Building a product to help enterprises make better-informed AI security decisions.

Enterprises adopting AI face a practical challenge: security policies, technical evidence, questionnaires, and assessment findings often sit in separate places.

Decision-makers still need to establish:

• What evidence supports the assessment?
• Which gaps require attention?
• What remains unknown?
• What should the organization address before moving forward?

AI-SGAP – AI Security Gap Analysis & Prioritization – is the framework and product I am building to help answer those questions.

I am beginning conversations with prospective pre-seed investors.

The customer problem

Consider a bank evaluating an AI-enabled workflow.

• Security teams need to understand the system and its safeguards.
• Business leaders need visibility into unresolved issues and priorities.
• Reviewers need evidence that relates to the specific system being assessed.
• Delivery teams need findings they can translate into corrective action.

The product opportunity is to make that assessment process more structured, understandable, and repeatable.

The product direction

AI-SGAP is being developed to bring together:

• Structured customer intake and evidence collection.
• Model-assisted evidence interpretation.
• Clear visibility into gaps and uncertainty.
• Human assessment and review.
• Prioritized findings and reporting.

The objective is to support accountable decisions while reducing unnecessary assessment effort. Customer impact and commercial value remain hypotheses to validate through beta participation.

Business model

The planned model is product licensing per AI system, with partners providing supporting services.

This creates two complementary paths:

• Organizations licensing AI-SGAP to support their assessment needs.
• Partners using the product alongside implementation, assessment, and remediation services.

Pricing, packaging, and distribution assumptions will be refined through customer and partner discovery.

Progress so far

Development has included:

• Translating the framework into executable assessment workflows.
• Refining customer intake in response to practitioner feedback.
• Evaluating model assistance using synthetic evidence and scenarios.
• Developing customer and assessor interactions.
• Testing application components and deployment packages.
• Establishing AWS staging foundations and advancing validation.
• Creating build, risk, access, and recovery records to strengthen engineering accountability.

These are development milestones. They do not establish product-market fit, production readiness, or demonstrated customer outcomes.

What comes next?

The next phase focuses on:

• Validating the integrated product in a controlled hosted environment.
• Preparing and conducting bounded partner beta evaluations.
• Measuring usability, assessment usefulness, operating cost, and reliability.
• Testing willingness to pay and the per-system licensing model.
• Strengthening engineering capacity and repeatable delivery.

Founder

I am Amrita Mukherjee, a cloud and security professional with experience across Oracle, partner ecosystems, enterprise architecture, consulting, and technical solution development.

AI-SGAP brings that experience to a problem I have encountered throughout enterprise technology adoption: turning security requirements and fragmented information into decisions people can explain and act on.

Investment conversations

I welcome conversations with investors interested in enterprise security, AI governance, and early-stage product development.

Potential funding priorities include engineering capacity, product integration, controlled beta execution, and customer validation. Funding requirements and milestones can be discussed directly.

A high-level overview is available for initial discussions. Further diligence materials can be shared under appropriate confidentiality arrangements.

Start an Investor Conversation, email to amritamukherjee@proton.me

Explore the development decisions and lessons in Building AI-SGAP.