The development of AI-based systems entails a manifold of doubled-hard challenges. They are mainly due, on one side, to the technical debt of involved engineering disciplines (systems, safety, security), their inherent complexity, their yetto-solve concerns, and, on the other side, to the emergent risks of AI autonomy, the trade-offs between AI heuristics vs. required determinism, and, overall, the difficulty to define, characterize, assess and prove that AI-based systems are sufficiently safe and trustworthy. Despite the vast amount of research contributions and the undeniable progress in many fields over the last decades, a gap still exists between experimental and certifiable AIs. The present paper aims at bridging this gap "by design". Considering engineering paradigms as a basis to specify, relate and infer knowledge, a new paradigm is proposed to achieve AI certification. The proposed paradigm recognizes existing AI approaches, namely connectionist, symbolic, and hybrid, and proffers to leverage their essential traits captured as knowledge. A conceptual meta-body is thus obtained respectively containing categories for Data-, Knowledge-and Hybrid-driven. Since it is observed that research strays from Knowledge-driven and it rather strives for Data-driven approaches, our paradigm calls for empowering Knowledge Engineering relying upon Hybrid-driven approaches to improve their coupling and benefit from their complementarity.