Enhanced data security and access control using machine learning

1. A method for training one or more machine learning models to control data accessibility, comprising:

generating a first training data set from a set of historical access records, wherein each respective access record in the first training data set corresponds to a respective request for data and comprises information identifying whether the respective request satisfies one or more data access rules;

generating a second training data set from a set of data records, wherein each respective data record in the second training data set corresponds to a respective data element and comprises:

information identifying whether the respective data element satisfies the one or more data access rules; and

information identifying data profiles for sources of each data element in the respective set of aggregated data elements;

generating a third training data set from the set of historical access records, wherein each respective access record in the third training data set corresponds to a respective set of aggregated data elements and comprises information identifying whether the respective set of aggregated data elements satisfies the one or more data access rules;

training the one or more machine learning models based on the first, second, and third training data sets to generate an output identifying whether requests for data should be granted; and

deploying the one or more machine learning models to one or more computing systems.

2. The method of claim 1 , wherein training the one or more machine learning models based on the first, second, and third training data sets comprises:

training a first set of the one or more machine learning models based on the first training data set;

training a second set of the one or more machine learning models based on the second training data set; and

training a third set of the one or more machine learning models based on the third training data set.

3. The method of claim 2 , wherein the one or more data access rules comprise:

(i) a first rule;

(ii) a second rule; and

(iii) a third rule.

4. The method of claim 3 , wherein:

training the first set of the one or more machine learning models comprises:

training a first machine learning model based on the first training data set and the first rule,

training a second machine learning model based on the first training data set and the second rule, and

training a third machine learning model based on the first training data set and the third rule,

training the second set of the one or more machine learning models comprises:

training a fourth machine learning model based on the second training data set and the first rule,

training a fifth machine learning model based on the second training data set and the second rule, and

training a sixth machine learning model based on the second training data set and the third rule, and

training the third set of the one or more machine learning models comprises:

training a seventh machine learning model based on the third training data set and the first rule,

training an eighth machine learning model based on the third training data set and the second rule, and

training a ninth machine learning model based on the third training data set and the third rule.

5. The method of claim 3 , wherein:

the first rule specifies that data can be accessed only if doing so would improve humanity,

the second rule specifies that data can be accessed only if an intended use of the data is legitimate, and

the third rule specifies that data can be accessed only if it remains protected.

6. The method of claim 1 , wherein each respective access record in the first training data set further comprises information identifying:

a purpose for the respective request; and

one or more data elements that are relevant for the respective request.

7. The method of claim 1 , wherein each respective data record in the second training data set further comprises information identifying:

one or more characteristics of the respective data element.

8. The method of claim 1 , wherein each respective data record in the third training data set further comprises information identifying:

a data profile for a source of the respective data element.