Further strategies can be defined based on specific use cases. For example, in an R&D company where doctors are dealing with sensitive data and need to share it, if two doctors are sitting in different countries with sensitive data that they need to see because they know what to do with it, a further strategy can be defined. If both doctors are sitting in different geographic locations and must see their observations of what is happening in a patient's adverse event, Google has another service called VPC Service Control. This service can apply additional protection by preventing Google Cloud Data Loss Prevention data exfiltration. For example, if it is detected that four projects have sensitive data that should not be shared with everyone but should be shared with a specific person or specific project, not only through human sharing but also through machine sharing and machine process sharing, strategies can be defined based on this. Google's VPC Service Control service can be used to make data exfiltrations restricting or to restrict copying from one place to another. The copy can be one-directional rather than restricting bi-directional transfers. These are strategies that can be defined once the report is available, which is very helpful to obtain through Google Cloud Data Loss Prevention services scan. The first advantage is that text-based queries are available where they can query in an easy way. Google Cloud Data Loss Prevention service is helpful in putting, converting, or transforming all data into text. There is an easy option to export in BigQuery. BigQuery has another feature where someone can make a custom query and obtain data based on that query. Further decisions can be taken or business analytics can be performed to achieve business orientation. However, to achieve this or for someone who avails those services, more dissatisfaction may occur because documentation is not easily available in the public domain for Google. For example, compared to other clouds, most use cases required are already published by other clouds with the services. For example, if someone wants to automate in their existing system, if ten people are searching, nine people will find the exact use case they wanted to implement in their documentation.
Data Loss Prevention (DLP) solutions are essential in safeguarding sensitive data from breaches and unauthorized access, ensuring compliance with industry regulations while maintaining trust.DLP solutions offer comprehensive data protection by monitoring, detecting, and blocking potential data breaches before they occur. By integrating with existing IT infrastructure, they provide visibility into data usage and movement across networks. These solutions are designed to be scalable and...
Further strategies can be defined based on specific use cases. For example, in an R&D company where doctors are dealing with sensitive data and need to share it, if two doctors are sitting in different countries with sensitive data that they need to see because they know what to do with it, a further strategy can be defined. If both doctors are sitting in different geographic locations and must see their observations of what is happening in a patient's adverse event, Google has another service called VPC Service Control. This service can apply additional protection by preventing Google Cloud Data Loss Prevention data exfiltration. For example, if it is detected that four projects have sensitive data that should not be shared with everyone but should be shared with a specific person or specific project, not only through human sharing but also through machine sharing and machine process sharing, strategies can be defined based on this. Google's VPC Service Control service can be used to make data exfiltrations restricting or to restrict copying from one place to another. The copy can be one-directional rather than restricting bi-directional transfers. These are strategies that can be defined once the report is available, which is very helpful to obtain through Google Cloud Data Loss Prevention services scan. The first advantage is that text-based queries are available where they can query in an easy way. Google Cloud Data Loss Prevention service is helpful in putting, converting, or transforming all data into text. There is an easy option to export in BigQuery. BigQuery has another feature where someone can make a custom query and obtain data based on that query. Further decisions can be taken or business analytics can be performed to achieve business orientation. However, to achieve this or for someone who avails those services, more dissatisfaction may occur because documentation is not easily available in the public domain for Google. For example, compared to other clouds, most use cases required are already published by other clouds with the services. For example, if someone wants to automate in their existing system, if ten people are searching, nine people will find the exact use case they wanted to implement in their documentation.
I rate the overall solution a ten out of ten.