Machine learning techniques for automated document and organization validation
Systems and methods for validating documents, organization, and individuals are provided, utilizing both automated and manually controlled validation checks. In one implementation, a method includes a step of receiving a request to perform a validation analysis with respect to an organization, wherein the validation analysis includes Machine-Learning (ML) procedures for checking multiple validation metrics. In response to gathering multiple documents relevant for performing the validation analysis, the method further includes a step of extracting data from each of the multiple documents relevant for checking the multiple validation metrics. Also, the method includes a step of accepting manual assistance from a validation specialist when needed for performing the validation analysis.
1 . A Machine Learning (ML)-based validator comprising:
a processing device; and
memory configured to store an ML-assisted validation program having instructions that, when executed, enable the processing device to perform the steps of:
receiving a request to perform a validation analysis with respect to an organization, wherein the validation analysis includes procedures for checking multiple validation metrics, wherein the request comprises a Certificate Signing Request (CSR) for obtaining an X.509 digital certificate and the validation analysis comprises an Organization Validation (OV) analysis or an Extended Validation (EV) analysis performed by, or on behalf of, a Certificate Authority (CA),
in response to gathering multiple documents relevant for performing the validation analysis, extracting data from each of the multiple documents relevant for checking the multiple validation metrics, wherein gathering the multiple documents includes forming a case file for the request and retrieving at least some of the multiple documents from approved business-related or government-related websites,
determining, for each of the multiple documents, whether the document is valid or invalid and determining a confidence level representing assurance that the document is valid,
determining whether the extracted data supports one or more criteria corresponding to the multiple validation metrics for the OV analysis or the EV analysis,
automatically marking a case file as certifiable when confidence levels for the multiple documents and confidence levels for the multiple validation metrics meet respective predetermined thresholds and, in response, enabling issuance of the X.509 digital certificate, and
accepting manual assistance from a validation specialist when needed for performing the validation analysis when at least one document is invalid or has a confidence level below a predetermined threshold or when at least one validation metric has a confidence level below a predetermined threshold.
2 . The ML-based validator of claim 1 , wherein gathering the multiple documents includes one or more of a) receiving one or more documents submitted along with the request, b) retrieving one or more documents from relevant websites, and c) requesting and receiving one or more missing documents from a representative of the organization.
3 . The ML-based validator of claim 2 , wherein accepting the manual assistance further includes asking the validation specialist to obtain any missing documents needed for performing the validation analysis.
4 . The ML-based validator of claim 1 , wherein the instructions further enable the processing device to perform one or more of the steps of:
a) determining whether each of the multiple documents is valid or invalid, and
b) determining a confidence level for each of the multiple documents representing assurance that the respective document is valid.
5 . The ML-based validator of claim 4 , wherein, in response to determining that a document of the multiple documents is invalid or has a confidence level below a predetermined threshold, the instructions further enable the processing device to perform the steps of:
gathering a replacement document for replacing an invalid or low-confidence document, and
determining whether the replacement document is valid or invalid or whether a confidence level of the replacement document is above or below the predetermined threshold.
6 . The ML-based validator of claim 1 , wherein checking the multiple validation metrics includes verifying whether or not the extracted data supports one or more criteria regarding the validation analysis.
7 . The ML-based validator of claim 1 , wherein the request to perform the validation analysis is a Certificate Signing Request (CSR) for obtaining a digital certificate with respect to the organization, wherein the CSR includes a public key and identifying information for the organization and is integrity-protected by a digital signature.
8 . The ML-based validator of claim 1 , wherein the request is received from a representative device used by an administrator of the organization, and wherein the validation analysis includes a verification process for verifying an identity of the administrator, wherein, when the OV analysis or the EV analysis is completed and the multiple validation metrics are satisfied, a case file associated with the request is flagged as certifiable to enable issuance of an X.509 certificate to the organization.
9 . The ML-based validator of claim 1 , wherein the instructions further enable the processing device to utilize Reinforcement Learning (RL) based on manual assistance from the validation specialist.
10 . The ML-based validator of claim 1 , wherein the validation analysis is an Organization Validation (OV) analysis or an Extended Validation (EV) analysis, and wherein the ML-based validator is part of a Certificate Authority (CA).
