Quadratic optimization (QO) has been studied extensively in the literature due to its applicability in many practical problems. While practical, it is known that QO problems are generally NP-hard. So, researchers developed many approximation methods to find good solutions. In this paper, we go beyond the norm and analyze QO problems using robust optimization techniques. To this end, we first show that any QO problem can be reformulated as a disjoint bi-convex QO problem. Then, we provide an equivalent adjustable robust optimization (ARO) reformulation and leverage the methods available in the literature on ARO to approximate this reformulation. More specifically, we show that using a so-called decision rule technique to approximate the ARO reformulation is interpreted as using a linearization-relaxation technique on its bi-convex reformulation problem. Additionally, we design an algorithm that can find a close-to-optimal solution based on our new reformulations. Our numerical results demonstrate the efficiency of our algorithm, particularly for large-sized instances, compared with the off-the-shelf solvers.