This list will be empty when you first access the platform. This Discovery page shows your list of Discovery Groups by default.
Select the Discovery panel under the Manage section in the left-hand navigation column. Common options include organizing by responsible team/business unit, brands or subsidiaries. Users can elect to organize their Discovery Groups to delineate assets in whatever way best benefits their company and workflows. They are independent seed clusters that comprise a single discovery run and operate on their own recurrence schedules. Discovery groupsĬustom discoveries are organized into Discovery Groups. Custom discovery can also help organizations find disparate infrastructure that may relate to independent business units and acquired companies. By submitting a larger list of known assets to operate as discovery seeds, the discovery engine will return a wider pool of assets. Customizing discoveryĬustom discoveries are ideal for organizations that require deeper visibility into infrastructure that may not be immediately linked to their primary seed assets. If you notice any missing assets or have other entities to manage that may not be discovered through infrastructure clearly linked to your organization, you can elect to run customized discoveries to detect these outlier assets.
See the Understanding dashboards article for more information on how to derive insights from these dashboards. Review these dashboard insights to become familiar with your Attack Surface as you wait for additional assets to be discovered and populated in your inventory. If you selected a pre-configured Attack Surface from the list of available organizations, you will be redirected to the Dashboard Overview screen where you can view insights into your organization’s infrastructure in Preview Mode. Then select your organization from the list and click “Build my Attack Surface”.Īt this point, the discovery will be running in the background. When first accessing your Defender EASM instance, select “Getting Started” in the “General” section to search for your organization in the list of automated attack surfaces. This enables users to quickly access their inventory as Defender EASM refreshes the data, adding additional assets and recent context to your Attack Surface. It is recommended that all users search for their organization’s attack surface before creating a custom attack surface and running additional discoveries. Microsoft has preemptively configured the attack surfaces of many organizations, mapping their initial attack surface by discovering infrastructure that’s connected to known assets. Discovered assets are indexed in a customer’s inventory, providing a dynamic system of record of web applications, third party dependencies, and web infrastructure under the organization’s management through a single pane of glass.īefore you run a custom discovery, see the What is discovery? article to understand key concepts mentioned in this article. Discovery scans the internet for assets owned by your organization to uncover previously unknown and unmonitored properties.
There is also a test web app that you can run locally to interact with the backend.Microsoft Defender External Attack Surface Management (Defender EASM) relies on our proprietary discovery technology to continuously define your organization’s unique Internet-exposed attack surface.
This repository contains sample code for all the Lambda functions depicted in the diagram below as well as an AWS CloudFormation template for creating the functions and related resources. In parallel, it also produces a thumbnail of the photo. It then uses image recognition to tag objects in the photo. This workflow processes photos uploaded to Amazon S3 and extracts metadata from the image such as geolocation, size/format, time, etc. The Image Recognition and Processing Backend demonstrates how to use AWS Step Functions to orchestrate a serverless processing workflow using AWS Lambda, Amazon S3, Amazon DynamoDB and Amazon Rekognition. Lambda-refarch-imagerecognition - The Image Recognition and Processing Backend reference architecture demonstrates how to use AWS Step Functions to orchestrate a serverless processing workflow using AWS Lambda, Amazon S3, Amazon DynamoDB and Amazon Rekognition