1. Securing ML Model APIs with Kong Rate Limiting Plugins


    To secure Machine Learning (ML) Model APIs with Kong Rate Limiting plugins, you would typically deploy your ML model as a service and then route requests to this service through Kong API Gateway. Kong allows you to add various plugins to routes, services, or globally which can manage, secure, and monitor your API traffic.

    One of the common security measures is to implement rate limiting to prevent abuse and to ensure fair usage of the API by setting limits on how often a user can call the API. The Kong Plugin resource from the Pulumi Kong package enables you to apply such plugins to your services in Kong.

    Below, I'll provide you a Pulumi program using Python that sets up the following:

    1. A Kong Service: This represents your ML Model API in the Kong gateway.
    2. A Kong Route: This defines the paths that are configured to route requests to your service.
    3. A Kong Rate Limiting Plugin: This enforces rate limits on your service to secure it from abuse.

    Pulumi Program to Secure ML Model APIs with Kong

    The Python program will assume that you have Kong running either on your local machine or a server that is accessible and that you've configured Pulumi to manage your Kong gateway.

    import pulumi import pulumi_kong as kong # Define your ML Model API as a service in Kong. ml_model_service = kong.Service("mlModelService", name="ml-model-service", protocol="http", host="{your_ml_model_host}", # Replace with your ML model host. port=80, # The port on which your ML model server listens. path="/" # The path to your ML model API. ) # Define a route for the ML Model Service. ml_model_route = kong.Route("mlModelRoute", protocols=["http", "https"], methods=["GET", "POST"], # Assuming the ML model responds to GET and POST requests. paths=["/ml-model"], # The route path mapped to your service. service_id=ml_model_service.id ) # Apply a Rate Limiting Plugin to the ML Model Service. # This specifies that only 100 requests per minute are allowed. rate_limiting_plugin = kong.Plugin("rateLimitingPlugin", name="rate-limiting", service_id=ml_model_service.id, config_json=pulumi.Output.from_input({ "second": 100 # Rate limit: 100 requests per second. }).apply(pulumi.Output.secret) # Masking the configuration to keep it secure. ) # Export the service and route information. pulumi.export("ml_model_service_id", ml_model_service.id) pulumi.export("ml_model_route_id", ml_model_route.id) pulumi.export("rate_limiting_plugin_id", rate_limiting_plugin.id)


    • kong.Service: This resource describes the ML Model API as a service in Kong. You will need to specify the host where your ML model API is running.
    • kong.Route: This creates a route in Kong to which client requests to the /ml-model path will be forwarded to your ML Model API service.
    • kong.Plugin: This enables the rate-limiting plugin on your ML Model API service. The config_json parameter allows you to define the specific rate-limiting rules you want to apply. We've chosen to limit to 100 requests per second in this case.

    Make sure to replace {your_ml_model_host} with the actual host address where your ML model service is accessible.

    This program assumes that the ML model API already exists and is operational. The Kong API Gateway acts as a reverse proxy that controls the traffic to your ML Model API and applies the rate limiting based on the configuration provided.

    For more information about the Kong resources with Pulumi, you can visit the following documentation links:

    Before running this Pulumi program, ensure you have Kong accessible and have the Pulumi CLI properly set up with access to manage Kong resources. After deployment, your ML Model API should be secured with the specified rate limits, enhancing its security and robustness.