  Diagnostic data such as logs and memory dumps from production systems are
often shared with development teams to do root cause analysis of system
crashes. Invariably such diagnostic data contains sensitive information and
sharing it can lead to data leaks. To handle this problem we present Knowledge
and Learning-based Adaptable System for Sensitive InFormation Identification
and Handling (KLASSIFI) which is an end to end system capable of identifying
and redacting sensitive information present in diagnostic data. KLASSIFI is
highly customizable, allowing it to be used for various different business use
cases by simply changing the configuration. KLASSIFI ensures that the output
file is useful by retaining the metadata which is used by various debugging
tools. Various optimizations have been done to improve the performance of
KLASSIFI. Empirical evaluation of KLASSIFI shows that it is able to process
large files (128 GB) in 84 minutes and its performance scales linearly with
varying factors. This points to practicability of KLASSIFI
