Abstract: A document management system generates and validates online documents. The document management system enables the customization of an online document through various rendering parameters for customizing the appearance of agreement content rendered to a receiving entity. The document management system may validate the rendering parameters of customized online documents to determine a likelihood that the documents are valid. For example, the system may determine a likelihood that an online document generated with prechecked checkbox is invalid. The document management system may recommend custom templates for online documents or modifications to an online document that may increase a likelihood of achieving an originating entity's objective. For example, the document management system can apply a machine-learned model to recommend a target rendering parameter for inclusion that would increase the likelihood of a receiving entity clicking an “Agree” button.

Abstract: A document management system generates and validates online documents. The document management system enables the customization of an online document through various rendering parameters for customizing the appearance of agreement content rendered to a receiving entity. The document management system may validate the rendering parameters of customized online documents to determine a likelihood that the documents are valid. For example, the system may determine a likelihood that an online document generated with prechecked checkbox is invalid. The document management system may recommend custom templates for online documents or modifications to an online document that may increase a likelihood of achieving an originating entity's objective. For example, the document management system can apply a machine-learned model to recommend a target rendering parameter for inclusion that would increase the likelihood of a receiving entity clicking an “Agree” button.

Abstract: A document management system generates and validates online documents. The document management system enables the customization of an online document through various rendering parameters for customizing the appearance of agreement content rendered to a receiving entity. The document management system may validate the rendering parameters of customized online documents to determine a likelihood that the documents are valid. For example, the system may determine a likelihood that an online document generated with prechecked checkbox is invalid. The document management system may recommend custom templates for online documents or modifications to an online document that may increase a likelihood of achieving an originating entity's objective. For example, the document management system can apply a machine-learned model to recommend a target rendering parameter for inclusion that would increase the likelihood of a receiving entity clicking an “Agree” button.

Abstract: One or more techniques and/or systems are provided for risk assessment. Historical authentication data and/or compromised user account data may be evaluated to identify a set of authentication context properties associated with user authentication sessions and/or a set of malicious account context properties associated with compromised user accounts (e.g., properties indicative of whether a user recently visited a malicious site, created a fake social network profile, logged in from unknown locations, etc.). The set of authentication context properties and/or the set of malicious account context properties may be annotated to create an annotated context property training set that may be used to train a risk assessment machine learning model to generate a risk assessment model. The risk assessment model may be used to evaluate user context properties of a user account event to generate a risk analysis metric indicative of a likelihood the user account event is malicious or safe.

Abstract: One or more techniques and/or systems are provided for risk assessment. Historical authentication data and/or compromised user account data may be evaluated to identify a set of authentication context properties associated with user authentication sessions and/or a set of malicious account context properties associated with compromised user accounts (e.g., properties indicative of whether a user recently visited a malicious site, created a fake social network profile, logged in from unknown locations, etc.). The set of authentication context properties and/or the set of malicious account context properties may be annotated to create an annotated context property training set that may be used to train a risk assessment machine learning model to generate a risk assessment model. The risk assessment model may be used to evaluate user context properties of a user account event to generate a risk analysis metric indicative of a likelihood the user account event is malicious or safe.

Abstract: One or more techniques and/or systems are provided for risk assessment. Historical authentication data and/or compromised user account data may be evaluated to identify a set of authentication context properties associated with user authentication sessions and/or a set of malicious account context properties associated with compromised user accounts (e.g., properties indicative of whether a user recently visited a malicious site, created a fake social network profile, logged in from unknown locations, etc.). The set of authentication context properties and/or the set of malicious account context properties may be annotated to create an annotated context property training set that may be used to train a risk assessment machine learning model to generate a risk assessment model. The risk assessment model may be used to evaluate user context properties of a user account event to generate a risk analysis metric indicative of a likelihood the user account event is malicious or safe.

Abstract: One or more techniques and/or systems are provided for risk assessment. Historical authentication data and/or compromised user account data may be evaluated to identify a set of authentication context properties associated with user authentication sessions and/or a set of malicious account context properties associated with compromised user accounts (e.g., properties indicative of whether a user recently visited a malicious site, created a fake social network profile, logged in from unknown locations, etc.). The set of authentication context properties and/or the set of malicious account context properties may be annotated to create an annotated context property training set that may be used to train a risk assessment machine learning model to generate a risk assessment model. The risk assessment model may be used to evaluate user context properties of a user account event to generate a risk analysis metric indicative of a likelihood the user account event is malicious or safe.

Abstract: A method is presented for creating a life cycle workflow for a project on a server computer. One or more workflow phases are created on the server computer. Each workflow phase corresponds to a plurality of workflow stages for the project. One or more workflow stages are created on the server computer. Each workflow stage corresponds to a specific sequence of workflow activities. One or more project detail pages are created on the server computer. Each project detail page is a web page that is made visible during the workflow stage. When a workflow stage is created, a workflow phase is selected to be associated with the workflow stage and one or more project detail pages are selected for the workflow stage.