A company's data scientists want to create AI/ML training models using Amazon SageMaker. The training models will use large datasets in an Amazon S3 bucket. The datasets contain sensitive information. On average, the data scientists need 30 days to train models. The S3 bucket has been secured appropriately. The company's data retention policy states that all data older than 45 days must be removed from the S3 bucket.
To mitigate a credential stuffing attack against a web-based application behind an Application Load Balancer (ALB), creating an AWS WAF web ACL with a custom rule to block requests containing the known malicious user agent string is an effective solution. This approach allows for precise targeting of the attack vector (the user agent string of the device emulator) without impacting legitimate users. AWS WAF provides the capability to inspect HTTP(S) requests and block those that match defined criteria, such as specific strings in the user agent header, thereby preventing malicious requests from reaching the application.
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