Artificial Intelligence and Cameroon's Counter-Terrorism Strategy in the Far North: A Policy Analysis

A note on scope

This paper is a desk-based policy analysis, not an empirical study. It does not draw on original interviews, government access, or classified data. Where it references the security situation, legal framework, or regional cooperation, it cites publicly available reporting. Where it discusses what AI could do, this is framed explicitly as a proposal and a synthesis of publicly documented approaches elsewhere — not as findings from Cameroonian officials or verified operational detail about how any specific country's system works internally. Readers should treat the recommendations as a starting point for further, properly resourced research, not as a finished implementation plan.

1. Introduction

Cameroon's Far North region remains one of the most active fronts in the broader Lake Chad Basin insurgency. According to research by the Institute for Security Studies (ISS Africa), the Far North recorded the highest number of Boko Haram-linked incidents of any of the eight affected Lake Chad Basin zones in 2025, with 714 reported attacks that year and 227 more between January and June 2026. Human Rights Watch has documented dozens of civilian deaths in towns such as Mozogo, Blabline, Darak, and Gouzoudou, and the group has continued to strike both civilian and military targets — including a March 2025 attack on Cameroonian forces at Wulgo that the African Union publicly condemned, and a July 2025 attack on a military camp at Tourou.

A recurring pattern in independent reporting is that Boko Haram's two main factions — ISWAP and JAS — have increasingly relied on Cameroonian commanders with local knowledge of terrain, supply routes, and cross-border trade, which lets them plan attacks and sustain logistics from inside Cameroonian territory. This suggests that the insurgency's staying power in the Far North is not simply a matter of raw troop numbers but of information: knowing where fighters are, how they move, and how they finance themselves.

This is the gap where artificial intelligence is most plausibly useful — not as a silver bullet, but as a way to make better use of the data Cameroon's security services and partners already generate (patrol reports, past incident locations, communications intercepts, satellite and drone imagery) so that scarce personnel and equipment can be directed more precisely. This paper examines that potential, the real constraints on realizing it, and what a realistic phased approach would need to include.

2. The Current Security and Institutional Context

2.1 The threat picture

Cameroon does not fight this insurgency alone. Since 1994, Cameroon has been part of the Multinational Joint Task Force (MNJTF) alongside Chad, Niger, Nigeria, and (since 2015) Benin, under the Lake Chad Basin Commission. The MNJTF is organized into four national sectors, with Cameroon's sector headquartered at Mora. The force has had real successes — the AU and LCBC reported that 2024 operations alone helped facilitate the return of thousands of displaced people — but independent analysts (International Crisis Group, ISS Africa) consistently note that its effectiveness is constrained by inconsistent troop commitments between member states, funding and procurement delays, and disputes over command integration. Militant factions have repeatedly regrouped once MNJTF units withdraw from an area.

This institutional reality matters for any AI proposal: Cameroon is not designing a national security AI system in isolation. Anything built needs to be compatible with — or deliberately kept separate from — a multinational command structure with uneven digital maturity across four countries.

2.2 The legal landscape has changed

A key premise of many earlier proposals in this space — that Cameroon has no meaningful data protection law — is now out of date. On 23 December 2024, Cameroon enacted Law No. 2024/017 relating to Personal Data Protection, making it the 38th African country with comprehensive data protection legislation. The law establishes a Personal Data Protection Authority, GDPR-influenced obligations (consent, transparency, data protection impact assessments, breach handling), and penalties for non-compliance, with a transition period running to June 2026.

This is a meaningfully different starting point than "build a legal framework from zero." The real policy question is narrower and more technical: does this general-purpose data protection law adequately anticipate security and intelligence use cases (which often carry statutory exemptions in other countries' equivalent laws), and does Cameroon need sector-specific rules — oversight of surveillance tool procurement, retention limits for biometric or communications data collected for counter-terrorism purposes, judicial or parliamentary review mechanisms — layered on top of the general law. That is a gap worth closing, but it is a different, smaller task than the original framing suggested.

3. What AI Could Plausibly Add

Four categories of AI application come up repeatedly in public discussion of counter-terrorism technology internationally, and each has a plausible (not guaranteed) analogue for the Far North:

Predictive analytics on historical incident data. Boko Haram attacks in the Far North are not randomly distributed — they cluster around certain terrain, seasons, and border-crossing points. Basic statistical and machine-learning models applied to Cameroon's own incident logs (not foreign data) could help identify which districts and time windows carry elevated risk, supporting resource allocation decisions that are currently made more on institutional memory than on systematic pattern analysis. This is the lowest-cost, lowest-infrastructure entry point, since it can be done with historical data and does not require new sensors.

Satellite and drone imagery analysis. Automated image analysis can help flag changes in known trafficking routes or unusual movement patterns for human review, reducing the burden on limited surveillance personnel. This requires reliable aircraft/satellite access and image-processing infrastructure that Cameroon would likely need to source through partnerships rather than build domestically in the near term.

