I’m building AI for Cloud Engineers — a community focused on real, practical cloud engineering with AI. Not theory.Not recycled LinkedIn content.Real things engineers can use: - AWS / Azure / GCP tips - Terraform examples - automation scripts - cloud cost optimization - AI prompts for engineers - troubleshooting workflows - infrastructure templates - real-world cloud scenarios I’m thinking about turning this into something bigger than just a community. Possible directions: - free knowledge hub - paid resource library - cloud engineering toolkit - affiliate offers - live workshops - consulting offers - partner-led content - small expert network If you are into cloud, DevOps, automation, AI, FinOps, security, or infrastructure, and you’d like to contribute, collaborate, create content, share tools, or help shape the direction of this community, let me know. I’m open to: - contributors - technical writers - cloud engineers - DevOps engineers - AI builders - FinOps people - affiliate/partnership ideas - people who want to build useful products for engineers The goal is simple: Build a practical place where cloud engineers can learn faster, automate more, and stay ahead of what’s coming. If this sounds interesting, comment below or send me a message.

IAM is one of the best areas for AI-assisted review. Not because AI should decide permissions for you, but because it can quickly highlight risky patterns: - wildcard permissions - overly broad roles - admin-level access - unused-looking access - possible privilege escalation paths This is especially useful in AWS IAM, GCP IAM, Azure RBAC, and service account reviews. If you test this, reply with: 1. AWS / Azure / GCP 2. what type of policy or role you reviewed 3. what risk AI found 4. whether you agreed with the result Never paste real customer IAM data without sanitizing it.

This is where AI can save a lot of time. Logs are noisy, repetitive, and hard to read under pressure. AI is good at summarizing patterns, timelines, repeated errors, and possible root causes. But be careful: Do not paste raw production logs with secrets, tokens, customer names, hostnames, IPs, project IDs, or account IDs. Use cleaned examples only. If you test this, reply with: 1. what type of logs you used 2. what cloud/platform they came from 3. whether the AI found the real issue 4. what you still had to verify manually

But the real problem is not “can AI generate code?” The real problem is: - who reviews the output? - what guardrails does the repo give the AI? - how do you stop AI from creating insecure Terraform? - how do you keep CI/CD changes reviewable? - how do you prevent copy-paste automation from becoming production risk? I built a practical AI Coding / DevOps workflow around this idea: AI should speed up engineering work, but it should not bypass engineering judgment. The workflow I recommend: 1. Give AI clear repo instructions 2. Use a PR review checklist 3. Review Terraform/IaC for security and cost risk 4. Add CI/CD guardrails before merge 5. Keep human approval for anything production-facing If you are using ChatGPT, Cursor, Copilot, Claude, or any AI coding tool for cloud/devops work, this is the part most teams skip. I also made a free starter kit for IT/cloud people who want a safer AI workflow: https://store.cloudpeakify.com/products/ai-it-ops-quickstart-kit-free And for developers / DevOps engineers, I packaged the full AI Coding Bundle here: https://store.cloudpeakify.com/products/ai-coding-bundle-for-devs-and-devops Question for the group: Where are you already using AI in your workflow? - Terraform / IaC - CI/CD - scripts - PR reviews - incident notes - documentation - something else?

Before applying Terraform changes, use AI for a first-pass review. Prompt: “You are a senior cloud engineer. Review this Terraform plan for security risks, cost impact, reliability issues, accidental deletions, and misconfigurations. Return a table with severity, affected resource, issue, impact, and recommended fix.” Use this before asking a human for review. Important: AI does not replace Terraform validation, policy checks, or engineering judgment. Question: Do you currently review Terraform plans manually, with tools, or both?