Artificial Intelligence in Risk Assessment: Revolutionizing Cameroon’s Strategic Approach to Counter-Terrorism and Public Safety

Author : Nouridin Melo, PhD
University of Maroua
nouridinmelo@gmail.com

Abstract

Cameroon’s security apparatus contends with increasingly sophisticated threats, particularly from Boko Haram insurgents in the Far North. The group’s operations along key border areas, such as Amchidé, Kolofata, and Mora, reveal critical gaps in Cameroon’s traditional, human-reliant security assessments, which often lack the agility required to anticipate insurgent movements and preempt attacks. This paper critically assesses the transformative potential of artificial intelligence (AI) to fortify Cameroon’s counter-terrorism and public safety frameworks, particularly in high-risk zones. Through technologies like predictive analytics, which could model insurgent attack cycles, real-time surveillance for border monitoring, and data mining for tracking recruitment patterns in digital spaces, AI offers tools that could substantially enhance response times and accuracy.

Building on international case studies, this research proposes a phased AI adoption strategy tailored to Cameroon’s infrastructural and socio-political contexts. Initial steps would involve establishing robust data privacy and security laws to prevent misuse and foster public trust. In regions such as Fotokol and Mokolo, where Boko Haram incursions remain frequent and devastating, an AI-driven approach could facilitate real-time detection of insurgent gatherings and inform strategic resource allocation. Recommendations emphasize targeted AI training for security personnel and sustainable partnerships with technology firms, ensuring local capacity building and long-term operational resilience. By positioning AI as a central pillar of its counter-terrorism strategy, Cameroon could significantly bolster public safety in the Far North and emerge as a model of AI-integrated security for Central Africa.

Keywords : Cameroon, Boko Haram, Counter-terrorism, Artificial Intelligence, Predictive Analytics and Public Safety

Introduction

Cameroon’s security framework faces escalating threats, particularly from Boko Haram insurgencies that have destabilized regions in the Far North, such as Mozogo, Kolofata, Fotoko, Amchide, and Blangoua. In these areas, where insurgent groups exploit porous borders and challenging terrains, Cameroon’s traditional, manpower-heavy security operations have shown significant limitations (Atem & Ngambi, 2023). Security efforts in these zones often depend on human surveillance, delayed intelligence reports, and limited technological support, making it difficult to preempt and respond swiftly to the insurgents' adaptive and decentralized tactics (Mbaku, 2021). For instance, recent incursions in Mayo-Tsanaga and Mozogo, characterized by rapid, guerrilla-style attacks, underscore the need for more agile, technology-enhanced security measures (Ngoh, 2021).

In this context, artificial intelligence (AI) presents a transformative opportunity for Cameroon’s counter-terrorism and public safety efforts. Globally, AI-driven systems are redefining risk assessment by employing predictive analytics to forecast threats, real-time surveillance to monitor hotspots continuously, and data mining to uncover communication and recruitment patterns among insurgents (Jackson, 2022). If integrated into Cameroon’s security framework, AI could support an adaptive, data-driven response, enhancing both predictive and reactive capabilities across high-risk regions. For example, predictive modeling could allow authorities to anticipate Boko Haram’s movements, enabling more proactive resource allocation to vulnerable areas like Mayo-Sava and Kolofata (Tabi, 2022).

This study critically examines the potential for AI integration within Cameroon’s risk assessment apparatus, considering the infrastructural, ethical, and operational challenges. Drawing on comparative case studies from regions where AI-driven security initiatives have proven effective, it explores how Cameroon could develop a phased AI implementation strategy (Atem & Ngambi, 2023). Key recommendations include enhancing digital infrastructure, establishing robust data privacy laws to build public trust, and investing in specialized AI training for security forces, ensuring that technological advancements are ethically grounded and contextually relevant (Mbaku, 2021; Ngoh, 2021).

By developing an adaptable, AI-enabled security framework, Cameroon could significantly improve its ability to anticipate and respond to insurgent activities, reinforcing public safety and counter-terrorism capabilities in the Far North while establishing itself as a regional leader in AI-driven security solutions (Ngambi, 2023).

