UX/UI Design · Design Systems · User Research · B2B SaaS

Aurva: Empowering enterprises with database security at scale

92%

Reduction in avg. research & analysis time

2x

New companies discovered per month

Team

• Product Design Consultant (Me)

• Co-founder (CEO)

• Co-founder (COO)

• Frontend Engineer

• Head of AI & Research

What I did

• Brainstorming & Planning
• User Research

• Competitive Analysis

• UX Direction

• Design Systems

• End-to-end UI Design

Project Timeline

Feb 2024 - April 2024

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THE 'WHY'

Organizations should be completely aware of where their data is and who accesses it at scale.

As enterprises scale, data security becomes increasingly complex & vulnerable due to lack of visibility, unplanned expansion & poor access policies.

With the power of technology and automation, companies should be empowered to safeguard their most valuable asset: their data.

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Background & Context

Enterprises use databases to store large amounts of customer data.

In today's data-driven world, orgs rely heavily on databases to manage their most valuable asset: data. Companies use databases to efficiently store, organize, and retrieve large volumes of company data. Common use cases include customer information, financial records, inventory management, and employee details.


Popular database examples are MySQL, PostgreSQL, Oracle, and Microsoft SQL Server. They can handle structured data in the form of tables, as well as unstructured data like documents & media files.

Databases have scaled exponentially but their security hasn't.

Sensitive user information needs to prevented from falling into the wrong hands. Many orgs struggle to track and control the access of their data by employees & micro-services as they scale. They lack the tools to monitor database activities in real-time, enforce granular access controls, and implement robust security policies across all their data sources.

Orgs risk exposing their confidential data which can result in financial losses, reputational damage, and legal consequences. There is a growing need for a powerful and user-friendly SaaS platform that empowers admins to monitor their databases effectively.

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THE PROBLEM STATEMENT

Research & design a robust SaaS platform to monitor & control database activity

A data security SaaS platform that allows admins to monitor and control database access at scale across all data sources used by their organisation.


It should provide real-time visibility into major database activities and enable granular access control for employees & micro-services. It should also support security policies and limited-time access frameworks to mitigate data risks.

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About the Company & Product

Aurva is a seed-funded company backed by Nexus Ventures and DeVC. It is headquartered in Sunnyvale, California. It is creating a comprehensive database security platform which lets data admins of an enterprise discover sensitive data, monitor & control data access, and enforce security policies.

Get visibility into what data is sensitive across databases

Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.

Find who accesses sensitive data and when

Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.

Enforce security policies that prevent unintended accesses

Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.

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How I am solving this

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Who are we solving for?

The Security Admin

Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.

The Software Developer

Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.

The ML Engineer / Analyst

Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.

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How do they go about their investments?

Preparing a questionnaire for interviews

Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time

Reaching out to design partners

Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.

Research Insights

Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.

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Major pain-points faced by the interviewees

Security & privacy are often reporting-first jobs

Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time

Pain-point 1

Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.

Pain-point 1

Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.

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Understanding the market

Existing Tools

Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time

Market Trends

Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.

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Analysing the competitors

Identification

Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time

Research

Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.

Analysis

Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.

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Major user motivations to solve for

User Motivation #1

Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time

User Motivation #2

Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.

User Motivation #3

Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.

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Brainstorming for possible solutions

Initial Whiteboarding

Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time

Entity Relationship Diagram

Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.

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Making sense out of these entities

Information Architecture

Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time

Sitemap

Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.

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User Flows

Flow #1

Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time

Flow #2

Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.

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Iterating my way out

Flow #1

Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time

Flow #2

Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.

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Some crucial design decisions

Selected Idea #1

Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time

Discarded Idea #2

Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.

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Design Systems

Design Foundations

Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time

Components & Variants

Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.

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Feature 2

Functionality #1

Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time

Functionality #2

Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.

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Feature 2

Functionality #1

Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time

Functionality #2

Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.

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Feature 2

Functionality #1

Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time

Functionality #2

Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.

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Conclusion & Learnings

Key Takeaways & Learnings

Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time

Next Steps for the Project

Automatically processing textual financial data and surfacing relevant insights, key topics, and sentiment changes. This saves analysts hours of manual reading and allows them to quickly spot critical information to inform investment decisions.

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Impact

Impact Metrics

Discovering sentiment and insights from company activities like earnings calls. Identify key topics, management's tone, and trends over time

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Project Testimonials

Shubham Ojha

Founding Engineer at Aurva

I collaborated with Mahaveer at Aurva. He consistently delivered exceptional UI/UX design that enhanced user experiences and streamlined functionality. He led the design & product efforts for various product lines: Sensitive Data Discovery, Database Access Monitoring, Security Policies, Data Access Portal. His collaborative nature and dedication to quality makes him an invaluable asset to any team.

Get in Touch

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