A risk management method, system, and non-transitory computer readable medium, include a data analyzing circuit configured to analyze user data, site data, and equipment data to map prior behavior types to an event on a site, a relationship determining circuit configured to determine a relationship between the mapped data and the event based on behaviors exhibited by the user and an impact on a performance factor and a risk factor, and a reinforcement learning circuit configured to use reinforcement learning to learn the performance factor to the risk factor ratio to optimize an overall site productivity.

In operational methodology and a software package or other computer enabled business method, which enables users to apply the method and practice. A wide variety of means for identifying, evaluating and mitigating risk and performance factors within an organization are also provided.

In operational methodology and a software package or other computer enabled business method, which enables users to apply the method and practice. A wide variety of means for identifying, evaluating and mitigating risk and performance factors within an organization are also provided.

A method, apparatus and system is provided for assessing risk for well completion, comprising: obtaining, using an input interface, a Below Rotary Table hours and a plurality of well-field parameters for one or more planned runs, determining, using at least one processor, one or more non-productive time values that correspond to the one or more planned runs based upon the well-field parameters, developing, using at least one processor, a non-productive time distribution and a Below Rotary Table distribution via one or more Monte Carlo trials; and outputting, using a graphic display, a risk transfer model results based on a total BRT hours from the Below Rotary Table and the non-productive time distribution produced from the one or more Monte Carlo trials.

A method, apparatus and system is provided for assessing risk for well completion, comprising: obtaining, using an input interface, a Below Rotary Table hours and a plurality of well-field parameters for one or more planned runs, determining, using at least one processor, one or more non-productive time values that correspond to the one or more planned runs based upon the well-field parameters, developing, using at least one processor, a non-productive time distribution and a Below Rotary Table distribution via one or more Monte Carlo trials; and outputting, using a graphic display, a risk transfer model results based on a total BRT hours from the Below Rotary Table and the non-productive time distribution produced from the one or more Monte Carlo trials.

Systems and methods are described herein for learning an entity’s trust model and risk tolerance. An entity’s trust score may be calculated based on data from a variety of data sources, and this data may be combined according to a set of weights which reflect an entity’s trust model and risk tolerance. For example, an entity may weight data of a certain type more heavily for certain types of transactions and another type of data more heavily for other transactions. By gathering data about the entity, a system may predict the entity’s trust model and risk tolerance and adjust the set of weights accordingly for calculating trust scores. Furthermore, by monitoring how entities adjust weights for different transaction types, default weighting profiles may be created that are customized for specific transaction types. As another example, an entity’s trust score, as reported to a requesting entity, may be adjusted based on that requesting entity’s own trust model, or how “trusting” the requesting entity is.

Systems and methods are described herein for learning an entity’s trust model and risk tolerance. An entity’s trust score may be calculated based on data from a variety of data sources, and this data may be combined according to a set of weights which reflect an entity’s trust model and risk tolerance. For example, an entity may weight data of a certain type more heavily for certain types of transactions and another type of data more heavily for other transactions. By gathering data about the entity, a system may predict the entity’s trust model and risk tolerance and adjust the set of weights accordingly for calculating trust scores. Furthermore, by monitoring how entities adjust weights for different transaction types, default weighting profiles may be created that are customized for specific transaction types. As another example, an entity’s trust score, as reported to a requesting entity, may be adjusted based on that requesting entity’s own trust model, or how “trusting” the requesting entity is.

A destruction device management method is provided that is capable of minimizing a risk that an information recording medium supposed to be disposed of as waste is taken out and distributed to a secondhand market and a risk that information leaks from the information recording medium.

In the present invention, a server device records personal identification information, destruction device identification information, and location information, received from a mobile terminal, in an information recording means. As a result, the server device can centrally manage information such as where the destruction device was located, which destruction device was used, and who performed the destruction work. That is, when the destruction of information recording medium is performed using the destruction device, the record is always stored in the server device. Therefore, the risk of information recording medium being taken outside by a malicious employee or the like can be suppressed because the information recording medium cannot be taken outside (under the false pretense that it has been destructed) without actually operating the destruction device at all.

A destruction device management method is provided that is capable of minimizing a risk that an information recording medium supposed to be disposed of as waste is taken out and distributed to a secondhand market and a risk that information leaks from the information recording medium.

In the present invention, a server device records personal identification information, destruction device identification information, and location information, received from a mobile terminal, in an information recording means. As a result, the server device can centrally manage information such as where the destruction device was located, which destruction device was used, and who performed the destruction work. That is, when the destruction of information recording medium is performed using the destruction device, the record is always stored in the server device. Therefore, the risk of information recording medium being taken outside by a malicious employee or the like can be suppressed because the information recording medium cannot be taken outside (under the false pretense that it has been destructed) without actually operating the destruction device at all.

The present disclosure provides techniques for calculating an entity's cybersecurity risk based on identified relationships between the entity and one or more vendors. Customer/vendor relationships may impact the cybersecurity risk for each of the parties involved because a security compromise of a downstream or upstream provider can lead to a compromise of multiple other companies. For example, if organization A uses B (e.g., a cloud service provider) to store files, and B is compromised, this may lead to organization A being compromised (e.g., the files organization A stored using B may have been compromised by the breach of B's cybersecurity). Embodiments of the present disclosure further provide a technique for calculating a cybersecurity risk score for an organization based on identified customer/vendor relationships.