--- title: "Workflow Applications Using Fortanix ACI" slug: "workflow-applications-using-fortanix-aci-1" updated: 2026-06-26T16:30:56Z published: 2026-06-26T16:30:56Z canonical: "support.fortanix.com/workflow-applications-using-fortanix-aci-1" --- > ## Documentation Index > Fetch the complete documentation index at: https://support.fortanix.com/llms.txt > Use this file to discover all available pages before exploring further. # Workflow Applications Using Fortanix ACI > [!NOTE] > NOTE > > Download the Python script for use with this example and unzip the tar file into your working directory. It is best to use the scripts provided in the tar file instead of copying the scripts outlined in the example below to avoid potential formatting issues. > > > > [](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/workflow-scripts.tar.gz(1).zip)workflow-scripts4.07 KB[](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/workflow-scripts.tar.gz(1).zip) ## 1.0 Introduction Workflow graphs are maps that show how generic applications are connected to datasets and other generic applications. These are collaborative objects where multiple users can provide their own objects and approvals. *For more information, refer to the* [*User Guide: Workflows*](/v1/docs/users-guide-create-update-clone-and-delete-workflows-1)*.* This article describes how to create and deploy Fortanix CCM Workflows using a simple data processing example. ### 1.1 A Research Portal for Patient Data This example demonstrates a Fortanix CCM workflow with Python scripts deployed securely using Fortanix Enclave OS. The Workflow provides the most common diseases from patient data. For input data, a synthetic file containing the medical history of fictive patients is used. The data used for this example is available from [*https://synthea.mitre.org/downloads*](https://synthea.mitre.org/downloads)*.* ```bash File: 1K Sample Synthetic Patient Records, CSV[mirror]: 9 MB This is made available via: Jason Walonoski, Mark Kramer, Joseph Nichols, Andre Quina, Chris Moesel, Dylan Hall, Carlton Duffett, Kudakwashe Dube, Thomas Gallagher, Scott McLachlan, Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record, Journal of the American Medical Informatics Association, Volume 25, Issue 3, March 2018, Pages 230–238, https://doi.org/10.1093/jamia/ocx079 ``` The input and output dataset represents patient information collected by a different healthcare provider and contains the following elements: - Patient demographic information - Patient conditions - Patient encounters - Patient medications ### 1.2 Prerequisites - Set up a default config for Amazon Web Service (AWS) with your Access Key/Secret Key. - Run the following command to install Boto: ```bash sudo apt-get install python3-boto3 ``` ## 2.0 Create CCM Workflow ### 2.1 Sign up and Log in 1. Sign in to Fortanix CCM using the URL - [*https://ccm.fortanix.com/*](https://ccm.fortanix.com/). ![LoginPage.png](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/19994190838164.png) **Figure 1: Sign up and Log in** *For more information, refer to the* [*User's Guide: Logging in*](/v1/docs/users-guide-logging-in-1#11-sign-up)*.* 2. Select an account once you have logged into the Fortanix CCM user interface. If you do not have an account create a new account. *For more information, refer to the* [*Quickstart Guide*](/v1/docs/quickstart-guide-1). 3. Create a group. *For more information, refer to the* [*Quickstart Guide*](/v1/docs/quickstart-guide-1) ### 2.2 Invite Users to Join CCM An Administrator invites users who are Data Owners, App Owners, and Output Owners to join a CCM account and create a workflow graph. To invite users, click the **Users** menu item. On the **Users** page, click **INVITE USER** to invite new users to the account. ![invite-user-landing-screen.png](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/21588508634260.png) **Figure 2: Invite Users** *For more information, refer to the* [*User's Guide: Invite Users*](/v1/docs/users-guide-invite-users-1)*.