Content-related mitigation
1. Content-related mitigation
Let’s talk about some content-related threat mitigation strategies. Mitigating data misuse, privacy violations, and handling sensitive, restricted, or unacceptable content is accomplished by following a three step process. First, classify the content to ensure your security controls align with the value of the data. Second, scan content and redact sensitive data. And third, detect unacceptable content before the item is published. It is important to classify data to ensure your security controls align with required protection protocols for different types of data. Some data is easy to detect, classify, and isolate, such as credit card numbers. Other times it may be more difficult to identify different classifications of data within a large sea of content. The Cloud Natural Language API could be used to help quickly identify different categories of content. To prevent the possibility of inadvertently incorporating inappropriate content into your system, you need to monitor and scan files and data that are coming into your applications. There are several APIs that can help automate this process. The Vision API can easily detect different types of inappropriate content in images, with an ability to flag both “adult” as well as violent content. The Video Intelligence API can provide similar monitoring and scanning for videos. And the Cloud Data Loss Prevention API can also be used to detect sensitive data content before it is accidentally exposed to the public. Sensitive data can accidentally be exposed when organizations accidentally publish documents, screenshots or other images that inadvertently were created to contain or show sensitive data. In addition, sometimes a user may provide unsolicited private/sensitive data for storage within a system that was not designed to protect that data. The DLP API is a machine learning model that can be used to scan various document formats and images for various types of sensitive data and then redact it, even from images. Look at this screenshot, taken from an application that allows the user to provide unrestricted feedback. The system was not designed to store this feedback as if it were sensitive data. Therefore, all of the data is stored unencrypted and without other effective security controls. The screenshot on the right shows this data after leveraging the power of the DLP API. You can see that all of the sensitive data has been detected and then redacted from the image.2. Let's practice!
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