This scenario requires data transformation (pseudonymization/masking) before ingestion into XSIAM to ensure sensitive data never resides in raw form within the platform. Both B and C are viable and robust solutions. Option B: Developing a custom Kafka consumer application offers maximum flexibility and control over the pseudonymization logic. It allows for complex masking rules, cryptographic hashing, or tokenization of sensitive fields before forwarding the data to the XSIAM Ingestion API. This ensures the data is transformed at the source, preventing sensitive data from ever reaching XSIAM in its original form. It provides real-time processing. Option C: Using a Kafka Connect SMT (Single Message Transform) is an elegant and native Kafka-ecosystem solution. SMTs allow for light-weight transformations of messages as they pass through Kafka Connect. You can develop a custom SMT or use existing ones (if applicable) to apply masking functions. This keeps the transformation within the Kafka pipeline before XSIAM consumes the data, providing real-time processing and maintaining data integrity. Option A: XSIAM's built-in data masking policies are typically applied after ingestion or at search time, which doesn't prevent the raw sensitive data from entering the platform. Option D: Daily export to S3 introduces significant latency and isn't real-time. Option E: Applying pseudonymization at search-time means the raw sensitive data is already ingested and stored in XSIAM, failing the primary requirement of preventing sensitive data from residing in raw form.