* The new users (Data Owners and App Owners) must join the CCM account and create Datasets and App Configs as described in the following sections. ### 2.3 Create Input and Output Datasets Datasets are the definitions containing the location and access credentials of the data that allow the Enclave OS in the workflow to download the data and upload the data. In this example, we use the AWS S3 bucket to: - Store an encrypted file that will be downloaded and decrypted by the enclave OS. - Upload an encrypted file using the credentials provided in the dataset. - Create an S3 bucket with a directory that is accessible using a URL. #### 2.3.1 Input User (Data Provider) Consider that the Data Owner user has access to sensitive information and wants to allow an Application Owner to process this information. This sensitive data is stored in a `conditions.csv` file. In this example, the file is encrypted, uploaded to a storage solution (AWS S3), and a dataset is configured with credentials and an encryption key for enclave access or processing: 1. If you haven't already, download and untar this tar ball `workflow-scripts.tar.gz`file: ```bash tar xvfz workflow-scripts.tar.gz ``` 2. Obtain a copy of the CSV sample data (also available here: [*https://synthea.mitre.org/downloads*](https://synthea.mitre.org/downloads)): ```bash wget https://synthetichealth.github.io/synthea-sample-data/downloads/synthea_sample_data_csv_apr2020.zip unzip synthea_sample_data_csv_apr2020.zip ``` You will only be using the file `csv/conditions.csv` to encrypt this file, generate a key locally, encrypt the file and store the key in a KMS securely: 1. Run the following command to generate an encryption key: ```bash xxd -u -l 32 -p /dev/random | tr -d '\n' > ./key.hex ``` 2. Use `aes_256_gcm.py` script included in the tar file that you downloaded. Run the following command to encrypt the file: ```bash ./workflow_scripts/aes_256_gcm.py enc -K ./key.hex -in ./csv/conditions.csv -out ./conditions.csv.enc ``` 3. Run the following command to upload the encrypted file to a secure storage location such as AWS S3: ```bash aws s3 --profile upload cp ./conditions.csv.enc ``` 4. Generate a pre-signed URL to access the file to avoid inserting the whole AWS SDK in the example. Use the `presign.py` script included in the tar file and run the following command: ```bash ./presign.py default download conditions.csv.enc 86400 ``` Where, `S3-directory` is the directory of your S3 bucket directory. As an example, the output from the above command will be as shown below: ```bash https://fortanix-pocs-data.s3.amazonaws.com/conditions.csv.enc?AWSAccessKeyId=&Signature=PcpH99nszG2%2Fv85z4IbgwgVDywc%3D&Expires=1613817035 ``` The output consists of two parts: At this point, users accessing the URL above with full query string parameters will be able to download the encrypted file until it expires in 1 day. If the URL above expires, the dataset will need to be updated with new query parameters > [!NOTE] > NOTE > > If you access the URL without the string following '`?`', you will get a 403 forbidden. > > Hence, treat the query parameters as access credentials. - The location - `https://fortanix-pocs-data.s3.amazonaws.com/conditions.csv.enc` - Query parameters: `AWSAccessKeyId=&Signature=PcpH99nszG2%2Fv85z4IbgwgVDywc%3D&Expires=1613817035` The query parameters must be base64 encoded for the dataset definition using the following command: ```bash echo -n ‘AWSAccessKeyId=&Signature=PcpH99nszG2%2Fv85z4IbgwgVDywc%3D&Expires=1613817035’ | base64 ``` - The output will be as follows: ```bash QVdTQWNjZXNzS2V5SWQ9QUtJQVhPNU42R0dOQ05WMzUzV1MmU2lnbmF0dXJlPVBjcEg5OW5zekcyJTJGdjg1ejRJYmd3Z1ZEeXdjJTNEJkV4cGlyZXM9MTYxMzgxNzAzNQ== ``` Perform the following steps to create an input dataset: 1. Click the **Datasets** menu item in the CCM UI left navigation panel and click **CREATE DATASET** to create a new dataset. ![dataset-landing-screen.png](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/21588521338772.png) **Figure 3: Create a New Dataset** 2. On the **Create new dataset** form: ![create-dataset-updated-field.png](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/21588521343764.png) **Figure 5: Create Input Dataset** 1. **Name** – Enter the name of the dataset. For example, **Conditions Data**. 