AI-SGAP
AI Security Gap Analysis & Prioritization
Building a clearer path from AI security questions to actionable decisions.
Organizations are adopting AI faster than many can assess its security implications. Policies, questionnaires, technical evidence, and model-generated recommendations can each provide useful information. Turning that information into a defensible decision remains difficult.
For security leaders and teams responsible for enterprise AI, AI-SGAP is being built to connect evidence, security gaps, and remediation priorities—helping them understand what needs attention before making deployment decisions.
AI-SGAP – AI Security Gap Analysis & Prioritization – is the framework and product I am building to help close that gap.
The goal is to help organizations understand what they know about an AI system, identify where evidence or safeguards are missing, and prioritize what needs attention.
What we are building
- A structured assessment approach that connects AI security questions to the system being evaluated.
- A simpler customer intake experience that reduces unnecessary effort while preserving useful context.
- Evidence-informed analysis that makes uncertainty and missing information visible.
- Model-assisted workflows that support human judgment and accountability.
- Actionable findings that help people understand the issue and their next step.
Where we are today
AI-SGAP is in active development. The work has progressed through framework development, intake refinement, local testing, model evaluation, and the establishment of AWS staging foundations.
Current work focuses on validating the assembled product, improving operational reliability, and preparing for a controlled partner beta. Development results use synthetic scenarios; they should not be interpreted as demonstrated customer outcomes.
Why I am building in public
Security products should earn trust through clear explanations, honest limitations, and evidence of progress.
In Building AI-SGAP, I share the decisions, setbacks, and lessons behind the product – from simplifying customer intake to discovering why individually successful builds can still fail when assembled.
The series explains the work at a useful level while protecting proprietary implementation details.
Follow the build
Read the Building AI-SGAP series here and on LinkedIn.
I am also beginning conversations with prospective beta partners and pre-seed investors interested in the problem, the approach, and the work ahead.
- Entering AWS Staging: Progress, Setbacks, and a Better Engineering Process
- When the Builds Disagree: Lessons in AI-Assisted Engineering
- A Plausible Answer Still Needs Proof
- Taking the Next Step Toward AWS Deployment with Codex
- The Minimum Evidence Needed for Useful AI Assistance
- What the Beta Must Prove – and What It Cannot
- What Should a Beta Partner Actually Test?
- A Controlled Beta Is Not a Production Certification
- The Handoff Is Part of the Control
- Evidence Is Not Neutral
- A Principle Is Not Yet a Test
- Regulation Sets the Floor. Organizations Still Have to Decide.
- A Compliance Map Is Not Proof
- Explainability Must Lead to Action
- A High Score Is Not a Readiness Decision
- “Unknown” Is a Real Security State
- From an Assessment Workbook to an Executable Engine
- From Security Guidance to Usable Evidence
- You Cannot Secure “The AI” Until You Define It
- What “Minimum Viable” Means in AI Security
- Turning a Security Framework Into an Executable Product
AI-SGAP is under development. It does not certify compliance or guarantee that an AI system is secure or ready for deployment.