Practitioner-led research into the structural gap between AI regulatory intent and organisational implementation, and the product security governance failures that create the same accountability gap from a different direction. Outputs are designed to be operationally useful, not only academically significant.
Where does AI regulation fail to translate into organisational practice, and what are the structural reasons for that failure?
And where does product security governance break down in ways that produce the same accountability gap: risk accepted indefinitely, exposure accumulated silently, defensible evidence absent when it matters? The focus in both cases is the implementation gap: what prevents well-designed governance from producing the outcomes it intends.
How AI product accountability is currently distributed across product, engineering, legal, and compliance functions, and where that distribution creates unowned risk.
How AI regulatory obligations are communicated from legal and compliance functions to product teams, and whether current translation mechanisms produce actionable requirements or compliance theatre.
How organisations currently implement post-deployment AI monitoring, what is measured, at what cadence, by whom, and with what escalation authority, mapped against regulatory obligations.
How multi-agent AI architectures challenge existing accountability attribution models, specifically the boundary between model provider, operator, deployer, and end user in agentic deployment contexts.
How risk acceptance governance breaks down in product organisations, where vulnerabilities are systematically deprioritised, attack chains go uncorrelated, and the gap between reported compliance posture and actual exposure widens. Examines the structural conditions that produce defensible risk management versus compliance theatre.
Practitioner resource library live: roadmap, tool directory, and governance templates. Articles published across two series.
Series 1 (AI Accountability in Regulated Technology) complete. Series 2 (Product Security in AI Agentic Development) active.
Research funding requests in preparation. Doctoral programme active: literature review, coursework, and Inforte seminars underway at University of Vaasa.
Publishing findings openly with practitioner-facing summaries and regulatory briefs.
Independent and self-funded.
This research is conducted remotely alongside a senior business analysis role in financial technology. The author is actively seeking a remote Product Owner or Product Manager engagement in AI, fintech, or regulated technology: work that would align with and sustain this research programme long-term.
research.4igov.cloud@gmail.com