A method for detecting sensitivity content in time-series data to minimize false positives and false negative alarm rates, the method comprising: receiving, by a processor, the time-series data from a source, wherein the time-series data is received for one or more instances, and wherein an instance of the one or more instances is associated with a value of the time-series data; detecting, by the processor, the sensitivity content in the time-series data, wherein the sensitivity content indicates presence of an anomaly, characterized in that, the detecting comprises: determining a kurtosis value corresponding to one of each instance of the one or more instances associated with the value of the time-series data by plotting a data distribution of the time-series data, wherein the value corresponding to each instance is collected and the time-series data is aggregated for the one or more instances, wherein the value of the time-series data includes a plurality of time stamps in one more time cycle, and the value of the time-series data being associated with a particular time interval, the particular time interval represented in a form of the plurality of time stamps maintained for the time series data; comparing the kurtosis value with a reference value to obtain a nature of the data distribution, wherein the data distribution is one of a platykurtic distribution, a mesokurtic distribution, and a leptokurtic distribution; and processing the time-series data using a first filtering means and a second filtering means, wherein the first filtering means is used for the data distribution of the time-series data is one of the platykurtic distribution and the mesokurtic distribution, wherein the first filtering means comprises a Hampel filter and wherein the second filtering means is used when the data distribution of the time-series data is the leptokurtic distribution and the time-series data is processed using the first filtering means by calculating a median and a median absolute deviation of the time-series data in order to reduce masking effect. receiving, by a processor, the time-series data from a source, wherein the time-series data is received for one or more instances, and wherein an instance of the one or more instances is associated with a value of the time-series data; detecting, by the processor, the sensitivity content in the time-series data, wherein the sensitivity content indicates presence of an anomaly, characterized in that, the detecting comprises: determining a kurtosis value corresponding to one of each instance of the one or more instances associated with the value of the time-series data by plotting a data distribution of the time-series data, wherein the value corresponding to each instance is collected and the time-series data is aggregated for the one or more instances, wherein the value of the time-series data includes a plurality of time stamps in one more time cycle, and the value of the time-series data being associated with a particular time interval, the particular time interval represented in a form of the plurality of time stamps maintained for the time series data; comparing the kurtosis value with a reference value to obtain a nature of the data distribution, wherein the data distribution is one of a platykurtic distribution, a mesokurtic distribution, and a leptokurtic distribution; and processing the time-series data using a first filtering means and a second filtering means, wherein the first filtering means is used for the data distribution of the time-series data is one of the platykurtic distribution and the mesokurtic distribution, wherein the first filtering means comprises a Hampel filter and wherein the second filtering means is used when the data distribution of the time-series data is the leptokurtic distribution and the time-series data is processed using the first filtering means by calculating a median and a median absolute deviation of the time-series data in order to reduce masking effect. determining a kurtosis value corresponding to one of each instance of the one or more instances associated with the value of the time-series data by plotting a data distribution of the time-series data, wherein the value corresponding to each instance is collected and the time-series data is aggregated for the one or more instances, wherein the value of the time-series data includes a plurality of time stamps in one more time cycle, and the value of the time-series data being associated with a particular time interval, the particular time interval represented in a form of the plurality of time stamps maintained for the time series data; comparing the kurtosis value with a reference value to obtain a nature of the data distribution, wherein the data distribution is one of a platykurtic distribution, a mesokurtic distribution, and a leptokurtic distribution; and processing the time-series data using a first filtering means and a second filtering means, wherein the first filtering means is used for the data distribution of the time-series data is one of the platykurtic distribution and the mesokurtic distribution, wherein the first filtering means comprises a Hampel filter and wherein the second filtering means is used when the data distribution of the time-series data is the leptokurtic distribution and the time-series data is processed using the first filtering means by calculating a median and a median absolute deviation of the time-series data in order to reduce masking effect.