Defects in requirements specifications can have severe consequences during the software development life cycle. Some of them result in overall project failure due to incorrect or missing quality characteristics such as security. There are several concerns that make security difficult to deal with; for instance, (1) when stakeholders discuss general requirements in (review) meetings, they are often not aware that they should also discuss security-related topics, and (2) they typically do not have enough expertise in security. This often leads to unspecified or ill-defined security aspects. These concerns become even more challenging in agile development contexts, where lightweight documentation is typically involved. The goal of this dissertation is to design and evaluate an approach to support reviewing security-related aspects in agile requirements specifications of web applications. The designed approach considers user stories and security specifications as input and relates those user stories to security properties via Natural Language Processing (NLP). Based on the related security properties, our approach then identifies high-level security requirements from the Open Web Application Security Project (OWASP) to be verified and generates a focused reading technique to support reviewers in detecting defects. We evaluate our approach via two controlled experiment trials. We compare the effectiveness and efficiency of novice inspectors verifying security aspects in agile requirements using our reading technique against using the complete list of OWASP high-level security requirements. The (statistically significant) results indicate that using our approach has a positive impact (with very large effect size) on the performance of inspectors in terms of effectiveness and efficiency.