Geolocation-based Access Policies for AI APIs

Question

Pulumi · Accepted Answer

If you intend to implement geolocation-based access policies for AI APIs, you would typically need a way to manage access control such that only requests from certain geographical regions are allowed to access your API. These kinds of policies can be implemented through identity and access management services provided by the cloud provider where your AI APIs are hosted.

Pulumi offers integration with cloud providers such as Azure, AWS, GCP, and others, allowing you to define infrastructure as code. For this example, I'll demonstrate how to use Pulumi with AWS to create an access policy for an AI API (e.g., an AWS Lambda function that serves as an API endpoint), which only allows requests from specific geographic locations.

AWS WAF (Web Application Firewall) allows you to create web ACLs that can have rules to filter incoming requests based on IP addresses, HTTP headers, HTTP body, URI strings, SQL code injection, and scripting. Although AWS WAF doesn’t support geolocation based rules out of the box, you can use AWS CloudFront coupled with AWS WAF to achieve our objective, as AWS CloudFront can pass the `CloudFront-Viewer-Country` header to your backend, which can be evaluated by AWS WAF.

Here's an overview of what we'll do:
1. Create an AWS Lambda function that serves our AI API.
2. Set up AWS API Gateway to create an API endpoint for the Lambda function.
3. Provision AWS CloudFront to distribute requests to our API Gateway endpoint.
4. Configure AWS WAF to filter requests based on the `CloudFront-Viewer-Country` header.

Let's assume you have already set up your Pulumi environment with AWS configuration.

I'll now write a Pulumi program in Python that provisions the resources needed:

```python
import pulumi
import pulumi_aws as aws

# Create an AWS Lambda function that will serve as our AI API.
# The 'code' parameter should point to the actual location of your Lambda code.
ai_lambda = aws.lambda_.Function(
    "ai_lambda",
    runtime="python3.8",
    role=example_lambda_role.arn,  # Replace with the appropriate role ARN
    handler="index.handler",       # Replace with the path to your handler function
    code=pulumi.FileArchive("./app.zip")
)

# Set up the AWS API Gateway to link to the Lambda function.
api_gateway = aws.apigatewayv2.Api(
    "api_gateway",
    protocol_type="HTTP",
    route_key="ANY /",
    target=ai_lambda.invoke_arn
)

# Provision AWS CloudFront distribution pointing to API Gateway.
cloudfront_distribution = aws.cloudfront.Distribution(
    "cloudfront_distribution",
    origins=[
        aws.cloudfront.DistributionOriginArgs(
            domain_name=api_gateway.api_endpoint,  # API Gateway endpoint
            origin_id="apiGatewayOrigin",
            custom_origin_config=aws.cloudfront.DistributionOriginCustomOriginConfigArgs(
                origin_protocol_policy="https-only"
            )
        )
    ],
    enabled=True,
    default_cache_behavior=aws.cloudfront.DistributionDefaultCacheBehaviorArgs(
        allowed_methods=[
            "GET", "POST", "HEAD", "OPTIONS", "PUT", "PATCH", "DELETE"
        ],
        cached_methods=["GET", "HEAD"],
        target_origin_id="apiGatewayOrigin",
        viewer_protocol_policy="redirect-to-https",
        forwarded_values=aws.cloudfront.DistributionDefaultCacheBehaviorForwardedValuesArgs(
            query_string=True,
            headers=["CloudFront-Viewer-Country"],
        )
    ),
    viewer_certificate=aws.cloudfront.DistributionViewerCertificateArgs(
        cloudfront_default_certificate=True
    ),
)

# Configure AWS WAF to filter requests based on the CloudFront-Viewer-Country header.
waf_web_acl = aws.wafv2.WebAcl(
    "waf_web_acl",
    scope="REGIONAL",
    default_action=aws.wafv2.WebAclDefaultActionArgs(
        allow={}
    ),
    visibility_config=aws.wafv2.WebAclVisibilityConfigArgs(
        cloudwatch_metrics_enabled=True,
        sampled_requests_enabled=True,
        metric_name="webAclMetricName"
    ),
    rules=[
        aws.wafv2.WebAclRuleArgs(
            name="GeoMatchRule",
            priority=0,
            action=aws.wafv2.WebAclRuleActionArgs(
                block={},
            ),
            visibility_config=aws.wafv2.WebAclRuleVisibilityConfigArgs(
                cloudwatch_metrics_enabled=True,
                sampled_requests_enabled=True,
                metric_name="GeoMatchRuleMetric"
            ),
            statement=aws.wafv2.WebAclRuleStatementArgs(
                geo_match_statement=aws.wafv2.WebAclRuleStatementGeoMatchStatementArgs(
                    country_codes=["US"]  # List of allowed country codes
                )
            )
        )
    ]
)

# Associate AWS WAF with CloudFront distribution.
waf_association = aws.wafv2.WebAclAssociation(
    "waf_association",
    resource_arn=cloudfront_distribution.arn,
    web_acl_arn=waf_web_acl.arn
)

# Export the CloudFront Distribution URL so that it can be used to invoke the API.
pulumi.export('distribution_url', cloudfront_distribution.domain_name)
```

This Pulumi program deploys the necessary AWS infrastructure to run an AI API with a geolocation-based access policy. Modify the list of allowed country codes in the `geo_match_statement` to suit your requirements. Also, specify the Lambda function's code and role ARN appropriately for your AI API implementation.

Before deploying this program, ensure you have the AWS Pulumi plugin installed and configured with accurate credentials and default region. Make sure your Lambda function code (zipped code in `./app.zip`) and the IAM Role ARN (`example_lambda_role.arn`) are correctly set up to handle your AI API logic and permissions, respectively.

Remember that the actual enforcement of the access policy, based on geolocation, is achieved by AWS WAF Rule (GeoMatchRule). The requests filtered out by this rule will receive Block responses, effectively preventing access from non-allowed geolocations.