International Journal of Sub-Saharan African Research (IJSSAR)

Background: Nigeria's digital information environment between 2020 and 2026 has been increasingly disrupted by AI-driven deepfake technology, which poses significant threats to national security, democratic governance, and public trust in institutions. Deepfakes have become more accessible, technically sophisticated, and easier to produce and disseminate. Despite these developments, scholarly research on their security implications within African political contexts remains limited.
Objective: This study examined the implications of AI-driven deepfake news for national security in Nigeria. Specifically, it investigated the role of the media in mitigating the effects of deepfake disinformation, identified the challenges confronting media organisations in combating synthetic content, and proposed a strategic media framework for addressing emerging deepfake threats.
Method: A qualitative exploratory research design was adopted. Primary data were generated through structured brainstorming sessions involving eight purposively selected participants comprising senior journalists, digital verification specialists, and political communication stakeholders with direct experience of Nigeria's 2023 general election. Secondary data were obtained from peer-reviewed literature, policy documents, and credible journalistic reports. Data were analysed thematically using Braun and Clarke's (2006) six-phase thematic analysis framework. Results: The findings reveal that AI-driven deepfake news constitutes a multidimensional threat to Nigeria's national security. Beyond facilitating deception, deepfakes generate widespread uncertainty, undermine public confidence in the media and state institutions, reinforce confirmation bias within politically polarised environments, and create the Liar's Dividend effect, whereby authentic evidence is dismissed as potentially fabricated. The study further found that Nigerian media organisations face significant capacity, technological, financial, and regulatory constraints in responding effectively to these threats.
Conclusion: Addressing the national security risks posed by AI-driven deepfakes requires a coordinated, multi-stakeholder strategy that integrates institutional deepfake detection capabilities, public media literacy initiatives, strengthened regulatory frameworks, and proactive crisis communication protocols. The agenda-setting, investigative, and fact-checking functions of the media remain indispensable for safeguarding national security in the era of synthetic media.
Unique Contribution: This study provides an empirically grounded, Africa-centred analysis of AI-driven deepfake news as an emerging national security challenge. By integrating the concepts of the Liar's Dividend, confirmation bias, and structural media capacity deficits within the Nigerian context, it extends the deepfake governance literature beyond predominantly Western perspectives. The study also offers a replicable qualitative framework for examining deepfake-related security challenges in other emerging democracies.
Key Recommendation: The study recommends a four-pillar strategic response framework comprising: (1) institutional capacity building through AI-assisted deepfake detection units; (2) sustained public media and digital literacy programmes; (3) the development and enforcement of comprehensive deepfake legislation and regulatory guidelines; and (4) transparent, proactive crisis communication protocols. Effective collaboration among media organisations, regulatory agencies, technology companies, civil society organisations, and government institutions is essential to mitigating the national security risks posed by AI-generated disinformation.