2. **Description** (optional) – Enter the description for the dataset. For example: Patients with associated conditions. 3. **Labels** (optional) – Attach one or more key-value labels to the dataset. For example, `Key`: **Location** and `Value`: **East US** 4. **Group** – Select the required group name from the drop down menu to associate this dataset with that group. 5. **Location** – The AWS S3 URL where data can be accessed. *For example: https://fortanix-pocs-data.s3.amazonaws.com/conditions.csv.enc* 6. **Long Description** (optional) – Enter the content in GitHub-flavoured Markdown file format. You can also use **Fetch Long Description** to get the Markdown file from an external URL. Ensure that Cross-origin resource sharing (CORS) is enabled on the external URL so that Fortanix CCM can access it. *For more information, refer to the steps for* [*AWS*](https://docs.aws.amazon.com/AmazonS3/latest/userguide/enabling-cors-examples.html) *and* [*Azure*](https://docshield.tungstenautomation.com/Printix/en_US/help/admin/Printix_admin/t_how_to_set_up_azure_blob_storage_cors.html). ![Fetch Long Description Dialog Box.png](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/17522483931156.png) **Figure 4: Fetch Long Description Dialog Box** The following is the sample long description in Markdown format: ```bash - Strikethrough Text ~~It is Strikethrough test..~~ - Blockquote Text > Test Blockquote. - Bold **A sample Description.** - Italic *It is Italics* - Bold and Italic ***Bold and Italics text*** - Link This is [Website](https://www.fortanix.com/)? ``` 7. **Credentials** – the credentials needed to access the data. The credentials must be in the correct JSON format and consist of: ```bash {  "query_string": "",  "encryption": {    "key": ""  } } ``` For example: ```bash {  "query_string": "QVdTQWNjZXNzS2V5SWQ9QUtJQVhPNU42R0dOQ05WMzUzV1MmU2lnbmF0dXJlPVBjcEg5OW5zekcyJTJGdjg1ejRJYmd3Z1ZEeXdjJTNEJkV4cGlyZXM9MTYxMzgxNzAzNQ==",  "encryption": {    "key": "63F0E4C07666126226D795027862ACC5848E939881C3CFE8CB3EB47DD7B3D24A"  } } ``` > [!TIP] > TIP > > Before saving the dataset, it is a good idea to verify that the JSON is correct. After saving the dataset you will not be able to view the credentials and access to data may fail. Any online JSON formatting tool can be used to validate that the JSON is correct. > [!NOTE] > NOTE > > - The credentials are only passed as text when creating the dataset over an HTTPS connection. > - It is then stored in a KMS (Fortanix-Data-Security-Manager) and only accessible to approved enclaves. > - Not even the Data Owner can retrieve the credentials. - Query parameters that were base64 encoded. - The key that was used to encrypt the file. 3. Click **CREATE DATASET** to create the input dataset. #### 2.3.2 Output User (Data Receiver) Once the data has been received by the Enclave, the user application will run within the Enclave and generate output data (processed data). This data should be encrypted (using your key) before being uploaded to an untrusted store. This is achieved by defining an output dataset to be used by the workflow. Perform the following steps: 1. Run the following command to generate an encryption key: ```bash xxd -u -l 32 -p /dev/random | tr -d '\n' > ./key_out.hex ``` 2. Use the `presign.py` script included in the tar file. Run the following command to generate a presign URL for the upload: ```bash ./presign.py default upload conditions_output.csv.enc 86400 ``` Where `s3-directory` is the directory of your S3 bucket directory. As an example, the output of the above command will be as shown below: ```bash https:/https://fortanix-pocs-data.s3.amazonaws.com/conditions_output.csv.enc?AWSAccessKeyId=Signature=HFvhxaiKY0cGR9XqgGLp5zcAWac%3D&Expires=1613817880 ``` The output consists of two parts: - The location: `https://fortanix-pocs-data.s3.amazonaws.com/conditions_output.csv.enc` - Query parameters: `AWSAccessKeyId=Signature=HFvhxaiKY0cGR9XqgGLp5zcAWac%3D&Expires=1613817880` The query parameters must be base64 encoded for the dataset definition using the following command: ```bash echo -necho -n ‘AWSAccessKeyId=Signature=HFvhxaiKY0cGR9XqgGLp5zcAWac%3D&Expires=1613817880’ | base64 ``` - The output will be as follows: ```bash QVdTQWNjZXNzS2V5SWQ9QUtJQVhPNU42R0dOTk1SWFZLUEEmU2lnbmF0dXJlPUhGdmh4YWlLWTBjR1I5WHFnR0xwNXpjQVdhYyUzRCZFeHBpcmVzPTE2MTM4MTc4ODA= ``` 3. Create an output dataset with the following sample values: - **Name** – the dataset name. For example: `Conditions processing output`. - **Description** (optional) – the dataset description. - **Labels** (optional)– attach one or more key-value labels to the dataset. For example: **Key**: `Location` and **Value**: `East US`. - **Group** – Select the required group name from the drop down menu to associate this dataset with that group. - **Location** – the URL where data can be accessed. *For example: https://fortanix-pocs-data.s3.amazonaws.com/conditions_output.csv.enc* - **Long Description** (optional)– enter the content in GitHub-flavoured Markdown file format. You can also use **Fetch Long Description** to get the Markdown file from an external URL. Ensure that Cross-origin resource sharing (CORS) is enabled on the external URL so that Fortanix CCM can access it. *For more information, refer to the steps for* [*AWS*](https://docs.aws.amazon.com/AmazonS3/latest/userguide/enabling-cors-examples.html) *and* [*Azure*](https://docshield.tungstenautomation.com/Printix/en_US/help/admin/Printix_admin/t_how_to_set_up_azure_blob_storage_cors.html). ![Fetch Long Description Dialog Box.png](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/17522483931156.png) **Figure 6: Fetch Long Description Dialog Box** The following is the sample long description in Markdown format: ```bash - Strikethrough Text ~~It is Strikethrough test..~~ - Blockquote Text > Test Blockquote. - Bold **A sample Description.** - Italic *It is Italics* - Bold and Italic ***Bold and Italics text*** - Link This is [Website](https://www.fortanix.com/)? ``` - **Credentials** – the credentials needed to access the data. The credentials must be in the correct JSON format and consist of: ```bash { {  "query_string": "",  "encryption": {    "key": ""  } } ``` For example: ```bash {  "query_string": "QVdTQWNjZXNzS2V5SWQ9QUtJQVhPNU42R0dOTk1SWFZLUEEmU2lnbmF0dXJlPUhGdmh4YWlLWTBjR1I5WHFnR0xwNXpjQVdhYyUzRCZFeHBpcmVzPTE2MTM4MTc4ODA=","encryption": {    "key": "63F0E4C07666126226D795027862ACC5848E939881C3CFE8CB3EB47DD7B3D24A"  } } ``` > [!TIP] > TIP > > Before saving the dataset, it is a good idea to verify that the JSON is correct. After saving the dataset you will not be able to view the credentials and access to data may fail. Any online JSON formatting tool can be used to validate that the JSON is correct. > [!NOTE] > NOTE > > - The credentials are only passed as text when creating the dataset over an HTTPS connection. > - It is then stored in a KMS (Fortanix Data Security Manager) and only accessible to approved enclaves. > - Not even the Data Owner can retrieve the credentials. - Query parameters that were base64 encoded. - The key that was used to encrypt the file. ![create-dataset-updated-field.png](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/21588521343764.png) **Figure 7: Create Output Dataset** ### 2.4 ACI Application #### 2.4.1 Create a General Purpose Python Docker Image Create a docker image that will run arbitrary protected python code. The following files are used. These files are included in the tar file provided with this example: `Dockerfile`: ```bash FROM python:3.6 RUN apt-get update && apt-get install -y python3-cryptography python3-requests python3-pandas RUN mkdir -p /opt/fortanix/enclave-os/app-config/rw RUN mkdir -p /demo/code /demo/input COPY ./start.py ./utils.py ./describe.py /demo/ CMD ["/demo/start.py"] ``` `start.py`: This file is the main entry point into the application. ```bash !/usr/bin/python3   import os import utils import hashlib from subprocess import PIPE, run def main():    input_folder="/opt/fortanix/enclave-os/app-config/rw"        command = ["/usr/bin/python3", "/demo/describe.py"]        # This downloads and decrypts all input data. File names are the object names from app config.    