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Introduction:
Most users treat as a simple chatbot, limiting its potential to basic Q&A. In reality, has evolved into an AI operating system—a platform that supports persistent memory, interactive code execution, real‑world API integration, and automated workflows. Unlocking these layers transforms from a search engine into an engineering powerhouse for cybersecurity, IT, and AI development.
Learning Objectives:
- Understand the seven functional layers of (Models → Prompting → Projects → Artifacts → MCP → Code → Skills → Hooks).
- Implement persistent context and interactive outputs using Projects and Artifacts.
- Build automation pipelines with guardrails using Skills, Hooks, and MCP connectors.
You Should Know
- Prompting Is Still the Foundation – But Now It’s Structured
Many users type one‑off questions and get mediocre results. The shift to structured prompting—defining a role, providing examples, and breaking down steps—dramatically improves output quality. This is the entry point to using as a reasoning engine rather than a parrot.
Step‑by‑Step Guide for Structured Prompting:
- Define a role – “You are a senior cloud security architect.”
- Provide context – “We use AWS with IAM roles and S3 buckets.”
- Give examples – “Here is a well‑written security policy: [paste example].”
- Set the output format – “Return a JSON with keys: action, reason, risk_level.”
- Request step‑by‑step reasoning – “Explain your thought process before the answer.”
Example prompt for hardening an S3 bucket:
Role: AWS security engineer
Task: Review this bucket policy for public exposure
Policy: { "Version": "2012-10-17", "Statement": [{"Effect": "Allow", "Principal": "", "Action": "s3:GetObject", "Resource": "arn:aws:s3:::my-bucket/"}] }
Output: JSON with "is_public", "remediation_commands", and "risk_score".
Reason step by step.
2. Projects = Persistent Memory Layer
Projects allow you to upload files (logs, configs, codebases, PDFs) and set custom instructions that persist across all chats in that project. No more repasting context every session. For IT and security teams, this means maintaining a continuous AI assistant aware of your infrastructure.
Step‑by‑Step to Set Up a Security Operations Project:
1. In .ai, click Projects → New Project.
2. Name it “SOC Playbooks.”
- Under Project Instructions, paste: “You are a SOC analyst. Use the uploaded playbooks and IAM policies. Never suggest commands without approval.”
4. Upload files: `incident_response_playbook.md`, `network_subnets.csv`, `firewall_rules.conf`.
- Start a chat: “Based on the playbook, what’s the first step for a suspicious S3 GetObject spike?”
6. will reference uploaded content automatically.
- To update, replace files or edit instructions—all future chats inherit changes.
3. Artifacts = Interactive Output Layer
Artifacts let generate not just text but working code, interactive dashboards, SVG diagrams, and markdown documents that you can edit, run or export. This turns analysis into actionable tools.
Tutorial: Generate a Network Scanner Artifact
- Prompt : “Create an interactive Python script artifact that scans /24 subnets for open ports 22, 443, and 3389. Use `python3` and output a table.”
- will generate code inside an Artifact window.
- Click Run (if supported) or copy to your terminal:
Save artifact as scanner.py python3 scanner.py 192.168.1.0/24
- For Windows (PowerShell):
After saving artifact as scanner.ps1 .\scanner.ps1 -Subnet "192.168.1.0/24"
- Modify the artifact directly in ’s UI: change port list or add logging.
- MCP + Connectors = Integration Layer (Real‑World Awareness)
The Model Context Protocol (MCP) allows to connect to external services—Google Drive, Slack, GitHub, APIs, and even local filesystems. This makes “real‑world aware” by reading live data and taking actions (with your permission).
Step‑by‑Step: Connect to GitHub using MCP
1. Install the MCP server (requires Node.js):
Linux / macOS / WSL npm install -g @modelcontextprotocol/server-github export GITHUB_PERSONAL_ACCESS_TOKEN=your_token_here
2. For Windows (Command Prompt as admin):
npm install -g @modelcontextprotocol/server-github set GITHUB_PERSONAL_ACCESS_TOKEN=your_token_here
3. In Desktop or compatible client, add to config file:
{
"mcpServers": {
"github": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-github"],
"env": { "GITHUB_PERSONAL_ACCESS_TOKEN": "your_token_here" }
}
}
}
4. Restart , then ask: “List my open pull requests in repo ‘my-org/security-tool’.”
5. For security, use fine‑grained tokens and never share the config file.
5. Code = Execution Layer (Power and Risk)
Code is a command‑line interface that lets run shell commands, edit files, and interact directly with your development or security environment. This is where AI becomes a true co‑engineer—but also where you need strict guardrails.
