In today’s hyper-connected world, small and medium-sized enterprises (SMEs) form the backbone of emerging economies. Yet, as these businesses undergo digital transformation, they find themselves increasingly vulnerable to cyber threats. From phishing scams and ransomware to sophisticated data breaches, cyberattacks are not just a corporate problem — they are a developmental one.
While large organisations in developed countries like the United Kingdom and the United States invest heavily in digital defence systems, SMEs in Africa, Southeast Asia, and Latin America are often left exposed, under-resourced, and ill-equipped to defend themselves. Herein lies both the challenge and the opportunity: by integrating machine learning (ML) into cybersecurity frameworks, emerging markets can level the playing field and secure their digital futures.
Machine learning offers a transformative shift in cybersecurity strategy, moving from reactive to proactive defence. Traditional antivirus and firewall systems depend on known threat signatures. They often lag, only catching threats after damage is done. ML-based solutions, on the other hand, excel at recognising patterns, predicting anomalies, and adapting to new threats in real time.
For SMEs that lack dedicated IT teams or cybersecurity infrastructure, this can be a game-changer. Consider phishing still one of the most common attack vectors for small businesses. ML-powered email filters can detect fake domains, suspicious language, and malicious attachments faster and more accurately than human oversight. In the case of malware, ML systems can recognise abnormal system behaviour, isolate suspicious files, and even block network intrusions autonomously.
Essentially, machine learning becomes a vigilant digital sentinel, scalable, tireless, and intelligent. Yet adoption remains sluggish in many parts of the Global South. Cost, awareness, and technical skill gaps are persistent barriers. While ML tools are becoming more affordable, the perception persists that they are exclusive to big tech firms or advanced markets. Additionally, SME owners may lack the knowledge to evaluate or implement such tools. Comparatively, countries like the UK and the US offer valuable models.
In the UK, the National Cyber Security Centre (NCSC) provides a free ‘Cyber Essentials’ certification that promotes basic security hygiene for SMEs. It also offers toolkits that integrate ML for threat detection, especially helpful for financial and retail businesses that face constant fraud attempts. The UK government has partnered with academia and private cybersecurity firms to develop AI-driven early warning systems tailored for smaller enterprises. Public-private collaborations like the UK’s London Office for Rapid Cybersecurity Advancement (LORCA) incubate startups focused on SME-centric solutions, from endpoint detection to behavioural analytics.
Similarly, in the USA, the Cybersecurity and Infrastructure Security Agency (CISA) regularly updates public tools and training modules, while cloud platforms like AWS and Microsoft Azure provide accessible, ML-powered security solutions embedded into SME digital products. For example, US-based institutions like MIT and Stanford partner with small tech firms to develop predictive algorithms that SMEs can integrate into low-cost security suites.
Africa and other emerging regions can adapt these models. The rise of mobile money in Kenya and Nigeria’s booming fintech space shows that ML is not alien to the continent it just needs to be scaled beyond niche applications. In Nigeria, for example, some fintech companies are already using ML to detect fraud in real time. Kenya’s Safaricom has deployed AI to monitor suspicious transaction behaviour on its M-Pesa platform.
As a cybersecurity expert, I have offered insights and recommendations on this in my recent Nigerian Tribune interview titled ‘Climate-safe future possible when governments, citizens work together to actualise green policies’. In the interview, I recommended the creation of a national cyber threat intelligence platform using AI to monitor suspicious traffic patterns, phishing attempts, and dark web activities in real time. Furthermore, David Ajibade, in The Guardian article titled ‘How local e-commerce startups can serve Nigerians better’ and Ugochukwu Charles Akajiaku, in The Punch newspaper piece titled ‘Shaping AI future’ explained how SMEs can benefit from AI, machine learning, and cybersecurity technologies. These, and more, are some ways Nigerian SMEs can leverage AI and machine learning tools to enhance security and record success in their businesses.
These localised successes prove that the technology is viable. What is lacking is broader integration and policy support. Cloud-based ML services offer a potential solution to scale adoption. Platforms like Google Cloud, Azure, and AWS now provide modular, pay-as-you-go cybersecurity tools designed for SMEs. These tools require minimal setup, auto-update based on new threats, and can be managed remotely. For SMEs with limited budgets, this reduces the barrier to entry, eliminating the need for in-house security teams or high upfront infrastructure costs.
But technology alone is not enough. This is where awareness, training, and digital literacy become essential. National digital literacy campaigns run through trade associations, banks, or telcos can drastically reduce incidents and build a security-first mindset among SME owners and their staff. Policy innovation is equally crucial. For emerging markets, governments should offer tax incentives to SMEs investing in cybersecurity tools. Furthermore, international development organisations also have a role to play.
Collaborations across academia, tech hubs, and financial institutions can further catalyse progress. Innovation labs can incubate tools specifically designed for informal retailers, rural cooperatives, or cross-border traders. Banks and telecoms can bundle ML-powered cybersecurity services into digital onboarding packages for small businesses. Media and civil society can help demystify ML by sharing relatable use cases and spotlighting local champions.
Cybersecurity should be treated as critical infrastructure on par with roads, power, or water. When SMEs are crippled by ransomware or fraud, the ripple effects hit entire communities — jobs vanish, services stall, and local economies suffer. Supporting cybersecurity innovation, especially ML-enabled protection for SMEs, should therefore be considered a priority for sustainable development and resilience.
Ultimately, cybersecurity powered by machine learning must move from the margins to the mainstream of SME development. It is no longer a luxury — it is a survival imperative. The choice for emerging markets is not whether to adopt ML for digital safety but how urgently and equitably they can make it happen. The stakes are high, but so too is the opportunity to build secure, innovative, and globally competitive small business ecosystems.
Prince Chukwuemeka, data scientist and cybersecurity expert, writes from the UK.
