1. Whitelisting IPs for Secure AI Model Endpoints


    To whitelist IP addresses for securing AI model endpoints, you typically need to utilize cloud provider services such as firewalls, security groups, or network access control lists (ACLs). These services allow you to specify rules that grant or deny traffic to and from your endpoints based on IP addresses.

    In this example, we will use Pulumi to configure a security group in AWS. A security group acts as a virtual firewall that controls the inbound and outbound traffic for your resources. We will create a new security group and add a rule that allows inbound traffic from a specific IP address to an AI model endpoint, presumably hosted on an Amazon EC2 instance or an AWS Elastic Load Balancer.

    Here's what our Pulumi program will do:

    • Create a new AWS security group in a specified region.
    • Add a security group rule to whitelist an IP address (or a range of IP addresses).
    • Ensure that the AI model endpoint (assumed to be an EC2 instance) is associated with the security group.

    Before you run this Pulumi program, make sure you have:

    • Installed the Pulumi CLI and set up the AWS provider.
    • Configured your AWS credentials.

    Now, let's delve into the Pulumi program written in Python to accomplish IP whitelisting for secure AI model endpoints.

    import pulumi import pulumi_aws as aws # Define the IP addresses that you want to whitelist. # You should replace "" with the actual IP range you intend to whitelist. whitelisted_ip_range = "" # Create a new security group in AWS for your AI model endpoint. ai_model_sg = aws.ec2.SecurityGroup('aiModelSecurityGroup', description='Allow inbound traffic from whitelisted IPs to AI model endpoint', ingress=[ # Ingress rule to allow traffic from the whitelisted IP range { 'from_port': 443, # Assuming the AI model endpoint serves on HTTPS port 'to_port': 443, 'protocol': 'tcp', 'cidr_blocks': [whitelisted_ip_range] } ]) # (Optional) If you already have an AI model endpoint such as an EC2 instance, # you can associate the security group with the instance by attaching the security group. # Replace 'instance_id' with the actual ID of your AI model endpoint instance. # ai_model_instance = aws.ec2.Instance.get('aiModelInstance', 'instance_id') # ai_model_sg_attachment = aws.ec2.SecurityGroupAttachment('aiModelSgAttachment', # instance_id=ai_model_instance.id, # security_group_id=ai_model_sg.id) # Export the security group ID for reference. pulumi.export('security_group_id', ai_model_sg.id)

    In this program, ai_model_sg is the security group we created. It has one ingress rule, which allows inbound HTTPS traffic from the IP range you specified in whitelisted_ip_range.

    Note that the ingress rule specifies a from_port and to_port set to 443, which is the default port for HTTPS traffic. If your AI model endpoint is using a different port, you should adjust those values accordingly.

    Moreover, the ingress rule uses cidr_blocks to specify the range of IP addresses allowed. You can list multiple CIDRs in this array if you need to whitelist more than one IP range.

    To apply this configuration, you would run pulumi up in your terminal. This would prompt Pulumi to perform the update, showing you a preview of the changes and asking for confirmation before provisioning the resources with AWS.

    After the resources are created, the output security_group_id can be used to reference the security group in other parts of your AWS infrastructure setup or to share with your colleagues or systems to acknowledge the newly created rules.