Safe Usage Guide for Linux / Windows:
- Install (example for Linux):
npm install -g @/code code init
- Run a reviewed command:
code "find ./logs -name '.log' -mtime -1 -exec grep -l 'unauthorized' {} \;" - On Windows (PowerShell with WSL recommended or use .exe):
code "Get-ChildItem -Recurse .log | Select-String 'unauthorized'"
- Critical security rules:
- Never run Code with sudo or admin privileges.
- Pre‑approve commands using `–approve-pattern` flag.
- Always audit the command before execution.
- Use a dedicated sandboxed VM for sensitive testing.
6. Skills + Hooks = Automation with Guardrails
Skills are reusable, parameterized workflows (e.g., “analyze a pcap file” or “generate a CIS benchmark report”). Hooks are triggers that run automatically before or after certain actions, allowing you to enforce policies—like blocking dangerous commands or logging all AI interactions.
Implementing a Security Hook on Linux:
1. Create a hook script `/etc//hooks/pre_command.sh`:
!/bin/bash if echo "$CLAUDE_COMMAND" | grep -q "rm -rf /"; then echo "BLOCKED: Dangerous command detected." >> /var/log/_audit.log exit 1 fi
2. Make it executable: `chmod +x /etc//hooks/pre_command.sh`
- Configure to use hooks by setting environment variable:
export CLAUDE_HOOKS_DIR="/etc//hooks"
- Windows alternative: Use PowerShell script and Scheduled Tasks to monitor ’s process and log commands.
Using a Skill (example: “CIS Audit Skill”):
- Define skill in JSON:
{ "name": "cis_audit", "parameters": ["target_ip", "os_type"], "workflow": [ "nmap -sV $target_ip", "grype $target_ip --output json" ] } - Invoke: “Run cis_audit with target_ip=10.0.0.5, os_type=ubuntu22.”
- Bringing It Together – An AI‑Powered Security Workflow
Combine all layers to automate a real security task: analyse a new Slack alert, query GitHub for related code, generate a fix, and create a Jira ticket.
Step‑by‑Step Integrated Example:
- MCP Connector – reads a Slack message about “S3 bucket exposed.”
- Project Memory – recalls your IAM policies from uploaded files.
- Artifact – generates a Python script to list bucket permissions.
- Code – You approve and run
python3 check_bucket.py my-bucket. - Skill – Invoke the “auto_remediate” skill to apply a read‑only ACL.
- Hook – A pre‑execution hook logs the action and requires manager approval.
- Output – posts a summary back to Slack and creates a GitHub issue via MCP.
What Undercode Say
- Key Takeaway 1: is no longer a chatbot—it’s a programmable AI operating system. Mastering Projects, MCP, and Skills unlocks 10x productivity for IT and security teams.
- Key Takeaway 2: With great power comes great risk. Code and MCP integrations require strict guardrails (hooks, sandboxes, audit logs) to prevent accidental data leaks or system damage.
Analysis: The LinkedIn post by Okan YILDIZ correctly identifies that most users stagnate at “chat.” In cybersecurity, this is dangerous because AI can automate repetitive tasks (log analysis, patch validation, policy checks) but also introduces new attack surfaces—prompt injection, tool abuse, or unintended command execution. The evolution to an “AI OS” means we must treat like a privileged service account: monitor its actions, limit its permissions, and design workflows that assume it will be manipulated. The future belongs to teams that build layered automation with security embedded from the hook level up.
Additionally, the lack of built‑in RBAC (role‑based access control) in current MCP implementations means organizations should wrap APIs with proxy gateways that enforce least privilege. As AI agents gain file system and API access, traditional DevSecOps pipelines must expand to include “AgentSec” – continuous validation of AI actions.
Prediction
Within 18 months, enterprises will replace 30% of their internal IT support and junior SOC analyst tasks with ‑based workflows that use MCP to directly query SIEMs, ticketing systems, and cloud consoles. This shift will force a new certification category: “Certified AI Systems Operator” – blending prompt engineering with IAM, logging, and incident response for AI agents. However, early adopters who skip guardrails will suffer high‑profile breaches from malicious prompt chains. The winning strategy is to treat every AI action as untrusted and enforce micro‑permissions per skill. Start building your hooks and sandboxes today.
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