[1][2][4] Stochastic forensics was invented in 2010 by computer scientist Jonathan Grier to detect and investigate insider data theft.
[2] Insider data theft has been notoriously difficult to investigate using traditional methods, since it does not create any artifacts (such as changes to the file attributes or Windows Registry).
[6] Since its invention, stochastic forensics has been used in real world investigation of insider data theft,[6] been the subject of academic research,[1][7] and met with industry demand for tools and training.
[2][6] Classical Newtonian mechanics calculates the exact position and momentum of every particle in a system.
[2][3][6] Stochastic forensics chief application is detecting and investigating insider data theft.
[1][2] Drawing on this, stochastic mechanics has been used to successfully investigate insider data theft where other techniques have failed.
[6] Stochastic forensics has been criticized as only providing evidence and indications of data theft, and not concrete proof.
[1][6] Furthermore, many operating systems do not track access timestamps by default, making stochastic forensics not directly applicable.