The availability of huge amounts of data in big data science and analyzing them requires computational efficiency as well aspossessing privacy and security at the same time is apparently a challenge. Convention encryption techniques are also problematical toachieve the volume, velocity and variety of the big data. Hence, this paper aims to examine the use of adaptive encryption algorithmspointing out the possibility of achieving an optimal ratio of security and performance in big data. There are types of encryption techniquesthat allow for the control of the level of protection that is being offered with reference to the amount of security needed for the particulardata and the resources available for the process. First, we discuss various adaptive encryption methods as part of prior work, includingvariable key size, adaptive encryption types, and context sensitivity. We also talk about how such algorithms can be incorporated with bigdata processing platforms such as Hadoop and Spark. In addition, we also assess the effectiveness of the developed adaptive encryptionalgorithms with different big data benchmarks. From our early finding, it can be seen that adaptive encryption challenged and outperformsstatic encryption techniques in terms of processing time with no neglect to optimum security. Also, we present the advantages,disadvantages, issues involved and threats concerning the adaptive encryption such as the keys management issue, schema evolution andpossible insecurity. At last, we propose a few avenues for further research in the area, such as proposing new types of adaptive encryptionschemes based on machine learning and data classification approaches to shed more light on the optimization of the security-performanceparadigm.Keywords: Adaptive Encryption, Big Data, Security, Performance, Data Protection