Graphics processing unit optimization

A system for optimizing graphics processing unit (GPU) resources during machine learning training events while preserving siloed client data privately, comprising:

• a first siloed database stored in a memory and configured to silo initial graph data having a first structure representing information technology associated with an organization, wherein the information technology associated with the organization includes any one of (i) at least one on-premises resource, (ii) at least one third party service, (iii) at least one cloud computing service, (iv) a plurality of credentials associated with (i), (ii), (iii), or (v) any combination of (i), (ii), (iii) and (vi);
• an algorithmic framework processor configured to map a first structure represented by the initial graph data to a second structure, the second structure having a lower dimension than the first structure and containing real numbers, thereby generating an initial client embedding data set;
• an information gain processor configured to compute a first information gain corresponding to the initial client embedding data set;
• a dedicated server including a graphics processing unit (GPU) configured to train a machine learning model based on the initial client embedding data set to generate at least one initial attack path in the initial graph data;
• a second siloed database configured to silo second graph data having a third structure representing the information technology associated with the organization;
• the algorithmic framework processor further configured to map the third structure representing the second graph data to a fourth structure, the fourth structure having a lower dimension than the third structure and containing real numbers, thereby generating a second client embedding data set;
• the information gain processor further configured to compute a second information gain corresponding to the second client embedding data set;
• a difference processor configured to compute a difference between the first information gain and the second information gain;
• and
• the dedicated server further configured to retrain the machine learning model if the difference between the first information gain and the second information gain meets a predetermined threshold to generate at least one new attack path in the second graph data and to await for a next client embedding data set update if the difference between the first information gain and the second information gain does not meet the predetermined threshold.

A non-transitory computer-readable medium having stored thereon one or more sequences of instructions for causing one or more processors to perform:

• siloing initial graph data having a first structure representing information technology associated with an organization, wherein the information technology associated with the organization includes any one of (i) at least one on-premises resource, (ii) at least one third party service, (iii) at least one cloud computing service, (iv) a plurality of credentials associated with (i), (ii), (iii), or (v) any combination of (i), (ii), (iii) and (vi);
• mapping the first structure represented by the initial graph data to a second structure, the second structure having a lower dimension than the first structure and containing real numbers, thereby generating an initial client embedding data set;
• computing a first information gain corresponding to the initial client embedding data set, wherein the information gain is a scalar representation showing an amount of information diversity in a client embedding data set;
• training a machine learning model based on the initial client embedding data set to generate at least one initial attack path in the initial graph data;
• siloing updated graph data as second graph data having a third structure representing the information technology associated with the organization;
• mapping the third structure representing the second graph data to a fourth structure, the fourth structure having a lower dimension than the third structure and containing real numbers, thereby generating a second client embedding data set;
• computing a second information gain corresponding to the second client embedding data set;
• computing a difference between the first information gain and the second information gain;
• retraining the machine learning model if the difference between the first information gain and the second information gain meets a predetermined threshold to generate at least one new attack path in the second graph data;
• and
• awaiting for a next client embedding data set update if the difference between the first information gain and the second information gain does not meet the predetermined threshold, thereby preventing retraining.