Project summary

Linnexus AI Shield is a cybersecurity and AI governance platform designed to prevent sensitive data leakage, enforce responsible AI usage, and improve safety and compliance across AI systems used by organizations, AI startups, and individuals.

As AI adoption grows rapidly, many users and organizations unknowingly expose sensitive data (customer records, personal identifiers, internal documents) into AI tools. Current solutions are either too technical, too expensive, or built only for large enterprises.

This project aims to build a lightweight, deployable AI safety layer that detects, blocks, and logs risky AI interactions in real time, making safe AI usage accessible beyond large corporations.

What are this project's goals? How will you achieve them?

Goals:

Prevent sensitive data leakage into AI systems

Detect personal, financial, and confidential data before it is sent to AI models.

Enable safe AI adoption for organizations and startups

Provide simple tools to monitor and control AI usage.

Build an accessible AI governance layer

Offer lightweight, affordable AI safety infrastructure for emerging markets.

How it will be achieved:

We will build a phased AI security platform:

Phase 1 (MVP)

Browser-based AI prompt protection system

Sensitive data detection engine (regex + rules-based)

Real-time warnings and auto-redaction

Phase 2

API-based AI gateway for startups

Logging and audit dashboard for organizations

Policy enforcement system

Phase 3

Expanded governance tools (risk scoring, compliance reporting)

Mobile and enterprise integrations

The core approach is to start with a simple real-time AI prompt security layer, then expand into a full governance system.

How will this funding be used?

Funding will be used to:

1. Product Development

Build MVP (browser extension + backend API)

Develop sensitive data detection engine

Create initial dashboard interface

2. Infrastructure

Cloud hosting and API services

Secure data logging system

Basic analytics infrastructure

3. User Testing & Pilot Deployment

Testing with small AI startups and organizations

Collecting feedback and refining detection accuracy

4. Research & Safety Design

Improving detection rules for AI-specific risks

Studying prompt injection and data leakage patterns

Who is on your team? What's your track record on similar projects?

The project is being led by the founder of Linnexus Digital Solutions Nig Ltd, a cybersecurity and AI-focused company.

The team has experience in:

AI training and digital skills development for students and professionals

Applied Generative AI education and implementation programs

Partnership work with education and workforce development initiatives

We are currently transitioning from training and implementation work into building AI safety infrastructure tools.

(Note: initial team is lean; technical hiring and development will expand after funding.)

What are the most likely causes and outcomes if this project fails?

Possible failure causes:

Low adoption by AI startups and SMEs due to lack of awareness of AI security risks

Technical difficulty in accurately detecting all sensitive data types

Competition from larger cybersecurity and AI governance platforms

Underestimating integration complexity with multiple AI tools

Likely outcomes if it fails:

Prototype successfully built but limited market adoption

Tool used in niche environments but not scaled widely

Insights gained about AI governance needs in emerging markets

Technology can be repurposed for cybersecurity or compliance tools

Even in failure, the project contributes valuable research and infrastructure knowledge around AI safety in underrepresented markets.

How much money have you raised in the last 12 months, and from where?

To date, the project has not raised external funding from investors or grant organizations.

It is currently in the early development stage, supported through internal resources and operational work within Linnexus Digital Solutions Nig Ltd.