1. TLS Termination for Secure AI Service APIs.

    Python

    To set up TLS termination for your AI service APIs, you typically need to expose these services through a secured load balancer or a similar resource that can handle TLS certificates and terminate SSL connections. This allows your services to communicate securely with clients by encrypting the traffic.

    In Kubernetes, you often use a Service of type LoadBalancer or an Ingress along with cert-manager for automating the management of TLS certificates. However, for the sake of this tutorial, I'll show you how to set up TLS termination with an AWS Application Load Balancer (ALB) using Pulumi's AWS provider. The ALB will act as a front for your AI service, handling TLS termination and forwarding the requests to your service.

    Here's what you need to do, step by step:

    1. Create an AWS ACM Certificate for your domain, which the ALB will use for TLS termination.
    2. Create a VPC for networking infrastructure.
    3. Create an ALB, attach it to your VPC, and configure it to use the ACM certificate for TLS termination.
    4. Define a Target Group that will route requests to your AI service.
    5. Register your AI service instances with the Target Group.
    6. Create a Listener for the ALB that listens on HTTPS and uses the ACM certificate.

    Let's walk through the code for setting this up using Pulumi with Python:

    import pulumi import pulumi_aws as aws # Assume you have a domain name registered outside of AWS and have set up a DNS validation option elsewhere domain_name = "your-api-domain.com" # Step 1: Create an AWS ACM Certificate certificate = aws.acm.Certificate("service-cert", domain_name=domain_name, validation_method="DNS") # Step 2: Create a VPC vpc = aws.ec2.Vpc("app-vpc", cidr_block="10.0.0.0/16") subnet1 = aws.ec2.Subnet("app-subnet-1", vpc_id=vpc.id, cidr_block="10.0.1.0/24") subnet2 = aws.ec2.Subnet("app-subnet-2", vpc_id=vpc.id, cidr_block="10.0.2.0/24") # Step 3: Create an ALB load_balancer = aws.lb.LoadBalancer("app-lb", internal=False, security_groups=[], # associate security groups as needed subnets=[subnet1.id, subnet2.id]) # Step 4: Define a Target Group for your AI service target_group = aws.lb.TargetGroup("app-tg", port=80, protocol="HTTP", vpc_id=vpc.id) # Step 5: Register your AI service instances with the Target Group (assuming you have instances) # Here you should register your EC2 instances or ECS tasks that run your AI service # Step 6: Create a Listener for the ALB that listens on HTTPS listener = aws.lb.Listener("app-listener", load_balancer_arn=load_balancer.arn, port=443, protocol="HTTPS", ssl_policy="ELBSecurityPolicy-2016-08", # choose a policy that complies with your security requirements certificate_arn=certificate.arn, default_actions=[{ "type": "forward", "target_group_arn": target_group.arn }]) # Export the DNS name of the ALB to access your AI service pulumi.export('load_balancer_dns_name', load_balancer.dns_name)

    This program sets up your AI service with a secure endpoint that clients can interact with over HTTPS. The ACM Certificate provides the SSL/TLS certificate required for encrypted communication. The ALB with a Listener configured for HTTPS handles the actual TLS termination. Requests on port 443 are decrypted by the ALB and then forwarded to the Target Group on port 80. The Target Group routes these requests to the registered service instances.

    Remember to register your actual service instances with the Target Group, which could be EC2 instances or ECS services, depending on where your AI services are hosted.

    You should also consider additional AWS security best practices, for example, using security groups to restrict access to your resources and ensuring your ACM certificate validation is properly set up using DNS or email validation.


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