for i in utils.read_json_datasets("input"):        decrypted = utils.get_dataset(i)        open(input_folder + i.name, 'wb').write(decrypted)                # Add the file as input argument for our script        command.append(input_folder + i.name)            print("Running script")    result = run(command, stdout=PIPE, stderr=PIPE, universal_newlines=True)        # For simplicity uploading just stdout/stderr/returncode.    utils.upload_result("output", result.returncode, result.stdout, result.stderr)        print("Execution complete") if __name__ == "__main__":    main() ``` `utils.py`: This file contains the set of library functions. ```bash !/usr/bin/env python3 import os import sys import string import json import requests import base64 import hashlib from subprocess import PIPE, run from cryptography.hazmat.backends import default_backend from cryptography.hazmat.primitives.ciphers import (    Cipher, algorithms, modes ) NONCE_SIZE=12 TAG_SIZE=16 KEY_SIZE=32 PORTS_PATH="/opt/fortanix/enclave-os/app-config/rw/" def convert_key(key_hex):    key=key_hex.rstrip();    if len(key) != 64:        raise Exception("Key file must be 64 bytes hex string for AES-256-GCM")    if not all(c in string.hexdigits for c in key) or len(key) != 64:        raise Exception("Key must be a 64 character hex stream")    return bytes.fromhex(key) def encrypt_buffer(key, data):    nonce=os.urandom(NONCE_SIZE)    cipher = Cipher(algorithms.AES(key), modes.GCM(nonce), backend=default_backend())    encryptor = cipher.encryptor()    return nonce + encryptor.update(data.encode()) + encryptor.finalize() + encryptor.tag def decrypt_buffer(key, data):    tag=data[-TAG_SIZE:]    nonce=data[:NONCE_SIZE]    cipher = Cipher(algorithms.AES(key), modes.GCM(nonce, tag), backend=default_backend())    decryptor = cipher.decryptor()    return decryptor.update(data[NONCE_SIZE:len(data)-TAG_SIZE]) + decryptor.finalize() class JsonDataset:    def __init__(self, location, credentials, name):       self.location = location       self.credentials = credentials       self.name = name def read_json_datasets(port):    ports=[]    for folder in os.listdir(PORTS_PATH + port):        subfolder=PORTS_PATH + port + "/" + folder        if os.path.exists(subfolder + "/dataset"):            credentials=json.load(open(subfolder + "/dataset/credentials.bin", "r"))            location=open(subfolder + "/dataset/location.txt", "r").read()            ports.append(JsonDataset(location, credentials, folder))    return ports def get_dataset(dataset):    url = dataset.location + "?" + base64.b64decode(dataset.credentials["query_string"]).decode('ascii')    r = requests.get(url, allow_redirects=True)    r.raise_for_status()    print("Retrieved dataset from location: " + dataset.location)    key = convert_key(dataset.credentials["encryption"]["key"])    return decrypt_buffer(key, r.content) class RunResult:    def __init__(self, returncode, stdout, stderr):        self.returncode = returncode        self.stdout = base64.b64encode(stdout.encode()).decode('ascii')        self.stderr = base64.b64encode(stderr.encode()).decode('ascii') def upload_result(port, returncode, stdout, stderr):    result=RunResult(returncode, stdout, stderr)    json_str=json.dumps(result.__dict__)    for dataset in read_json_datasets(port):        url = dataset.location + "?" + base64.b64decode(dataset.credentials["query_string"]).decode('ascii')        key = convert_key(dataset.credentials["encryption"]["key"])        print("Writing output to location: " + dataset.location)        requests.put(url, encrypt_buffer(key, json_str)) ``` `describe.py`: This file contains the custom code called by `start.py`. ```bash !/usr/bin/python3   import pandas as pd import sys for i in sys.argv[1:]:    df_do = pd.read_csv(i)    print("Dataset: " + i + "\n")    print(df_do['DESCRIPTION'].describe())    print("") ``` A standard docker build and docker push command must be used to create your docker and push to your registry. For example: ```bash docker build -t /simple-python-sgx . docker push /simple-python-sgx ``` #### 2.4.1 Create asn ACI Application and Image Creation This section describes how to register the docker image in CCM and convert it to an SGX format. 