Literature Review

AI in Risk Assessment
Artificial intelligence (AI) has rapidly reshaped risk assessment frameworks, offering significant advances in identifying, analyzing, and mitigating security threats. Through the application of predictive analytics, machine learning, and anomaly detection, AI enables the processing of vast and complex data sets, creating opportunities to identify potential threats proactively. Studies demonstrate that AI-based systems play an integral role in national security strategies, particularly in countries like the United States, where machine learning algorithms are leveraged for predictive modeling, enabling early threat detection and real-time synthesis of surveillance data (Jackson, 2022). For Cameroon, where Boko Haram and similar non-state actors exploit both geographical and infrastructural weaknesses, such systems could provide a significant advantage, allowing security forces to detect insurgent patterns and activities that traditional methods often overlook. The capacity for real-time data synthesis offered by AI could transform Cameroon’s security environment, reducing reliance on manpower-intensive operations and making resource allocation more efficient.

Counter-Terrorism Frameworks in Africa
Literature on African counter-terrorism efforts suggests a reliance on international partnerships due to regional infrastructure and resource limitations (Ngoh, 2021). While multilateral bodies, such as the African Union (AU), play a key role in facilitating intelligence-sharing among member states, these collaborative frameworks are often reactive rather than proactive, lacking the agility to address the evolving, decentralized tactics of terrorist organizations. Despite cooperation with regional bodies such as the Multinational Joint Task Force (MNJTF) and ECOWAS, Cameroon’s counter-terrorism strategy continues to be strained by resource shortages and outdated operational methodologies (Tabi, 2022). Given the reliance on foreign intelligence and manual surveillance efforts, Cameroon's counter-terrorism apparatus struggles to adapt to rapidly evolving threats. AI integration in risk assessment and threat detection could provide the necessary innovation to bridge these gaps, allowing for a more responsive and autonomous approach. Furthermore, predictive AI technologies could significantly reduce response times by enabling local security forces to anticipate rather than merely react to potential threats.

Challenges in Cameroon’s Current Strategic Approach

Cameroon’s current counter-terrorism strategies largely rely on human intelligence and physical surveillance, which are limited in their capacity to counter sophisticated, modern-day insurgencies. The persistence of Boko Haram attacks in the Far North, despite regional alliances, demonstrates the limitations of traditional counter-terrorism methods. Boko Haram’s adaptive use of digital and local networks enables the group to exploit Cameroon’s limited technological infrastructure, making it challenging for security forces to intercept or anticipate movements, especially in remote and border regions (Mbaku, 2020). Additionally, research underscores how Cameroon’s resource constraints hinder efforts to implement technology-intensive solutions. A shift toward AI-driven risk assessment frameworks could address these gaps by providing robust, data-driven insights that streamline operations and improve responsiveness in high-risk areas. Furthermore, the deployment of predictive analytics and real-time surveillance would support a more targeted counter-terrorism approach, offering the potential to reshape the nation’s strategic framework toward a more proactive and tech-enabled posture.

By leveraging AI, Cameroon could transition from reliance on conventional intelligence to a comprehensive, data-centric model that responds effectively to contemporary security challenges in the region.

Methodology

This study adopts a qualitative research design to provide an in-depth analysis of artificial intelligence (AI) as a tool to enhance Cameroon’s counter-terrorism and public safety strategies, particularly in response to Boko Haram threats in the Far North. Data collection included structured interviews with 30 highly targeted participants: 10 senior security officials from the Ministry of Defense and the National Security Agency, 5 regional counter-terrorism operatives stationed in areas such as Maroua and Mora, 10 policymakers from the Ministry of Territorial Administration, and 5 AI specialists from local tech firms collaborating with the government on digital security initiatives. This diverse but specialized participant group was selected to ensure perspectives that span strategic policy, operational challenges, and technical feasibility, providing insights that directly address both the strategic gaps and practical opportunities in implementing AI within Cameroonian security.

