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Exclusive access to Custom Solutions is provided solely for Enterprise users. If you are interested in learning more about the Enterprise Edition, please contact us.
Build Solutions tailored to your unique security workflows, data sources, and operational requirements. This guide walks you through creating custom solutions from scratch, designing dataset schemas, and integrating with your existing security stack.

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When to Build a Custom Solution

Custom solutions are ideal when:
  • Unique Use Cases: Your security workflow doesn’t fit pre-built solutions (ASM, DAST, Vulnerability Assessment)
  • Custom Data Sources: You need to integrate internal tools, proprietary scanners, or organization-specific data
  • Specialized Workflows: You require specific module combinations or custom enrichment steps
  • Integration Requirements: You need to adapt outputs for downstream systems (SIEM, ticketing, data lakes)
  • Compliance Needs: You have specific reporting or audit trail requirements
If you’re extending an existing solution (ASM, DAST, VA) rather than building from scratch, you can copy and modify the pre-built solution workflow as your starting point.

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Step-by-Step: Creating a Custom Solution

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Step 1: Create the Solution

Navigate to the Solutions page and click Create Solution.
1

Name Your Solution

Choose a descriptive name that reflects the solution’s purpose (e.g., “API Inventory”, “Cloud Asset Discovery”, “Third-Party Risk Monitoring”).
2

Add a Description

Provide a clear description of what the solution does. This helps team members understand its purpose at a glance. You can use AI assistance to refine your description.
3

Choose Visual Identity

Card Color: Select a color to make your solution visually distinct on the dashboard. Use consistent colors for related solutions.Icon: Choose an icon that represents your solution’s function. Available icons include security, network, cloud, database, and monitoring symbols.

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Step 2: Create Your Dataset

Datasets store your solution’s results in a structured, queryable format. You’ll define a schema that determines how data is organized and displayed in Insights.
1

Navigate to Insights Tab

After creating your solution, go to the Insights tab and click Create Dataset.
2

Name Your Dataset

Choose a descriptive name that reflects what data you’ll store (e.g., “API Endpoints”, “Discovered Assets”).
3

Define Fields

Click Add key to add fields. Each field needs an icon, name (lowercase with underscores), type (text, int, data, bool, uuid, datetime), and description.
4

Mark Primary Key Fields

Toggle Default on for fields that uniquely identify records. At least one field must be marked as default.
5

Create

Click Create Dataset to save your schema.

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Step 3: Build Your Workflow

Switch to the Builder tab to design your solution’s workflow. Here you’ll combine tools, modules, and scripts to process your inputs and generate the outputs that feed into your dataset.

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Workflow Components

Workflow Building Tutorials & Guides: Start with inputs, use modular design (Input → Processing → Output), add deduplication between stages, and plan for scale with distributed execution. New to workflows? Learn about workflow editor basics and managing nodes.

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Example: API Discovery Workflow

Let’s build a workflow that discovers API endpoints from Swagger/OpenAPI documentation files. This workflow will output structured endpoint data that feeds into the dataset schema we created in Step 2. Workflow:
Implementation:
  1. Input: List of Swagger/OpenAPI documentation URLs (e.g., https://petstore.swagger.io/v2/swagger.json)
  2. Processing: Use the custom module to extract API endpoints and structure the data into a JSON format.
  3. Output: Structured JSON data with endpoint details
Example Output: 
{"url": "http://petstore.swagger.io/v2/user/test", "api_title": "Swagger Petstore", "hostname": "petstore.swagger.io", "domain_name": "swagger.io", "method": "PUT", "body_parameters": ["email", "firstName", "id", "lastName", "password", "phone", "userStatus", "username"], "content_type": "application/json", "source": "https://petstore.swagger.io/v2/swagger.json"}
{"url": "http://petstore.swagger.io/v2/user/test", "api_title": "Swagger Petstore", "hostname": "petstore.swagger.io", "domain_name": "swagger.io", "method": "GET", "content_type": "application/json", "source": "https://petstore.swagger.io/v2/swagger.json", "body_parameters": []}
{"url": "http://petstore.swagger.io/v2/pet/1/uploadImage", "api_title": "Swagger Petstore", "hostname": "petstore.swagger.io", "domain_name": "swagger.io", "method": "POST", "content_type": "multipart/form-data", "source": "https://petstore.swagger.io/v2/swagger.json", "body_parameters": []}

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Step 4: Connect Your Workflow to the Dataset

After building your workflow, you need to tell the platform where to send your data. This connection ensures that when your workflow runs, the results automatically populate your dataset in Insights.

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Select Your Output Node

Click on the node in your workflow that you want to store in your dataset.

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Choose the Dataset

In the right sidebar, look for the Solutions tab. From the dropdown menu, select the dataset you created in Step 2. This links the node’s output directly to your dataset.

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Select the Output File

Choose which file from the node’s output to send to the dataset:
  • Tools typically produce an output.txt file by default
  • Scripts (Python, Bash) can write to any filename—just make sure it matches what your code actually creates.
The output file should contain data in a format that matches your dataset schema (usually JSON with one record per line).

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Step 5: Run and Check Your Data

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Run Tab

Once everything is connected, you are ready to execute your first run with Insights. The platform will process your data according to the workflow you’ve built. In Run tab you can see the output called api-endpoints that we’ve created earlier.

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Insights Tab

In the Insights tab, you can see the data being ingested into the dataset with out-of-the-box filters, views and API.
If your dataset appears empty after a successful run, double-check that your output nodes are properly connected to the dataset as described in Step 4.
Need help? Check out monitoring runs or explore Insights features.

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Access Your Data via API

Once your custom solution is running and populating datasets, you can programmatically access the data through our API. Every dataset includes auto-generated API documentation with ready-to-use code examples.

Working with Insights via API

Learn how to query your datasets, apply filters, and integrate solution data into your downstream systems using our REST API.
Have questions or need help designing your custom solution? Join our Community or contact support.
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