1. To create an ACI application using Fortanix CCM UI, click the **Applications** menu item. 2. On the **Applications** page, click **+ADD APPLICATION** to create an application. ![Fig-5-application-landing-screen.png](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/23041241937044.png) **Figure 8: Create an ACI Application** *For more information, refer to* [*Add ACI Application*](/v1/docs/users-guide-add-and-edit-an-application-1#60-add-aci-application)*.* ![add-ACI-application-landing-page.png](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/21588508676244.png) **Figure 9: Create an ACI Application** 3. Create an image of the application. ![add-ACI-image-landing-screen.png](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/21588521364500.png) **Figure 10: Create an Application Image** *For more information, refer to* [*Create Image for ACI Application*](/v1/docs/users-guide-create-an-image-1#50-create-image-for-aci-application)*.* 4. Navigate to **Tasks** menu item in the Fortanix CCM UI left navigation panel and fetch the domain whitelisting tasks: ![pending-landing-screen-ACI.png](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/21588508686740.png) **Figure 11: Fetch Tasks for Pending** ![](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/image(92).png) **Figure 12: Fetch Tasks for Approval** 5. Approve the tasks. ![WorkflowEx12.png](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/16493617721108.png) **Figure 13: Approve Domain Whitelist Task** ![WorkflowEx13.png](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/16493604942996.png) **Figure 14: Approve Build Whitelist Task** ### 2.5 Create Application Configuration The Docker Image recognizes the Python script using an Application Configuration which defines the ports. Perform the following steps to create an ACI application configuration: 1. Navigate to **Applications** → **Configurations** menu item in the Fortanix CCM UI. 2. Click **ADD CONFIGURATION** to add a new configuration. ![add-configuration-landing-screen.png](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/21588521382036.png) **Figure 15: Create App Configuration** 3. In the **ADD APPLICATION CONFIGURATION** window, fill in the following: > [!NOTE] > NOTE > > For ACI applications, Fortanix permits only files in the path `/opt/fortanix/`. ![ADD APP CONFIGURATION.png](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/19994374709012.png) **Figure 16: Save Configuration** 1. **Image** – Select the application image, such as `/simple-python-sgx:latest`, for which you want to create a configuration. Where,`<my-registry>` is the location of your docker registry 2. **Configuration Name** – Enter a name for the configuration. 3. **Group** – Select the appropriate group name from the drop down menu to associate this application with that group. 4. **Description** – Enter the description of the configuration. 5. **Ports** – Specify the ports to be used in the workflow. Multiple ports can be added, depending on the required connections. For example: `input`, `output`, `heartbeat`, and so on. 6. **Labels** – Attach one or more key-value labels to the application configuration. 7. **Configuration items** – These are key-value pairs used to configure the application. 4. Click **SAVE CONFIGURATION** to save the configuration. ![SAVE BUTTON.png](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/21588521388564.png) **Figure 17: Configuration Saved** ### 2.6 Create a Workflow Perform the following steps to create a workflow: 1. Click the **Workflows** menu item in the Fortanix CCM UI left navigation panel. 