In the rapidly changing world of serverless data analytics on cloud platforms, it is important that we realize how security through vulnerability assessment and patch management is not to be underestimated. The paper starts by taking a deep dive into the complexities involved in maintaining the security of serverless data analytics systems, looking at both challenges encountered and approaches used for handling these difficulties. For our research, we did an exhaustive review of methodologies, an extensive survey on existing literature, detailed discussions about implementation strategies, presentation of empirical results and insight-driven conclusions drawn from our findings; all aimed at providing useful insights for adequate vulnerability management in serverless environments. Furthermore, one immediately turns to peculiarities that arise when vulnerability assessment and patch management are performed under serverless paradigm. These include dynamicity of serverless functions, dependence on third-party services as well as architecture inherently distributed which is typical for cloud computing environment. By examining practical examples and real-world case studies, we offer best practices and emerging trends in securing serverless data analytics deployments. This will provide organizations with tangible steps of preventing risks and keeping their data assets safe within the cloud. In doing so, we aspire to equip organizations with the right knowledge and tools needed to effectively secure serverless architectures through explicating challenges, strategies as well as best practices regarding vulnerability assessment and patch management in serverless environments.