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AI Blast-Radius Reviews for AWS Changes Using Amazon Bedrock

What this session is about

Cloud infrastructure changes carry hidden risks that manual reviews often miss. This session demonstrates how to build a pre-deployment blast-radius reviewer using Amazon Bedrock that analyzes infrastructure-as-code diffs, IAM policy changes, and deployment metadata to produce structured risk assessments in seconds. Attendees will learn how to design grounded AI workflows that identify affected services, security gaps, cost implications, and rollback considerations, and how to apply Bedrock guardrails for consistent, safe outputs. The session includes a live demonstration and covers practical patterns for integrating AI-generated assessments into existing engineering approval and governance processes without sacrificing speed or reliability.

Playbook

Editorial commentary · what to actually do about this on Monday

The concept
Pre-deployment AI review of IaC diffs, IAM policy changes, deployment metadata. Structured risk assessments in seconds. Bedrock guardrails for safe outputs.
Why it matters
Manual change reviews miss subtleties. AI catches what reviewers gloss over — especially in IAM and network diffs.
The hard parts
AI can hallucinate risk assessments. False alarms erode trust fast.
Playbook moves
(1) Validate AI suggestions against historical incidents. (2) Tune prompts on your environment, not generic templates. (3) Treat AI assessments as input to review, not the review itself.
The surprise
The best AI-generated change review isn't one that lists risks — it's one that lists *what would need to be true* for the change to be safe. That framing forces the human to verify those assumptions, not rubber-stamp. Reframe your prompt accordingly. ---

Independent editorial perspective — not an official AWS or speaker statement. Designed for executives evaluating what to brief their teams on next.

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