Quantum Risk Assessment Comparison

A computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:

• receive a plurality of historical risk assessments and a plurality of quantum readiness levels;
• calculate a plurality of risk dimension metrics based on the plurality of historical risk assessments and the plurality of quantum readiness levels;
• calculate a plurality of risk assessment attributes dimension metrics based on the plurality of historical risk assessments and the plurality of quantum readiness levels;
• generate a nearest neighbors nomination based on the calculated plurality of risk dimension metrics and the calculated plurality of risk assessment attributes dimension metrics;
• calculate a confidence of quality metric based on the generated nearest neighbors nomination;
• and
• determine that the confidence of quality metric is below a threshold;
• and
• execute a cryptography algorithm based on a determination that the confidence of quality metric is below the threshold.

A system comprising:

• a processor, a computer readable memory, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:
• receive a plurality of historical risk assessments and a plurality of quantum readiness levels;
• calculate a plurality of risk dimension metrics based on the plurality of historical risk assessments and the plurality of quantum readiness levels;
• calculate a plurality of risk assessment attributes dimension metrics based on the plurality of historical risk assessments and the plurality of quantum readiness levels;
• generate a nearest neighbors nomination based on the calculated plurality of risk dimension metrics and the calculated plurality of risk assessment attributes dimension metrics;
• determine that a historical nearest neighbors nomination is not within a nomination threshold of the generated nearest neighbors nomination;
• train a reinforcement machine learning (ML) model based on the historical nearest neighbors nomination not being within the nomination threshold of the generated nearest neighbors nomination;
• calculate a confidence of quality metric based on the generated nearest neighbors nomination;
• and
• determine that the confidence of quality metric is below a threshold;
• and
• execute a cryptography algorithm based on a determination that the confidence of quality metric is below the threshold.

A method, comprising:

• receiving, by a computing device, a plurality of historical risk assessments and a plurality of quantum readiness levels;
• calculating, by the computing device, a plurality of risk dimension metrics based on the plurality of historical risk assessments and the plurality of quantum readiness levels;
• calculating, by the computing device, a plurality of risk assessment attributes dimension metrics based on the plurality of historical risk assessments and the plurality of quantum readiness levels;
• generating, by the computing device, nearest neighbors nomination based on the calculated plurality of risk dimension metrics and the calculated plurality of risk assessment attributes dimension metrics;
• determining, by the computing device, that a historical nearest neighbors nomination is not within a nomination threshold of the generated nearest neighbors nomination;
• training a reinforcement machine learning (ML) model based on the historical nearest neighbors nomination not being within the nomination threshold of the generated nearest neighbors nomination;
• running the trained reinforcement ML model to output a type of nearest neighbor nomination metric based on the training of the reinforcement ML model;
• calculating, by the computing device, a confidence of quality metric based on the generated nearest neighbors nomination;
• determining, by the computing device, that the confidence of quality metric is below a threshold;
• and
• executing, by the computing device, a cryptography algorithm based on a determination that the confidence of quality metric is below the threshold.

A computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:

• receive a plurality of historical risk assessments and a plurality of quantum readiness levels;
• calculate a plurality of risk dimension metrics based on the plurality of historical risk assessments and the plurality of quantum readiness levels;
• calculate a plurality of risk assessment attributes dimension metrics based on the plurality of historical risk assessments and the plurality of quantum readiness levels;
• generate a first nearest neighbors nomination based on the calculated plurality of risk dimension metrics and the calculated plurality of risk assessment attributes dimension metrics;
• determine that a historical nearest neighbors nomination is not within a nomination threshold of the generated first nearest neighbors nomination;
• train a reinforcement machine learning (ML) model based on the historical nearest neighbors nomination not being within the nomination threshold of the generated first nearest neighbors nomination;
• run the trained reinforcement ML model to output a type of nearest neighbor nomination metric based on the training of the reinforcement ML model;
• generate a second nearest neighbors nomination based on the type of nearest neighbor nomination metric;
• calculate a confidence of quality metric based on the generated second nearest neighbors nomination;
• determine that the confidence of quality metric is above a threshold;
• and
• review the confidence of quality metric based on a determination that the confidence of quality metric is above the threshold.