We give a simple technique for verifying the Restricted Isometry Property for random matrices that underlies Compressed Sensing. First, we show an intimate linkage between the CS theory, classic results on JL lemma. Second, we exploit this linkage to provide simple proofs for a fundamental CS construct, the so-called Restricted Isometry Property (RIP). In particular, we show how the elementary concentration of measure inequalities for random inner products used in proving the JL lemma together with simple covering arguments provide a simple and direct avenue to obtain core results for CS. The concentration inequalities are easy to verify for standard probability distributions such as Bernoulli, Gaussian, and other distributions.