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

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?

Use it like a junior engineer: - give it context - assign a role - define the output format - ask it to explain risks - verify everything manually Example prompt: “You are a senior cloud engineer. Review this Terraform plan for security, cost, reliability, and accidental changes. Return a table with risk level, affected resource, issue, impact, and recommended fix.” AI will not replace cloud engineers. But cloud engineers who know how to use AI properly will be much faster. Where would AI save you the most time? 1. Terraform 2. IAM 3. Logs 4. Cost optimization 5. Documentation 6. Incident response

I started building a practical digital toolkit store for IT admins, cloud engineers, MSPs, and infrastructure teams. The store is here: https://store.cloudpeakify.com The idea is simple: Most teams do not need more theory. They need structured working material they can actually use: - review checklists - prompts - scripts - governance templates - handoff documents - evidence collection workflows - cloud landing zone starters - AI ops review material The first Cloudpeakify kits are focused on: - Windows Server health checks - Active Directory audits - Entra ID governance reviews - GCP landing zone automation - Azure AI landing zone governance - AWS cloud foundation governance - AI sysadmin workflows - MCP security and AI agent operations These are not “magic AI products”. They are practical starter kits for technical people who still want to stay in control. Each product is built around the same principle: Review first. Understand the environment. Collect evidence. Document clearly. Automate only what makes sense. I will keep adding more kits based on real infrastructure problems, especially around cloud cost optimization, governance, identity, automation, and AI-assisted operations. If you are working in IT operations, cloud, MSP delivery, or infrastructure consulting, I would appreciate your feedback: What kind of toolkit would actually save you time? What would you want prepared as scripts, prompts, templates, or checklists? I am building this for practical operators, not for hype.