As of 2021, the world economic forum deems cyber-security failures as one of the most potent threats to the world. According to a McAfee report, the cost of cybercrimes in 2020 reached nearly 1 trillion US dollars, which was around 50 percent more than what it was in 2018. Exacerbating the already mammoth financial implication of such a failure is the ever-growing diversity in cyber attacks. Side-channel analysis is one such attack type wherein the information leaked via the implementation of a cryptographic algorithm is leveraged to obtain secret data, rather than any weaknesses in the cryptographic algorithm itself. This leaked information, amongst others, can be in terms of power, EM radiation, or the time taken to perform a cryptographic operation. Countermeasures against such side-channel attacks aim at reducing the amount of information leaked via the side channels or reducing the correlation between the secret operations and the information leaked. Manufacturing the chip is often a prerequisite for evaluating the efficacy of such countermeasures, which is a costly and time-consuming process. Thus, the security evaluation of a design has a substantial impact on the design cost and the time to market. In case the design does not meet minimum security requirements, it has to be redesigned and manufactured, increasing not only costs but also the design time considerably. Hence, there is a need for pre-silicon leakage assessment tools that can provide designers a sense of certainty about the security aspects of their design. However, the existing pre-silicon leakage assessment tools are either deemed unreliable or too slow to be used to perform power leakage assessment, which is the problem this thesis aims to ameliorate. This thesis explores the use of generative adversarial networks (GANs) for generating synthetic power traces. Generative deep learning has been used in various domains like computer vision, audio, and even for medical data like ECG. GANs have been introduced in the context of side-channel attacks to enlarge the size of the profiling dataset for carrying out profiled side-channel attacks. In this work, we propose a robust methodology to condition and train GANs to generate power traces that can be used to carry out leakage assessment. This methodology can even be extended to support the design space exploration of countermeasures by providing reliable leakage assessment at design time. The generated power traces are not only indistinguishable but also as attackable as the real traces. The conditioning technique helps the GAN to generalize to various scenarios and the proposed framework provides a speed-up of around 140 times over traditional CAD methods to simulate power traces while maintaining their structure and accuracy.