Communications and open-source pattern analysis for recruitment monitoring. Boko Haram, like many insurgent groups, uses social media and messaging platforms for propaganda and recruitment. Pattern-recognition tools could help identify recruitment content earlier. This is also the application area with the sharpest civil liberties risk, since it inherently touches ordinary citizens' online activity, not just declared combatants — and is exactly the kind of use case that needs explicit legal authorization rather than being read into a general data protection law.

Decision-support for resource deployment. Rather than fully "automated response systems," a more realistic near-term goal is decision-support tools that give commanders a clearer, faster picture of where to allocate available units — leaving the actual decision with human commanders. This is both more technically achievable given Cameroon's infrastructure and more defensible from a rules-of-engagement and accountability standpoint.

4. Feasibility Constraints

Infrastructure. Reliable internet connectivity is limited and inconsistent across much of the Far North, which is precisely where real-time systems would need to operate. Any AI deployment plan has to assume intermittent connectivity as the default, not the exception — meaning tools need to work with periodic data synchronization rather than assuming constant links to central servers.

Human capital. Cameroon has a small pool of AI and data science professionals, and an even smaller pool with security-sector experience. Any credible plan needs sustained investment in training — plausibly through partnerships with Cameroonian universities and technical institutes — rather than a one-off course, since AI systems that outlive their original foreign consultants require in-country staff able to maintain and audit them.

Financing. AI deployment is capital-intensive, and Cameroon's fiscal space is limited. A phased approach that starts with lower-cost applications (historical data analysis) before moving to expensive infrastructure (real-time surveillance networks) is the financially realistic path, and also gives the country time to build the oversight and legal capacity these tools require before more invasive applications come online.

Governance and rights. Beyond the data protection law itself, effective oversight requires that surveillance and pattern-recognition tools not become a mechanism for profiling entire communities in the Far North based on ethnicity or religion, which would be both a human rights concern and, practically, counterproductive — alienating the local population whose cooperation is essential to identifying genuine threats. International and Cameroonian human rights organizations have already raised concerns about civilian harm in the existing counter-insurgency response; adding untested surveillance technology without independent oversight would compound rather than resolve that concern.

5. International Context

Predictive analytics, satellite/drone monitoring, and communications pattern analysis are used in counter-terrorism and border security programs in a number of countries, including Israel, the United States, and — closer to the region — Kenya, which has invested in border security technology along its border with Somalia. Public reporting on these programs describes their broad goals and general categories of technology, but rarely their internal technical workings, and none of them operate in conditions identical to Cameroon's Far North (population density, infrastructure, terrain, and the specific tactics of Boko Haram's factions all differ). The most defensible use of these examples is as a general indication that the categories of technology discussed above have been tried elsewhere at some scale — not as a template that can be copied directly, and not as evidence about how well any of them actually worked. A genuine comparative study would need direct engagement with those countries' security services or independent evaluations, which is outside the scope of this paper.

6. Recommendations

Legal and regulatory. Commission a focused legal review of whether Cameroon's 2024 data protection law adequately covers security and intelligence uses, and if not, draft narrow, security-specific rules (oversight body, retention limits, procurement transparency for surveillance tools) rather than treating data protection as an unaddressed gap.

Start with the lowest-infrastructure application. Begin with historical incident-pattern analysis using data Cameroon's security services already hold, rather than starting with real-time surveillance, which is the most infrastructure- and rights-intensive category.

Build human capital deliberately. Pursue structured, multi-year partnerships with Cameroonian universities and technical institutes to build a domestic pipeline of AI-literate security and technical personnel, rather than one-off training delivered by outside vendors.

Coordinate with MNJTF partners. Given that Cameroon operates within a multinational command structure, any AI tool for cross-border threat picture should be designed with interoperability (or deliberate boundaries) with Chad, Niger, and Nigeria in mind from the outset, rather than as a purely national system retrofitted later.

Independent oversight from day one. Establish an oversight mechanism — ideally involving both the Personal Data Protection Authority and an independent human rights body — before any pilot involving communications or imagery surveillance goes live, not after.

7. Conclusion

The security situation in Cameroon's Far North is serious and, on the available evidence, has not eased in 2025–2026. AI-assisted approaches to counter-terrorism have real, plausible potential to help Cameroonian security forces make more efficient use of limited resources — but the case for that potential has to rest on Cameroon's actual legal, institutional, and infrastructural starting point, not on an imagined one. Cameroon already has a data protection law; the gap is a security-specific overlay, not a law from scratch. Cameroon already operates within a multinational task force with known coordination problems; a national AI system needs to reckon with that, not ignore it. A phased, low-cost, rights-conscious approach — starting with data Cameroon already has — is both more affordable and more credible than an ambitious real-time surveillance buildout attempted all at once.

Sources consulted: Human Rights Watch (2021); International Crisis Group Cameroon and MNJTF reporting; ISS Africa reporting on Boko Haram's Cameroonian commanders (2026); African Union press releases (2025); Lake Chad Basin Commission/MNJTF joint steering committee communiqués (2024); Pollicy, Afriwise, African Law & Business, and Lex Africa legal analyses of Cameroon Law No. 2024/017 (2024–2025).