Additionally, secondary data was rigorously reviewed from recent government reports, including the 2023 National Security Strategy Document and regional counter-terrorism operational assessments, as well as relevant case studies on AI-driven security frameworks used in African and global counter-insurgency contexts.

For data analysis, thematic analysis was employed to dissect participant responses into specific themes: predictive risk assessment, real-time surveillance, operational limitations, and ethical considerations for AI in public security. This approach facilitated the extraction of actionable insights and contextually relevant findings tailored to Cameroon’s security and infrastructural constraints (Atem & Ngambi, 2023). Strict ethical protocols were observed, with anonymization measures in place, given the sensitive nature of the security discussions. This methodology enables a rigorous, context-focused analysis of AI’s potential to inform Cameroon’s security framework with direct relevance to the country’s most affected regions.

Results

1. AI Applications in Risk Assessment for Counter-Terrorism and Public Safety Predictive Modeling

In Cameroon’s Far North, where Boko Haram attacks persist, predictive modeling can offer a transformative impact on security operations. Using historical and real-time data, AI-powered predictive models could forecast potential threats by analyzing patterns from previous incidents and assessing real-time social and political changes. For instance, by tracking patterns of attacks during certain seasons or in response to local events in areas like Maroua, Waza, and Mora, predictive algorithms could accurately identify high-risk periods and zones, enabling security forces to preemptively allocate resources where they are most needed (Ngoh, 2021). This level of precision could notably enhance proactive response strategies, especially in regions with limited infrastructure and difficult access.

Real-Time Monitoring and Surveillance

Cameroon’s current surveillance capabilities, particularly in isolated and rugged areas, are insufficient for continuous monitoring of insurgent activity. AI-enhanced real-time surveillance, utilizing advanced technologies such as satellite imagery, drones, and facial recognition, could significantly expand monitoring reach in strategic areas, including the volatile border zones along Nigeria. Facial recognition and anomaly detection algorithms could automatically alert authorities to suspicious activities, movements, or individuals in secured areas. Additionally, real-time tracking could help pinpoint Boko Haram’s movements across the porous border, allowing for quicker response and better control over insurgent flows. This approach would reduce the reliance on manpower alone, facilitating rapid resource allocation to evolving hotspots, thereby enhancing border and civilian security (Tabi, 2022).

Data Mining and Pattern Recognition

Insurgent groups like Boko Haram frequently use digital platforms to recruit and organize, making data mining and pattern recognition indispensable in modern counter-terrorism efforts. AI-driven data mining could analyze digital communication and online activity to detect potential security threats and recruitment networks. In Cameroon’s context, where insurgents exploit social media and other digital channels to reach vulnerable youth in the Far North, data mining could reveal recruitment tactics and identify the online presence of insurgent propaganda early. If integrated with ethical guidelines, including strong data privacy frameworks, AI could empower authorities to intervene in recruitment networks before they fully establish themselves, potentially reducing the flow of new recruits to insurgent ranks (Mbaku, 2020).

Automated Response Systems

AI-driven automated response systems offer substantial advantages in high-stakes, resource-limited environments by assisting with quick, data-backed tactical decisions. These systems could dynamically suggest optimal resource deployment based on incoming threat data, minimizing human error and enhancing response accuracy. For instance, in emergencies in remote border regions like Blangoua and Mayo-Sava, where response time is crucial, automated AI systems could guide personnel to intercept insurgent activities promptly, aligning with Cameroon's security goals of maintaining a rapid, efficient response capacity (Jackson, 2022). These AI-enabled solutions hold promise for reducing operational lag and improving crisis response precision, especially in areas where swift reaction times are vital to protect both security forces and civilians.