11 . A method comprising the steps of:
receiving a request to perform a validation analysis with respect to an organization, wherein the validation analysis includes Machine-Learning (ML) procedures for checking multiple validation metrics, wherein the request comprises a Certificate Signing Request (CSR) for obtaining an X.509 digital certificate and the validation analysis comprises an Organization Validation (OV) analysis or an Extended Validation (EV) analysis performed by, or on behalf of, a Certificate Authority (CA),
in response to gathering multiple documents relevant for performing the validation analysis, extracting data from each of the multiple documents relevant for checking the multiple validation metrics, wherein gathering the multiple documents includes forming a case file for the request and retrieving at least some of the multiple documents from approved business-related or government-related websites,
determining, for each of the multiple documents, whether the document is valid or invalid and determining a confidence level representing assurance that the document is valid,
determining whether the extracted data supports one or more criteria corresponding to the multiple validation metrics for the OV analysis of the EV analysis,
automatically marking a case file as certifiable when confidence levels for the multiple documents and confidence levels for the multiple validation metrics meet respective predetermined thresholds and, in response, enabling issuance of the X.509 digital certificate, and
accepting manual assistance from a validation specialist when needed for performing the validation analysis when at least one document is invalid or has a confidence level below a predetermined threshold or when at least one validation metric has a confidence level below a predetermined threshold.
12 . The method of claim 11 , wherein gathering the multiple documents includes one or more of a) receiving one or more documents submitted along with the request, b) retrieving one or more documents from relevant websites, and c) requesting and receiving missing documents from a representative of the organization.
13 . The method of claim 12 , wherein accepting manual assistance further includes asking the validation specialist to obtain any missing documents needed for performing the validation analysis, the method further comprising the step of using Reinforcement Learning (RL) to revise a ML model based on input from the validation specialist.
14 . The method of claim 11 , further comprising one or more of the steps of:
a) determining whether each of the multiple documents is valid or invalid, and
b) determining a confidence level for each of the multiple documents representing assurance that the respective document is valid.
15 . The method of claim 14 , wherein, in response to determining that a document of the multiple documents is invalid or has a confidence level below a predetermined threshold, the method further comprises the steps of:
gathering a replacement document for replacing an invalid or low-confidence document, and
determining whether the replacement document is valid or invalid and/or determining whether a confidence level of the replacement document is above or below the predetermined threshold.
16 . The method of claim 11 , wherein checking the multiple validation metrics includes verifying whether or not the extracted data supports one or more criteria regarding the validation analysis.
17 . A non-transitory computer-readable medium configured to store an ML-assisted validation program having instructions that, when executed, enable a processing device to perform the steps of:
receiving a request to perform a validation analysis with respect to an organization, wherein the validation analysis includes procedures for checking multiple validation metrics, wherein the request comprises a Certificate Signing Request (CSR) for obtaining an X.509 digital certificate and the validation analysis comprises an Organization Validation (OV) analysis or an Extended Validation (EV) analysis performed by, or on behalf of, a Certificate Authority (CA),
in response to gathering multiple documents relevant for performing the validation analysis, extracting data from each of the multiple documents relevant for checking the multiple validation metrics, wherein gathering the multiple documents includes forming a case file for the request and retrieving at least some of the multiple documents from approved business-related or government-related websites,
determining, for each of the multiple documents, whether the document is valid or invalid and determining a confidence level representing assurance that the document is valid,
determining whether the extracted data supports one or more criteria corresponding to the multiple validation metrics for the OV analysis or the EV analysis,
automatically marking a case file as certifiable when confidence levels for the multiple documents and confidence levels for the multiple validation metrics meet respective predetermined thresholds and, in response, enabling issuance of the X.509 digital certificate, and
accepting manual assistance from a validation specialist when needed for performing the validation analysis when at least one document is invalid or has a confidence level below a predetermined threshold or when at least one validation metric has a confidence level below a predetermined threshold.
18 . The non-transitory computer-readable medium of claim 17 , wherein the request is received from a representative device used by an administrator of the organization, and wherein the validation analysis includes a verification process for verifying an identity of the administrator.
19 . The non-transitory computer-readable medium of claim 17 , wherein the instructions further enable the processing device to utilize Reinforcement Learning (RL) to revise the ML-assisted validation program based on manual assistance from the validation specialist.
20 . The non-transitory computer-readable medium of claim 17 , wherein the validation analysis is an Organization Validation (OV) analysis or an Extended Validation (EV) analysis, and wherein the non-transitory computer-readable medium is stored in a computing device associated with a Certificate Authority (CA).