Abstract: Methods and systems for managing security in a cloud computing environment are provided. Exemplary methods include: gathering data about workloads and applications in the cloud computing environment; updating a graph database using the data, the graph database representing the workloads of the cloud computing environment as nodes and relationships between the workloads as edges; receiving a security template, the security template logically describing targets in the cloud computing environment to be protected and how to protect the targets; creating a security policy using the security template and information in the graph database; and deploying the security policy in the cloud computing environment.

Abstract: Methods and systems for managing security in a cloud computing environment are provided. Exemplary methods include: gathering data about workloads and applications in the cloud computing environment; updating a graph database using the data, the graph database representing the workloads of the cloud computing environment as nodes and relationships between the workloads as edges; receiving a security template, the security template logically describing targets in the cloud computing environment to be protected and how to protect the targets; creating a security policy using the security template and information in the graph database; and deploying the security policy in the cloud computing environment.

Abstract: Systems and methods for machine learning and adaptive optimization are provided herein. A method includes continually receiving input that is indicative of client events, including client behaviors and respective outcomes of software trials of a product maintained in a database, continually segmenting open opportunities using the client behaviors and respective outcomes, continually scoring and prioritizing the open opportunities using the client behaviors and respective outcomes for targeting and re-targeting, continually adjusting targeted proposals to open opportunities and sourcing in prospects based on a targeting scheme, continually presenting targeted offers to create expansion opportunities and updating a product roadmap of the product using the open opportunities, the product roadmap including technical specifications for the product.

Abstract: Systems and methods for machine learning and adaptive optimization are provided herein. A method includes continually receiving input that is indicative of client events, including client behaviors and respective outcomes of software trials of a product maintained in a database, continually segmenting open opportunities using the client behaviors and respective outcomes, continually scoring and prioritizing the open opportunities using the client behaviors and respective outcomes for targeting and re-targeting, continually adjusting targeted proposals to open opportunities and sourcing in prospects based on a targeting scheme, continually presenting targeted offers to create expansion opportunities and updating a product roadmap of the product using the open opportunities, the product roadmap including technical specifications for the product.