Closing the Sovereign Safety Gap: A Localized Adversarial Auditing Framework for African AI Governance
Omonivie Cynthia Jatto
The Localized Adversarial Auditing Initiative (LAAI) proposes building African-led technical audit capacity for AI systems deployed on the continent, drawing on offensive security methodology rather than purely academic evaluation design. Nigeria ranks 35th globally on Policy Capacity but only 72nd overall on the 2025 Oxford Government AI Readiness Index, a gap that holds across Sub-Saharan Africa's top-ranked countries and shows that policy design is outpacing technical verification capacity. This report presents the LAAI audit framework, a pilot case scoping for an African healthcare AI deployment, and the policy case for why technical evaluation capacity, not compute, is Africa's most realistic governance leverage point.
The project makes a strong and specific argument: Africa's AI governance gap is less about writing policy and more about building the technical ability to verify deployed AI systems. The “Sovereign Safety Gap” framing is compelling, especially because the paper connects it to Nigeria’s mismatch between policy-capacity ranking and overall AI readiness, then broadens the claim to Kenya, South Africa, and Mauritius. The LAAI framework is also well motivated: using existing offensive-security and red-teaming talent for localized AI audits feels practical and regionally grounded rather than abstract.
The strongest part of the paper is the healthcare pilot matrix. It shows the author understands that African AI safety failures may appear through local disease prevalence, underrepresented patient groups, and robustness failures from ambiguous or manipulated inputs. That makes the proposal more concrete than a generic “AI governance capacity” argument.
The main weakness is that the paper stops at scoping. No actual audit is conducted, so we cannot tell whether LAAI would uncover failures that existing model evaluations miss, whether regulators would act on the findings, or whether offensive-security methods transfer cleanly to AI evaluation. The regional evidence is also thinner than the argument suggests: Nigeria has the strongest policy-capacity evidence, while the other country comparisons rely mainly on overall readiness rankings.
To strengthen this, the next version should run one small audit on a real or open healthcare AI system, document actual failure cases, and show how findings would be handed to a regulator or institution. It should also explain more precisely which pen-testing practices map well to AI auditing and which require new methods.
Overall, this is a thoughtful and well-written governance proposal with a strong theory of change, but it needs real audit evidence to move from persuasive framework to demonstrated contribution.
This paper presents a clear and compelling policy contribution by framing Africa's primary AI governance challenge as a "Sovereign Safety Gap" between policy and the technical capacity to verify deployed AI systems. The proposal to leverage existing offensive security expertise for adversarial AI auditing is practical and well motivated, while the four-stage auditing framework provides a structured approach for operationalizing AI governance. The Nigeria case study effectively illustrates the core argument.
The main limitation is that the work remains a conceptual framework without validation through a real-world audit. The healthcare test matrix would be stronger with more Africa-specific evaluation scenarios, and the regional analysis would benefit from comparable pillar-level evidence across additional countries. A pilot evaluation of a deployed healthcare AI system and engagement with regulators would significantly strengthen the paper's practical impact.
Overall, this is a thoughtful and well-argued position paper that offers a valuable framework for advancing AI governance through technical assurance rather than policy alone.
Cite this work
@misc {
title={
(HckPrj) Closing the Sovereign Safety Gap: A Localized Adversarial Auditing Framework for African AI Governance
},
author={
Omonivie Cynthia Jatto
},
date={
},
organization={Apart Research},
note={Research submission to the research sprint hosted by Apart.},
howpublished={https://apartresearch.com}
}
