Artificial intelligence (AI) has transformed threat detection in IT security operations through data analysis. In enterprise cloud environments [1], AI plays a vital role in identifying threats. However, concerns have emerged regarding bias in AI algorithms and its effect on the fairness of security systems [2].


To address these concerns, it is crucial to conduct data collection and preprocessing practices with awareness of potential biases and a commitment to diversity and inclusivity [2]. Human monitoring is essential to ensure fairness and detect biases that may have been overlooked by technology [2]. Additionally, diversifying the sources of threat detection through multiple AI systems can minimize the impact of bias [2]. By incorporating human expertise [2], distributing risk [2], and utilizing explainable AI techniques [2], biases can be identified [2], rectified [2], and prevented [2], ensuring the integrity and reliability of AI-powered cloud security [2].


The impact of bias in AI algorithms on security systems’ fairness is a significant concern. However, by implementing the mentioned strategies, the negative effects of bias can be mitigated. Moving forward, it is essential to continue improving AI algorithms and practices to ensure fair and reliable threat detection in cloud security.