2. Analysis of Implementation Feasibility in Cameroon

Technical Infrastructure
Cameroon’s current technical infrastructure poses significant obstacles to a comprehensive AI-based risk assessment and counter-terrorism framework. Internet connectivity remains limited and often unreliable in rural and conflict-prone areas, particularly in the Far North where insurgency is concentrated. This lack of connectivity impedes the consistent data flow required for effective AI operations, particularly those that rely on real-time data analysis. For instance, deploying AI-powered surveillance drones in Maroua or Waza is impractical without reliable data transmission capabilities. Collaboration with local telecom providers, such as MTN Cameroon, to expand high-speed internet access could support AI deployment. Additionally, the establishment of secure, centralized data storage facilities is necessary for processing and safeguarding the sensitive data integral to AI security applications (Atem & Ngambi, 2023, p. 45).

Human Capital and Training Needs
A critical barrier to AI adoption in Cameroon’s security sector is the scarcity of trained AI professionals. The expertise gap extends beyond technical know-how to the application of AI in security-specific contexts, where nuanced understanding is crucial. Partnering with academic institutions, such as the University of Yaoundé I, to develop AI-focused curriculums could foster a pipeline of skilled professionals. In parallel, targeted training for security personnel stationed in high-risk zones is essential to equip them with the knowledge required to operate AI-driven tools effectively. This strategic human resource investment would not only support national security but could position Cameroon as a Central African leader in AI-enhanced security frameworks, offering potential for regional collaboration (Ngoh, 2021, pp. 82-83).

Financial and Economic Constraints

AI integration is capital-intensive, necessitating substantial financial outlay for infrastructure, training, and maintenance. Given Cameroon’s limited budgetary flexibility, a phased approach is recommended, prioritizing cost-effective AI applications that offer immediate benefits. For instance, initiating efforts with predictive analytics using existing data to anticipate high-risk areas represents a lower-cost entry into AI while offering significant value in targeted resource allocation. Open-source machine learning frameworks, which offer free or low-cost access, could serve as practical initial tools, allowing Cameroon to build experience with AI while managing financial risk. This incremental approach would enable measurable progress within the constraints of Cameroon’s national budget, building a foundation for future expansion (Tabi, 2022, p. 99).

Legal and Ethical Challenges

AI’s application in security, particularly surveillance, raises profound legal and ethical issues. Current Cameroonian law lacks robust data privacy protections, creating risks around misuse of personal data and the infringement of civil liberties. As AI systems gather and process large amounts of citizen data, the need for comprehensive data protection legislation becomes pressing. A framework for AI governance should include data protection, transparency, and accountability principles, safeguarding citizens’ rights. Transparency and public engagement in AI-related policymaking could mitigate concerns, promoting trust and fostering a culture of accountability. This ethical alignment would ensure that AI’s benefits are not compromised by potential rights violations, creating a balance between security needs and civil liberties (Mbaku, 2020, p. 150).

3. Case Studies and Comparative Analysis

Global Case Studies

The integration of AI for national security in countries like Israel and the United States provides concrete examples of how data-driven strategies can be adapted to Cameroonian contexts, specifically to address the complex security challenges in the Far North. Israel’s AI-powered security systems, particularly its predictive analytics along sensitive borders, highlight the utility of real-time, machine-learning models that identify anomalies based on extensive datasets and behavioral pattern recognition (Jackson, 2022, p. 52). For instance, along its Gaza border, Israel’s AI systems actively monitor patterns in movement and communication, flagging irregularities that could signal infiltration attempts or the buildup of insurgent activity. In practical terms, Cameroon could adopt a similar, though scaled, system for regions such as Mora and Waza, where Boko Haram attacks occur with unpredictable frequency and typically leverage the advantage of the region’s complex terrain.

In the United States, the Department of Homeland Security’s use of AI has expanded to include large-scale surveillance in public spaces, with an emphasis on preemptively identifying risks at events that could be potential targets. Using image recognition and predictive algorithms, AI applications monitor crowd dynamics, automatically detect suspicious behaviors, and alert law enforcement with real-time information to prevent escalation. Applying such technology within Cameroon, particularly in the Far North’s urban centers, could support local intelligence efforts. With a more extensive digital infrastructure, such as upgraded data centers and secure internet access points, Cameroon could establish integrated monitoring across high-traffic and sensitive areas, allowing for swift response coordination among its regional and national agencies.

