The developments in the nascent field of artificial-intelligence-of-things (AIoT) relies heavily on the availability of high-quality multi-dimensional data. A huge amount of data is being collected in this era of big data, predominantly for AI/ML algorithms and emerging applications. Considering such voluminous quantities, the collected data may contain a substantial number of outliers which must be detected before utilizing them for data mining or computations. Therefore, outlier detection techniques such as Mahalanobis distance computation have gained significant popularity recently. Mahalanobis distance, the multivariate equivalent of the Euclidean distance, is used to detect the outliers in the correlated data accurately and finds widespread application in fault identification, data clustering, single-class classification, information security, data mining, etc. However, traditional CMOS-based approaches to compute Mahalanobis distance are bulky and consume a huge amount of energy. Therefore, there is an urgent need for a compact and energy-efficient implementation of an outlier detection technique which may be deployed on AIoT primitives, including wireless sensor nodes for in-situ outlier detection and generation of high-quality data. To this end, in this paper, for the first time, we have proposed an efficient Ferroelectric FinFET-based implementation for detecting outliers in correlated multivariate data using Mahalanobis distance. The proposed implementation utilizes two crossbar arrays of ferroelectric FinFETs to calculate the Mahalanobis distance and detect outliers in the popular Wisconsin breast cancer dataset using a novel inverter-based threshold circuit. Our implementation exhibits an accuracy of 94.1% which is comparable to the software implementations while consuming a significantly low energy (13.56 pJ)