Geolocation Filtering for AI Service Access

Question

Pulumi · Accepted Answer

Geolocation filtering for AI service access typically involves using a cloud provider's geolocation capabilities to control which geographical regions can access your AI services. This form of access control is useful if you have regulatory requirements that dictate where your application's data can be processed, or if you want to refine your services based on the location of your users.

In the context of cloud infrastructure, this usually means configuring a security service like AWS Web Application Firewall (WAF) to include geolocation rules. These rules can allow or block traffic to your AI services based on the country or region from which the requests originate.

To set up geolocation filtering using Pulumi in AWS, you can use the `aws.waf.GeoMatchSet` resource to define your geolocation constraints and a `aws.waf.Rule` to apply these constraints to web requests. Then, you can associate this rule with an `aws.waf.WebAcl` which is used to protect a specific resource, such as an API Gateway exposing your AI services.

Below, I'll provide you with a program that sets up a GeoMatchSet and Rule on AWS WAF for geolocation filtering. In the provided example, we'll be creating a geolocation match condition that allows requests only from a specific country (for example, the United States).

```python
import pulumi
import pulumi_aws as aws

# Create a Geo Match Set which will hold the geolocation rule
geo_match_set = aws.waf.GeoMatchSet("geoMatchSet",
    geo_match_constraints=[
        aws.waf.GeoMatchSetGeoMatchConstraintArgs(
            type="Country",
            value="US",  # This is a two-letter country code (ISO 3166)
        ),
    ])

# Create a rule that leverages the geo_match_set to filter traffic
geo_match_rule = aws.waf.Rule("geoMatchRule",
    metric_name="GeoMatchRuleMetric",
    predicates=[
        aws.waf.RulePredicateArgs(
            type="GeoMatch",
            data_id=geo_match_set.id,  # Reference the ID of the geo_match_set
            negated=False,  # False means requests from the specified country are allowed
        ),
    ])

# Assuming we have an AI service fronted by the API Gateway, and we want to associate
# the WAF rule with the gateway's stage. First, we need to create a Web ACL.
web_acl = aws.waf.WebAcl("webAcl",
    default_action=aws.waf.WebAclDefaultActionArgs(
        type="BLOCK",  # We block requests by default, only requests matching our rules will be allowed
    ),
    metric_name="WebAclMetric",
    rules=[
        aws.waf.WebAclRuleArgs(
            action=aws.waf.WebAclRuleActionArgs( 
                type="ALLOW",  # Requests matching this rule will be allowed
            ),
            priority=1,
            rule_id=geo_match_rule.id,  # Reference the ID of the geo_match_rule
            type="REGULAR",
        ),
    ])

# Export the ID of the Web ACL to be used in associating it with the AI service's API Gateway stage
pulumi.export("web_acl_id", web_acl.id)
```

In this code, we defined a Geo Match Set targeting the United States, created a WAF rule utilizing this match set, and included it in a Web ACL with a default action to block all traffic except for the ones matching our rule.

This program doesn’t set up the actual AI services or the API Gateway, but it does create the necessary AWS WAF components to enforce geolocation filtering. The final step would be to associate this Web ACL with the API Gateway or other AWS resources you are using to expose your AI services.

This is a basic example, and you might need additional configurations based on how your services are set up and what specific rules you need to enforce. Always ensure to adhere to your organization's security guidelines and compliance requirements when implementing such security measures.