Comparative Analysis of Similar African Nations
Kenya’s phased adoption of AI in high-risk border areas, especially along the Somali border, illustrates a regionally-relevant approach that can be incrementally applied in Cameroon. Kenya’s use of AI for real-time data analysis and predictive modeling has improved its ability to detect insurgent activities, offering valuable insights for Cameroonian counterparts who face similar threats. In the Mandera region, Kenya’s security agencies utilize AI to track movements and communication signals that often precede insurgent attacks, allowing for preemptive interventions tailored to local security dynamics (Ngoh, 2021, pp. 64-66). For Cameroon, adopting this approach in places like Kousseri and Fotokol where cross-border insurgencies frequently threaten public safety would enable better alignment of surveillance with threat zones.

The Kenyan model further demonstrates the value of partnerships with international AI providers to enhance technological capacity and access to advanced resources. Cameroon could explore similar partnerships with African and international entities, prioritizing data-sharing initiatives and personnel training through the African Union or technology-focused NGOs. However, adapting Kenya’s phased model to Cameroon requires attention to specific infrastructural gaps, such as inadequate connectivity in rural areas, limited storage facilities for sensitive data, and a lack of local expertise in AI operations. Given these constraints, Cameroon would benefit from initiating its AI applications in well-equipped urban centers like Maroua before expanding them into more remote regions, balancing immediate effectiveness with long-term scalability.

By learning from both global and regional case studies, Cameroon can adopt a layered and contextually adapted AI framework, starting with predictive modeling in high-risk zones and gradually expanding to encompass full-scale, real-time monitoring in coordination with neighboring states. This approach would enhance Cameroon’s strategic response to insurgencies, offering a path toward a sustainable, data-driven security model tailored to the country’s unique operational landscape.

Discussion

Insights from Findings
The analysis of AI applications in Cameroon’s counter-terrorism and public safety strategy highlights its transformative potential to address both tactical and operational gaps. Predictive modeling, in particular, offers an advanced means to allocate resources more efficiently by identifying high-risk areas based on historical data and real-time intelligence. Given the sporadic but deadly nature of attacks in Cameroon’s Far North, such as those in Fotokol and Amchide, AI-enabled predictive analytics could allow security forces to proactively deploy resources to vulnerable regions during peak risk periods, rather than reacting post-incident. Additionally, real-time surveillance can enable faster response times by providing an up-to-date view of emerging threats, especially in remote areas where insurgents have often taken advantage of limited oversight. These AI capabilities could elevate Cameroon’s counter-terrorism efforts from a primarily reactive posture to a more proactive, strategically informed one.

Alignment with National Objectives
Implementing AI-driven security aligns directly with Cameroon’s Vision 2035, which aims to build a stable, economically prosperous, and globally competitive nation (Atem & Ngambi, 2023, p. 56). By bolstering national security, Cameroon not only ensures the safety of its citizens but also fosters an environment conducive to sustainable development and foreign investment. The constant threat of insurgency in regions such as Maroua has discouraged economic engagement, affecting local communities and regional development. An enhanced AI-driven security framework can potentially restore investor confidence, accelerate infrastructural growth, and support tourism, which has been hampered by security concerns in the north. Moreover, AI-driven security can position Cameroon as a leading example in Central Africa, contributing to regional stability by actively participating in security collaborations and intelligence-sharing with neighboring countries also affected by insurgency, such as Chad and Nigeria.

Challenges and Limitations
However, several critical challenges underscore the necessity of a cautious, phased approach to AI integration. First, Cameroon’s financial and infrastructural limitations constrain the extent to which sophisticated AI systems can be deployed nationwide. A phased implementation, beginning with less resource-intensive AI applications such as data analysis and predictive modeling, would allow the nation to gradually build the necessary infrastructure and human capital to support more complex systems, such as real-time surveillance and automated response platforms.