2. On the **Workflows** page, click **+ WORKFLOW** to create a new workflow. ![add-workflow-button.png](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/21588508711316.png) **Figure 18: Create Workflow** 3. In the **CREATE NEW WORKFLOW** dialog box, enter the Workflow **Name**, assign it to a **Group,** and provide a **Description** (optional). Click **CREATE WORKFLOW** to access the Workflow graph. ![CreateWorkflow.png](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/21588508719124.png) **Figure 19: Created the Workflow** 4. To add an application to the Workflow graph, drag the "App" icon and drop it into the graph area. Click the **+ APPLICATION**. In the **ADD APPLICATION** dialog box, select an existing application name and image. For example: **/simple-python-sgx:latest** from the list of available application images. Where, `<my-registry>` is the location of your registry. ![CreateWorkflow-AddApp.png](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/21588508724884.png) **Figure 20: Select Application and Image** 5. Click **+ ADD NEW CONFIGURATION** to either add a new application configuration or select an existing one. ![Screenshot (43).png](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/20193107208724.png) **Figure 21: Add Application Configuration** 6. Add input and output datasets to the Workflow graph by dragging the dataset icon and placing it in the graph area. Click **+ DATASET**. In the **ADD DATASET** dialog box, select from an existing dataset created in the previous section. Assign the dataset to a group using the **SELECT A GROUP** option. ![AddDataset.png](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/21588521425812.png) **Figure 22: Add Dataset to Workflow** 7. Establish connections between the applications and input/output datasets. To do this, connect the Input Dataset to the Application by selecting the "Input" Target Port. Repeat this process to connect the Application to the Output Dataset with the "Output" Target Port. ![SelectPort.png](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/21588521432980.png) **Figure 23: Create Connection** 8. After the Workflow is complete, click **REQUEST APPROVAL** to initiate the approval process for the Workflow. ![WorkflowApproval.png](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/21588521440916.png) **Figure 24: Request Workflow Approval** > [!WARNING] > WARNING > > When a draft Workflow is submitted for approval, it will be removed from the drafts list and editing it directly will no longer be possible once it is in a "pending" or "approved" state. 9. The workflow remains in a pending state until it receives approval from all users. In the **Pending** menu item, click **SHOW APPROVAL REQUEST** to approve a Workflow. ![WorkflowApprovalPending.png](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/21588508776340.png) **Figure 25: Workflow Pending Approval** 10. In the **APPROVAL REQUEST - CREATE WORKFLOW** dialog box, you can either **APPROVE** or **DECLINE** a workflow. ![show-approval-request-dialog-box.png](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/21588508782740.png) **Figure 26: Approve the Workflow** > [!NOTE] > NOTE > > - A user can also approve/decline a Workflow from the CCM Tasks menu item. > - Notice that the users who have approved the workflow have a green tick ![WorkflowEx27.png](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/16493651016212.png) against their icon. 11. All the users of a Workflow must approve the Workflow to finalize it. If a user declines a Workflow, the Workflow is rejected. When all the users approve the Workflow, it is deployed. 1. CCM configures apps to access the datasets. 2. CCM creates the Workflow Application Configs. 3. CCM returns the list of hashes needed to start the apps. ### 2.7 Run the Application After a Workflow is approved by all the users, the Applications will have the Workflow Application Configurations provided to them. This configuration has information such as which Datasets or Apps they are connected to, any user-provided files or values to be provided within an enclave, and so on. We provide a configuration to applications using an identifier passed as an input argument. This identifier is a sha256sum of items that you need to secure from the configuration and workflow. Fortanix CCM will also embed this identifier inside the certificates it issues so that it is clear what configuration is used for the KMS to allow access to credentials. It embeds this inside a subject alternate name: `<identifier>.