Enterprise AI Trust and
Compliance Architect

I did not arrive at AI compliance infrastructure from theory. I arrived here through years of building software, securing web applications, testing production AI systems, deploying cloud-native platforms, competing on enterprise AI challenges, and eventually seeing the same pattern repeat across industries.

That was the genesis of AuditGuardX, AI Compliance automation.

• AI system architecture.
• Production security testing.
• Cloud-native SaaS delivery.
• Compliance readiness.
• AI governance infrastructure.

That path wasn't planned, but the combination turned out to be unusually specific to one of the most expensive unsolved problems in enterprise AI adoption.

AI-native companies are losing deals they should be winning, not because their product fails, but because their compliance infrastructure was never built to match what their stack actually is. The product demo is no longer enough. You can build an impressive AI product, but still lose enterprise momentum when the buyer’s security team starts asking questions. Enterprise buyers now want proof that the system can be trusted, governed, secured, documented, and adopted without creating new risk.

Standard SOC 2 was written for traditional SaaS. The controls don't ask about RAG pipelines, vector databases, LLM API key management, training data provenance, or model access controls. So AI-native companies prepare for the wrong exam, and the gaps appear precisely only when an enterprise procurement team starts asking the right questions.

That is the moment deals stall.

That is the gap AuditGuardX was built to close.

In March 2025, Meeting Ledger our intelligent meeting productivity assistant built on IBM watsonx.ai, took first place at the IBM watsonx.ai Agent Challenge. Two months later, in May 2025, BrainStormX our intelligent collaborative brainstorming platform built with IBM Granite Models, took first place at the IBM Granite Lean AI Solution Challenge. Back-to-back wins, on two different IBM AI platforms. Both judged against international competition. Both built under sprint conditions.

Through BroadComms, I have designed and shipped 20+ AI-native SaaS applications across Google Cloud, Microsoft Azure, and IBM Cloud, using React, Next.js, Node.js, Python, FastAPI, Flask, and Django. These systems integrate AI cpabilities, authentication, payment workflows, Zero Trust principles, and cloud-native deployment.

In October 2025, I joined Guhuza, to help test and validate their AI-driven recruitement platform operating at a 40,000+ user scale. Through functional testing, security and performance validation across 50+ API endpoints. Automated regression testing, vulnerability assessment, aligned with OWASP Top 10 and platform security recommendations.

Before that, at AppsLogix, I worked across secure coding standards, SAST and DAST integration, CI/CD security checks, code review methodology, and web application security practices that reduce critical defects and security vulnerabilities.

The combination of compliance knowledge, AI system architecture, production security testing and hardening is what makes this work different. Most compliance practitioners have never deployed AI systems. Most AI builders have never navigated the realities of a security audit. This work requires both, and that intersection is where I operate.