Data security systems and methods

At least one non-transitory computer-readable storage media having computer-executable instructions embodied thereon, wherein when executed by at least one processor, the computer-executable instructions cause the processor to:

• log a plurality of data access events initiated by a user, wherein the plurality of data access events relate to a request for data;
• analyze the plurality of data access events;
• generate, based upon the analyzing, a user profile, the user profile including (i) at least one historical data access pattern associated with the user, (ii) a type of the data accessed by the plurality of data access events, (iii) a frequency of accessing the type of data of the plurality of data access events, and (iv) a size of the data accessed by the plurality of data access events;
• receive a new data access event initiated by the user including (i) a type of requested data and (ii) a size of the requested data;
• compare the new data access event initiated by the user to the user profile including comparing (i) the type of requested data of the new data access event to the type of the data included in the user profile, (ii) a frequency of the new data access event to the frequency included in the user profile, and (iii) the size of the requested data of the new data access event to the size of the data included in the user profile;
• learn, through at least one of artificial intelligence and machine learning techniques, the data typically accessed by the user in the plurality of data access events initiated by the user;
• and
• determine, based upon the comparing, whether the data access event initiated by the user corresponds to the data typically accessed by the user and the at least one historical data access pattern included in the user profile.

A method for data security comprising:

• logging, by a processor, a plurality of data access events initiated by a user, wherein the plurality of data access events relate to a request for data;
• analyzing, by the processor, the plurality of data access events;
• generating, by the processor and based upon the analyzing, a user profile, the user profile including (i) at least one historical data access pattern associated with the user, (ii) a type of the data accessed by the plurality of data access events, (iii) a frequency of accessing the type of data of the plurality of data access events, and (iv) a size of the data accessed by the plurality of data access events;
• receiving a new data access event initiated by the user including (i) a type of requested data and (ii) a size of the requested data;
• comparing, by the processor, the new data access event initiated by the user to the user profile including comparing (i) the type of requested data of the new data access event to the type of the data included in the user profile, (ii) a frequency of the new data access event to the frequency included in the user profile, and (iii) the size of the requested data of the new data access event to the size of the data included in the user profile;
• learning, through at least one of artificial intelligence and machine learning techniques, the data typically accessed by the user in the plurality of data access events initiated by the user;
• and
• determining, by the processor and based upon the comparing, whether the data access event initiated by the user corresponds to the data typically accessed by the user and the at least one historical data access pattern included in the user profile.