<mrenclave>.id.fortanix.cloud` With the identifier above, the KMS that stores the dataset credentials will authenticate and give credentials only to applications that present a proper certificate. When the application starts, CCM will keep track of which applications are allowed to access which configurations using the identifier. Perform the following steps to view the Application Identifier: 1. Click the application in the approved Workflow graph. ![WorkflowDetailedView.png](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/21588508795540.png) **Figure 27: Workflow Application Detailed View** 2. In the detailed view of the Workflow application, copy the value of **Runtime configuration hash**. This ID is used to run the application. ![EOSExample23b1.png](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/16493604977300.png) **Figure 28: Copy the App Identifier** 3. To deploy the ACI application using Azure portal, perform the steps as mentioned in *Section 2 - Deploy Confidential ACI Group Using Azure Portal*. In the "Custom deployment" screen, for the **App Config Id** field, enter the Application Identifier copied in *Step 2* above. ![Screenshot from 2023-11-30 12-41-36 - Copy.png](https://cdn.us.document360.io/c3bd85d2-4ad8-4d85-9f60-f1c168a3aad9/Images/Documentation/21401808316820.png) **Figure 29: Azure ACI Deployment** 4. When the App Owner starts the application with the application config identifier. The Data Output Owner can view the output using the following steps: 1. Run the following command to download the output file: ```bash aws s3 --profile download cp s3:/conditions_output.csv.enc . ``` For example: ```bash aws s3 --profile download cp s3://fortanix-pocs-data/conditions_output.csv.enc . ``` 2. Run the following command to decrypt the file: Use the `aes_256_gcm.py` script provided in the tar file. ```bash ./aes_256_gcm.py dec -K ./key.hex -in ./conditions_output.csv.enc -out ./output.txt $ cat output.txt | jq . {  "returncode": 0,  "stdout": "RGF0YXNldDogL29wdC9mb3J0YW5peC9lbmNsYXZlLW9zL2FwcC1jb25maWcvaW5wdXQvY3hoY2Z4ZHZsCgpjb3VudCAgICAgICAgICAgICAgICAgICAgICAgICAgIDgzNzYKdW5pcXVlICAgICAgICAgICAgICAgICAgICAgICAgICAgMTI5CnRvcCAgICAgICBWaXJhbCBzaW51c2l0aXMgKGRpc29yZGVyKQpmcmVxICAgICAgICAgICAgICAgICAgICAgICAgICAgIDEyNDgKTmFtZTogREVTQ1JJUFRJT04sIGR0eXBlOiBvYmplY3QKCg==",  "stderr": "" } ``` The file now has the expected output: ```bash $ cat output.txt | jq -r .stdout | base64 -d Dataset: /opt/fortanix/enclave-os/app-config/input/cxhcfxdvl count                               8376 unique                               129 top           Viral sinusitis (disorder) freq                                1248 Name: DESCRIPTION, dtype: object ``` 5. When the App Owner starts the application with the application config identifier: 1. Applications will request an attestation certificate from the NodeAgent with the identifier as part of the report data. 2. The application requests an application certificate from the NodeAgent. 3. The Fortanix CCM verifies that the application is allowed to access the configuration. 4. The application requests from Fortanix CCM its configuration by providing its certificate provisioned above as an authentication mechanism. 5. The CCM does certificate authentication, extracts the application identifier from the certificate, and sends back the configuration corresponding to that identifier. 6. The application verifies and applies the configuration hash. 7. The application gets the credentials from URLs in the config. 8. The application authenticates and reads or writes data from the datasets. Fortanix Data Security Manager (DSM) is the world’s first cloud service secured with Intel® SGX. With Fortanix DSM, you can securely generate, store, and use cryptographic keys and certificates, as well as other secrets such as passwords, API keys, tokens, or any blob of data. Your business-critical applications and containers can integrate with Fortanix DSM using legacy cryptographic interfaces (PKCS#11, CNG, and JCE) or using the native Fortanix DSM RESTful interface. ## Related - [Add Application](/fortanix-ccm-add-and-edit-an-application.md) - [Logging In](/fortanix-ccm-logging-in.md)