Ethical concerns also warrant close attention, particularly in the realm of data privacy and citizens' rights. Surveillance technologies, while effective in security, present risks of misuse and overreach. Cameroon’s current legal framework lacks specific data privacy laws governing AI applications, raising concerns about potential infringements on civil liberties. Integrating AI responsibly requires not only the establishment of clear regulatory frameworks but also robust public oversight mechanisms. This is crucial to avoid the erosion of trust between citizens and the government, as well as to prevent the potential stigmatization of communities in high-risk areas. A phased approach would also allow Cameroonian policymakers to assess and refine ethical protocols, ensuring that AI integration respects individual rights while enhancing security.

Conclusion
In summary, the study indicates that AI’s integration into Cameroon’s security apparatus holds immense promise, with potential impacts that align with both immediate security needs and long-term developmental goals. However, realizing these benefits requires careful planning to address infrastructural, financial, and ethical challenges. A strategic, phased deployment of AI in high-risk zones supported by sound legislative frameworks would set Cameroon on a path toward a more data-driven, accountable, and regionally integrated security approach

Recommendations

Policy Recommendations
Establishing a comprehensive regulatory framework specific to AI applications in national security is an essential first step toward ethical and effective implementation in Cameroon. Given the sensitive nature of counter-terrorism operations, this framework should prioritize data protection and privacy rights, outlining strict guidelines on data usage, storage, and access. Enacting legislation that clearly defines the limits and responsibilities of AI in surveillance and data analysis will help to maintain public trust and ensure that these technologies are not misused (Tabi, 2022, p. 34). Additionally, by embedding transparency and accountability into policy, Cameroon could position itself as a leader in AI ethics within Central Africa.

AI Training Programs
Creating AI-focused training programs for security personnel in collaboration with institutions such as the University of Yaoundé I would significantly improve the readiness of Cameroonian forces to integrate and leverage AI technologies. Through specialized courses, officers would gain hands-on experience with predictive analytics, pattern recognition, and automated response tools tailored to local security needs. This approach not only enhances immediate operational capacity but also nurtures a future AI-skilled workforce in Cameroon. Establishing a strong base of local expertise is essential for long-term sustainability, reducing reliance on external consultants and fostering national ownership of AI-driven security innovations.

Infrastructure Development
To fully capitalize on AI’s potential, enhancing the technical infrastructure in Cameroon’s vulnerable regions is imperative. Expanding secure data storage and improving internet connectivity, particularly in remote areas like Maroua and Mora, would enable effective deployment of real-time surveillance and data analysis technologies. Partnering with private sector entities such as local telecom companies for these upgrades offers a practical, cost-effective strategy that can be sustained over time. By establishing reliable communication networks and data centers, Cameroon would strengthen its resilience against insurgent threats, ensuring that real-time intelligence reaches security forces promptly and that data collected is securely stored for ongoing analysis.

Conclusion

Integrating AI into Cameroon’s security strategies could markedly enhance the country’s capacity to manage regional threats, particularly in areas vulnerable to insurgencies, such as the Far North. Predictive modeling tailored to Cameroon’s conflict patterns could improve preemptive responses to Boko Haram’s activities, enabling security forces to allocate resources effectively during high-risk periods. Real-time surveillance, enhanced by local data networks, would support rapid intervention, especially in remote areas where insurgent activity often goes undetected. Moreover, data mining to track recruitment and communication patterns could be instrumental in dismantling insurgent networks before they escalate.

However, the effective adoption of AI hinges on structured, phased investments that address existing infrastructure gaps, particularly in secure data storage and internet connectivity. Legal frameworks specific to AI must be established to safeguard privacy and maintain public trust, ensuring that data protection standards are rigorously upheld. Ethical oversight is equally critical to prevent potential misuse of surveillance technologies and uphold civil liberties. Future research should prioritize exploring AI applications suited to Cameroon’s unique security landscape, ensuring solutions are not only technologically viable but also socially and ethically sound, ultimately aligning with Cameroon’s Vision 2035 for national stability and growth.

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