. Guided interaction, not free-form chat

    The experience balances natural language input with structured prompts and follow-up actions to keep users oriented.

  3. Seamless integration with existing workflows

    Navigator complements existing platform views, allowing users to move between AI-assisted insights and traditional exploration without context loss.

04 Target Users — Users & Scope

Security Leadership
(CISO, Head of Security)

  1. Clear, defensible exposure narrative

    Navigator helps leaders understand what the real exposure is and why it matters, without wading through technical detail.

  2. Faster decision-making under pressure

    High-level questions can be answered quickly, supporting prioritization, communication, and executive decisions.

  3. Confidence in AI-assisted insights

    Responses are grounded in platform data and relationships, allowing leaders to trust and stand behind the insights they act on or present.

Security Operations
& Analysts

  1. Quicker investigation without dashboard-hopping

    Navigator reduces the need to jump between dashboards by answering contextual questions in one place.

  2. Better prioritization of day-to-day work

    Exposure insights are tied to assets and controls, helping teams focus on what needs attention first.

  3. Less reliance on tribal knowledge

    The assistant helps surface context that usually lives in experienced team members' heads, making work more consistent across the team.

Interface
prevalent.ai / navigator
Navigator · Scoped to platform data only
Knowledge Graph
What are the critical findings affecting the Finance BU right now?

There are 3 critical findings linked to Finance BU assets. The highest-priority is an unpatched RCE vulnerability on api.gateway, connected to 2 compliance controls currently failing in PCI-DSS.

ASM · api.gateway CCM · PCI-DSS 6.3 Graph · Finance BU
Suggested
Navigator

Key Design Decisions

We carefully constrained AI to protect trust in a high-stakes system
Context-Aware AI Responses
Decision
  • Navigator was designed to operate strictly within the exposure management platform and its knowledge graph.
Rationale
  • Keeping the AI grounded in known assets, controls, and relationships reduces irrelevant answers and prevents misleading conclusions.
Explainable Responses by Default
Decision
  • AI outputs include supporting context and references instead of delivering standalone conclusions.
Rationale
  • Visibility into how answers are formed helps users trust and validate insights, especially for high-impact security decisions.
Guided Interaction Over Open Chat
Decision
  • The experience supports users with suggested questions and follow-up actions.
Rationale
  • This lowers the effort required to interact with AI and makes the assistant usable for both technical and non-technical users.

What we Paused

  1. 01 No autonomous actions
  2. 02 No free-form, unbounded chat
  3. 03 No AI-generated conclusions without references
Trust Spectrum

Where Navigator sits today

Explain → Recommend Assist → Execute Full Autonomy

Navigator is intentionally positioned at the explain-and-recommend end of the spectrum. Every suggestion requires a human to act. This is a deliberate design constraint — not a technical limitation. The roadmap builds trust incrementally before expanding what the AI can do on behalf of users.

CISOs + SOC Analysts Users
Design Lead Role
6 Weeks Timeline
Next Project Exposure Management Platform →