By Juliet Umeh

Deepfake technology has rapidly evolved from an academic curiosity into a powerful tool for fraud, impersonation, and large-scale deception. As organizations struggle to adapt, Olufunbi Babalola has emerged as a defining voice in how the industry confronts this threat—not by chasing attackers, but by changing how systems are built.

Olufunbi Babalola operates at the forefront of AI and cybersecurity product management, Babalola has consistently argued that deepfake crime succeeds because digital systems were never designed to assume deception at scale. Identity verification, content moderation, and authentication workflows often rely on outdated trust assumptions that synthetic media easily exploits.

His work challenges those assumptions by advocating for security-first product design and continuous AI-driven verification.

What sets Babalola apart is his ability to translate abstract security risks into tangible product decisions. Rather than framing deepfakes as an existential threat beyond control, he breaks the problem into solvable components—where AI models, system architecture, and human decision-making intersect. This product-led framing enables organizations to move from fear-driven responses to structured, repeatable defenses.

Babalola also emphasizes that deepfake-driven cybercrime is not limited to public-facing platforms. Internal systems—executive approvals, vendor onboarding, remote workforce authentication—are increasingly targeted through synthetic voice and video attacks. His work highlights how enterprises must rethink internal controls, not just customer-facing protections, to remain resilient.

By setting a clear standard for how AI security should be operationalized, Babalola has influenced how leaders across technology, finance, and cybersecurity approach synthetic identity threats. His work underscores that combating deepfake cybercrime is not about reacting faster than attackers, but about designing systems where deception has limited leverage to begin with.