Abstract: A trained machine learning algorithm receives input data that may contain sensitive information. For example, the input data may be top secret military specifications that are sent as an attachment in an email that is being sent outside of a government computer network. The trained machine learning algorithm is trained with one of: sensitive training data or insensitive training data (or there may be two trained machine learning algorithms where one is trained with the sensitive training data and one is trained with the insensitive training data). The trained machine learning algorithm determines whether the input data contains the sensitive information. In response to determining that the input data contains the sensitive information, an action is taken to prevent release of the input data. For example, the action may be to block the sending of the email.

Abstract: Source code for a type of malware is received. For example, the source code may be source code from a type of computer virus. An Artificial Intelligence (AI) algorithm is identified. For example, the AI algorithm may be ChatGPT. The source code of the type of malware is run through the AI algorithm to produce mutated source code for the type of malware. A prediction algorithm is used to predict a signature of the mutated source code for the type of malware. For example, the prediction algorithm is trained using existing source code of different types of malware to generate a prediction model. The signature of the mutated source code for the type of malware is then compared to a signature of a potentially new type of malware to determine if the signatures are similar.

Abstract: A current thread pattern is identified. For example, a thread pattern of a running software application is identified. Current resource information associated with the current thread pattern is identified. For example, the current resource information may include disk usage, packets sent, ports used, accounts created, etc. The current thread pattern and the current resource information associated with the current thread pattern are compared to an existing malicious thread pattern associated with a type of malware and existing malicious resource information associated with the existing thread pattern. A determination is made if the comparison meets a threshold. For example, if the current thread pattern is 90% similar to the existing malicious thread pattern and the current resource information is within 75% of the existing malicious resource information, the threshold is met. In response to the comparison meeting the threshold, an action is taken to mitigate the type of malware.

Abstract: A training scope of an instance of an Artificial Intelligence (AI) algorithm is determined based on a training corpus used to train the instance of the AI algorithm. AI input data is received. The AI input data is input data for the instance of the AI algorithm. A determination is made if some or all of the AI input data is not within the training scope of the instance of the AI algorithm. In response to determining that the some or all of the AI input data is not within the training scope of the instance of the AI algorithm, the some or all of the AI input data is filtered out. The filtered out some or all of the AI input data helps prevent the instance of the AI algorithm from having as many hallucinations.

Abstract: A trained machine learning algorithm receives input data that may contain sensitive information. For example, the input data may be top secret military specifications that are sent as an attachment in an email that is being sent outside of a government computer network. The trained machine learning algorithm is trained with one of: sensitive training data or insensitive training data (or there may be two trained machine learning algorithms where one is trained with the sensitive training data and one is trained with the insensitive training data). The trained machine learning algorithm determines whether the input data contains the sensitive information. In response to determining that the input data contains the sensitive information, an action is taken to prevent release of the input data. For example, the action may be to block the sending of the email.

Abstract: Source code for a type of malware is received. For example, the source code may be source code from a type of computer virus. An Artificial Intelligence (AI) algorithm is identified. For example, the AI algorithm may be ChatGPT. The source code of the type of malware is run through the AI algorithm to produce mutated source code for the type of malware. A prediction algorithm is used to predict a signature of the mutated source code for the type of malware. For example, the prediction algorithm is trained using existing source code of different types of malware to generate a prediction model. The signature of the mutated source code for the type of malware is then compared to a signature of a potentially new type of malware to determine if the signatures are similar.

Abstract: A current thread pattern is identified. For example, a thread pattern of a running software application is identified. Current resource information associated with the current thread pattern is identified. For example, the current resource information may include disk usage, packets sent, ports used, accounts created, etc. The current thread pattern and the current resource information associated with the current thread pattern are compared to an existing malicious thread pattern associated with a type of malware and existing malicious resource information associated with the existing thread pattern. A determination is made if the comparison meets a threshold. For example, if the current thread pattern is 90% similar to the existing malicious thread pattern and the current resource information is within 75% of the existing malicious resource information, the threshold is met. In response to the comparison meeting the threshold, an action is taken to mitigate the type of malware.

Abstract: A method of assessing risk in an electronic transaction involves assignment of quality attributes to cryptographic identities presented in a digital transaction. The quality assignment supports assessment of risk in the transaction. The evaluation of risk in the transaction is made by assessing machine readable attributes of the digital identities along with transaction details. The digital identity attributes may be constructed using extensions of existing standards. A guarantee against risk of loss may be obtained by procuring insurance on the transaction before execution. Third party insurers may analyze the risk of loss in a transaction by assessing the attributes of digital identities along with transaction details and may provide a requestor with an insurance premium quote. Based on the value of the quote, the transaction participants may decide whether or not to execute the transaction.

Abstract: A method of assessing risk in an electronic transaction involves assignment of quality attributes to cryptographic identities presented in a digital transaction. The quality assignment supports assessment of risk in the transaction. The evaluation of risk in the transaction is made by assessing machine readable attributes of the digital identities along with transaction details. The digital identity attributes may be constructed using extensions of existing standards. A guarantee against risk of loss may be obtained by procuring insurance on the transaction before execution. Third party insurers may analyze the risk of loss in a transaction by assessing the attributes of digital identities along with transaction details and may provide a requestor with an insurance premium quote. Based on the value of the quote, the transaction participants may decide whether or not to execute the transaction.

Abstract: A method of assessing risk in an electronic transaction involves assignment of quality attributes to cryptographic identities presented in a digital transaction. The quality assignment supports assessment of risk in the transaction. The evaluation of risk in the transaction is made by assessing machine readable attributes of the digital identities along with transaction details. The digital identity attributes may be constructed using extensions of existing standards. A guarantee against risk of loss may be obtained by procuring insurance on the transaction before execution. Third party insurers may analyze the risk of loss in a transaction by assessing the attributes of digital identities along with transaction details and may provide a requestor with an insurance premium quote. Based on the value of the quote, the transaction participants may decide whether or not to execute the transaction.

Abstract: A method of controlling information exposure in a multiparty transaction includes an originating transaction participant cryptographically encoding all information for each of the transaction participants such that a unique data content and encryption are used for each of the messages destined to the other transaction participants. The cryptographically encoded messages are transmitted to the transaction participants such that each may decrypt their message and respond to a primary transaction participant with status concerning their portion of the transaction. After reception of affirmative status messages from the transaction participants, the primary transaction participant may transmit messages to the responding transaction participants to execute the multiparty transaction. The originating transaction participant may also be provided an indication that the multiparty transaction is executed.