--- title: Step 2. Integrating PingOne Protect DaVinci flows in Android description: Explains how to develop a client application that integrates with PingOne Protect by initializing data collection, managing behavioral data capture, and returning the collected data to a DaVinci flow. component: orchsdks page_id: orchsdks:davinci:use-cases/protect/android/02-protect-android-app-davinci canonical_url: https://developer.pingidentity.com/orchsdks/davinci/use-cases/protect/android/02-protect-android-app-davinci.html keywords: ["DaVinci", "PingOne Protect", "Client App", "Data Collection", "Risk Evaluation"] section_ids: start: Initializing PingOne Protect data collection manually direct: Direct initialization using the Protect interface module: Use the ProtectLifecycle module for Android pause-resume: Pausing and resuming behavioral data capture return-data-davinci-flow: Returning data to a DaVinci flow for risk evaluation --- # Step 2. Integrating PingOne Protect DaVinci flows in Android [icon: circle-check, set=far]PingOne [icon: android, set=fab]Android Integrating your application with PingOne Protect enables you to perform risk evaluations during your customer's authentication flow. ## Initializing PingOne Protect data collection manually The earlier you can initialize data collection, the more data it can collect to make a risk evaluation. Rather than wait to receive a `ProtectCollector` from the server, you can initialize PingOne Protect data collection in your client app, providing your own configuration settings. There are two main methods for early initialization of PingOne Protect data collection on the client: 1. [Direct initialization using the Protect interface](#direct) 2. [Use the ProtectLifecycle module for Android](#module) ### Direct initialization using the Protect interface The DaVinci Client allows you to initialize data collection directly using the `Protect` interface. This provides maximum flexibility in how the collection operates. To directly initialize data collection using the `Protect` interface, complete these steps: 1. Add a configuration object to your code that defines the property values for data collection. The available properties are as follows: | **Parameter** | **Description** | | ---------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | | `envID` | Required. Your PingOne environment identifier.For example, `3072206d-c6ce-ch15-m0nd-f87e972c7cc3` | | `deviceAttributesToIgnore` | Optional. A list of device attributes to ignore when collecting device signals.For example, `AUDIO_OUTPUT_DEVICES` or `IS_ACCEPT_COOKIES`. | | `isBehavioralDataCollection` | When `true`, collect behavioral data.Default is `true`. | | `isConsoleLogEnabled` | When `true`, output SDK log messages in the developer console.Default is `false`. | | `isLazyMetadata` | When `true`, calculate metadata on demand rather than automatically after calling `start`.Default is `false`. | | `customHost` | Optional. Specify a custom host for the Protect API, which can be useful in specific deployment scenarios. | Example code: ```kotlin Protect.config { isBehavioralDataCollection = true isLazyMetadata = true envId = "3072206d-c6ce-ch15-m0nd-f87e972c7cc3" deviceAttributesToIgnore = listOf("deviceId", "androidId", "serialNumber") isConsoleLogEnabled = true } ``` 2. Call the `initialize()` function to start the data collection using the configuration object: ```kotlin Protect.initialize() Log.d("Protect", "Protect data collection initialized.") ``` ### Use the ProtectLifecycle module for Android The DaVinci Client for Android provides the `ProtectLifecycle` module for simplifying the management of data collection. As a DaVinci Client module, it is aware of the current state of authentication, and can automatically pause and resume behavioral data collection when required. Configure the `ProtectLifecycle` module in your DaVinci Client configuration, as with other modules. The available properties are as follows: | **Parameter** | **Description** | | ------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------ | | `envID` | Required. Your PingOne environment identifier.For example, `3072206d-c6ce-ch15-m0nd-f87e972c7cc3` | | `deviceAttributesToIgnore` | Optional. A list of device attributes to ignore when collecting device signals.For example, `AUDIO_OUTPUT_DEVICES` or `IS_ACCEPT_COOKIES`. | | `isBehavioralDataCollection` | When `true`, collect behavioral data.Default is `true`. | | `isConsoleLogEnabled` | When `true`, output SDK log messages in the developer console.Default is `false`. | | `isLazyMetadata` | When `true`, calculate metadata on demand rather than automatically after calling `start`.Default is `false`. | | `customHost` | Optional. Specify a custom host for the Protect API, which can be useful in specific deployment scenarios. | | `pauseBehavioralDataOnSuccess` | When `true`, the DaVinci Client automatically pauses behavioral data collection after successful authentication.Default is `false`. | | `resumeBehavioralDataOnStart` | When `true`, the DaVinci Client automatically starts behavioral data collection when it initializes. | Example code: ```kotlin DaVinci { timeout = 30 module(Oidc) { clientId = "dummy" // ... Other OIDC configuration } module(ProtectLifecycle) { isBehavioralDataCollection = true isLazyMetadata = true envId = "3072206d-c6ce-ch15-m0nd-f87e972c7cc3" deviceAttributesToIgnore = listOf("deviceId") isConsoleLogEnabled = true pauseBehavioralDataOnSuccess = true resumeBehavioralDataOnStart = true } } ``` ## Pausing and resuming behavioral data capture Part of the data collection includes collecting behavioral data, such as how the user interacts with the app, to help when performing evaluations. There are scenarios where you might want to pause the collection of behavioral data: * To reduce memory and processor utilization. Continuously collecting behavioral data beyond authentication could reduce the performance of your client app. * You only want to consider device attribute data when performing PingOne Protect evaluations. You can pause, and also resume behavioral data collection if required. The SDKs provide the `pauseBehavioralData()` and `resumeBehavioralData()` methods for manually pausing and resuming the capture of behavioral data: ```kotlin Protect.pauseBehavioralData() // Pause data collection. Protect.resumeBehavioralData() // Resume data collection. ``` ## Returning data to a DaVinci flow for risk evaluation To perform risk evaluations, the PingOne server requires you to return metadata and optionally behavioural data from the client. On receipt of `ProtectCollector`, use the `collect()` method to collect the required data,and populate the response ready to return to the server. | | | | - | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | | If you haven't already [initialized PingOne Protect data collection](#start) on the client, the `collect()` method uses the parameters within the `ProtectCollector` to initialize PingOne Protect data collection.This method of initializing PingOne Protect data collection means that you can alter collection settings on the server without having to recompile your client apps.However, initializing and returning PingOne Protect data in the same step reduces the amount of behavioural data your client can collect. | When the data is successfully collected, call `node.next()` to submit the data back to the server for evaluation: ```kotlin node.collectors.forEach { when (it) { is ProtectCollector -> { when (val result = it.collect()) { is Success -> { // Data collection successful: Proceed to the next node in the DaVinci flow. node.next() } is Failure -> { // Data collection failed: Implement robust error handling. // Example: Log the error, display an informative message, or implement a retry mechanism. } } } // ... Handle other collector types } } ```