How a Two-Stage Private AI Pipeline Turns AWS Security Findings into Client Reports

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A technical workflow described for authorised AWS account operators combines Security Hub, GuardDuty, and Amazon Inspector findings into a scheduled Lambda function that ranks and summarises security data. The Lambda uses Amazon Bedrock with Claude Sonnet to generate narrative summaries, compares weekly snapshots, and outputs Markdown and HTML artifacts to Amazon S3. A separate ARM64 Kali Linux EC2 instance then retrieves the Markdown file and runs a local quantised model offline to produce a polished, client-ready HTML report. The two-stage design deliberately separates evidence-oriented internal artifacts from executive-facing reports, preserving traceability while improving readability. The pipeline is framed as an auditable, repeatable process that reduces report-production effort without delegating remediation decisions to AI systems.

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