1. Securing AI Web Applications with AWS WAFv2


    To secure AI Web Applications with AWS WAFv2 (Web Application Firewall version 2), we will create a Web ACL (Access Control List), which acts as a set of rules to monitor and control the incoming and outgoing HTTP(S) traffic to your web application. We'll define rules that include conditions for which AWS WAF should allow or block web requests to your application.

    We'll use the aws.wafv2.WebAcl resource from the Pulumi AWS provider to create the Web ACL, define its default action (what to do if a request does not match any rules), and set up the visibility configuration to integrate with Amazon CloudWatch for monitoring.

    Here's a step-by-step Pulumi Python program that sets up a basic AWS WAFv2 for an AI web application:

    1. Import the necessary Pulumi and AWS modules.
    2. Create an instance of the aws.wafv2.WebAcl resource.
    3. Configure the rules to include common attacks protections like SQL injection and cross-site scripting (XSS).
    4. Define the default action (allow or block) when a request doesn't match any rules.
    5. Set up the visibility configuration for monitoring and logging.

    The following program demonstrates these steps:

    import pulumi import pulumi_aws as aws # Create a WAFv2 WebACL for securing web applications web_acl = aws.wafv2.WebAcl("webAclAIApp", # Choose a descriptive name that indicates the resource's purpose # Set to REGIONAL for resources like ALB, and CLOUDFRONT for use with CloudFront distributions. scope="REGIONAL", # Default action to take when a request doesn't match any rules default_action=aws.wafv2.WebAclDefaultActionArgs( allow={}, # Allow requests by default, you could use block={...} to block all requests by default ), # Configure visibility and logging visibility_config=aws.wafv2.WebAclVisibilityConfigArgs( cloud_watch_metrics_enabled=True, metric_name="webAclAIMetric", sampled_requests_enabled=True, ), # Define rules to filter requests rules=[ # Rule to protect against common SQL injection attacks aws.wafv2.WebAclRuleArgs( name="SQLInjectionRule", priority=1, # Rules are evaluated in order based on priority action=aws.wafv2.WebAclRuleActionArgs( block={}, # Block requests matching this rule ), statement=aws.wafv2.WebAclRuleStatementArgs( sqli_match_statement=aws.wafv2.WebAclRuleStatementSqliMatchStatementArgs( field_to_match=aws.wafv2.WebAclRuleStatementSqliMatchStatementFieldToMatchArgs( all_query_arguments={}, # Evaluate all query arguments for SQL injection patterns ), text_transformation=[ aws.wafv2.WebAclRuleStatementSqliMatchStatementTextTransformationArgs( priority=0, type="URL_DECODE", # Decode URL-encoded requests before inspecting ), ], ), ), ), # Rule to protect against XSS attacks aws.wafv2.WebAclRuleArgs( name="XSSRule", priority=2, action=aws.wafv2.WebAclRuleActionArgs( block={}, ), statement=aws.wafv2.WebAclRuleStatementArgs( xss_match_statement=aws.wafv2.WebAclRuleStatementXssMatchStatementArgs( field_to_match=aws.wafv2.WebAclRuleStatementXssMatchStatementFieldToMatchArgs( all_query_arguments={}, ), text_transformation=[ aws.wafv2.WebAclRuleStatementXssMatchStatementTextTransformationArgs( priority=0, type="URL_DECODE", ), ], ), ), ), ] ) # Export the ARN of the WebACL pulumi.export("web_acl_arn", web_acl.arn)

    This code creates a WebACL to safeguard your AI web application by allowing all requests by default but includes rules to block SQL injection and XSS attacks. It uses text transformation to normalize the query strings before inspection and ensures that they are logged to CloudWatch for visibility.

    You can attach this WebACL to your resources such as an Application Load Balancer or Amazon API Gateway, which host your AI web application, to start protecting against common web attacks.

    To learn more about each argument and property, check the WebAcl documentation.