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Africa’s cybercriminals are adopting AI faster than the institutions chasing them

AI was involved in 55% of cybercrime cases observed by African countries surveyed by Interpol in 2025, as criminals used the technology to produce convincing phishing messages, fabricate identities, and impersonate executives and public figures. Deepfake incidents increased sevenfold between the second and fourth quarters of 2024.

Africa’s cybercriminals are adopting AI faster than the institutions chasing them

African law enforcement agencies and cybercriminals both face the rapid adoption of AI technology, but their progress is not equal, according to Interpol's African Cyberthreat Assessment Report 2026. AI played a role in 55% of cybercrime cases observed in African countries surveyed by Interpol in 2025, with criminals using it to make phishing messages, create false identities, and impersonate executives or public figures.

Deepfake incidents surged sevenfold between the second and fourth quarters of 2024. However, police agencies are adopting AI at a much slower pace. Only 8% of surveyed intelligence analysts had advanced AI expertise, while 92% cited a lack of technical expertise as their primary hurdle in utilizing AI. Most agencies still relied on manual processes.

Interpol noted, "Criminals are now operating at machine speed," while law enforcement remains constrained by legal procedures, bureaucratic delays, and a lack of technical capacity. The disparity is amplifying the impact of AI in fraud, making common forms of fraud, like phishing and SIM-swaps, more affordable and scalable than ever before.

As generative AI tools have become more accessible, cybercriminals with minimal technical knowledge can weaponize them across the entire attack cycle, from creating phishing messages to evading detection. This imbalance creates a new front in the AI boom, as AI makes fraud cheaper and easier to scale than institutions can adapt to.

Written by urgent.news from TechCabal's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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