Chapter 10 – Practical Implementation Guide

10.1 Overview

This chapter provides a step‑by‑step blueprint for building, deploying, and maintaining the AI‑powered security stack described in the book. It assumes a small security team (5–15 analysts) with basic DevOps skills.

10.2 Prerequisites

• Hardware – At least one GPU‑enabled VM (e.g., NVIDIA T4) and one general‑purpose VM.
• Software – Docker, Docker‑Compose, Python 3.10+, Node 18+, and Terraform (optional).
• Credentials – Access to cloud provider (AWS, GCP, Azure) or on‑prem infrastructure.
• Team – 1–2 data scientists, 1–2 security engineers, 1–2 analysts.

10.3 Infrastructure Setup

1. Provision VMs – Use Terraform to spin up:
   • elastic-node (4 vCPU, 16 GB RAM)
   • gpu-node (1 x NVIDIA T4, 8 GB VRAM)
2. Networking – Create a private subnet; expose only necessary ports (5601 for Kibana, 9200 for Elasticsearch, 5000 for FastAPI).
3. Security Groups – Restrict inbound traffic to internal IPs; enable TLS termination.

10.4 Elastic Stack Installation

# On elastic-node
sudo apt-get update && sudo apt-get install -y apt-transport-https openjdk-11-jre
wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.17.0-amd64.deb
sudo dpkg -i elasticsearch-7.17.0-amd64.deb
sudo systemctl enable elasticsearch && sudo systemctl start elasticsearch

# Install Kibana
wget https://artifacts.elastic.co/downloads/kibana/kibana-7.17.0-amd64.deb
sudo dpkg -i kibana-7.17.0-amd64.deb
sudo systemctl enable kibana && sudo systemctl start kibana

• Enable the SIEM app via Kibana UI.
• Configure Beats to ship logs to elastic-node.

10.5 Wazuh Deployment

# On elastic-node
sudo apt-get install -y wazuh-manager
sudo systemctl enable wazuh-manager && sudo systemctl start wazuh-manager
# On each endpoint
sudo apt-get install -y wazuh-agent
sudo systemctl enable wazuh-agent && sudo systemctl start wazuh-agent

• Configure Wazuh to forward alerts to Elastic SIEM.

10.6 OpenCTI Setup

# On elastic-node
docker run -d --name opencti -p 8080:8080 opencti/platform:latest
# Create admin user via UI

• Import initial threat feeds (MISP, AlienVault).
• Expose the REST/TAXII API.

10.7 Model Training & Serving

1. Data Preparation – Collect 3 months of logs, label a subset.
2. Fine‑Tune Llama‑2 – Use Hugging Face transformers.

   from transformers import AutoModelForCausalLM, AutoTokenizer
   model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
   tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
   # Fine‑tune on log summaries
   

3. Serve with FastAPI – Deploy on gpu-node.

   from fastapi import FastAPI
   app = FastAPI()
   @app.post("/summarize")
   async def summarize(log: str):
       # Tokenize, generate, return summary
   

4. RL Playbook – Train Stable‑Baselines3 PPO policy.

   from stable_baselines3 import PPO
   model = PPO("MlpPolicy", env, verbose=1)
   model.save("playbook_policy.zip")
   

5. Serve RL Policy – Expose via FastAPI.

10.8 Orchestration Layer

• Deploy a Flask app that receives alerts from Elastic SIEM, calls the LLM summarizer, queries OpenCTI, and invokes the RL policy.
• Use Celery for background tasks.
• Store audit logs in PostgreSQL.

10.9 Automation & CI/CD

• GitHub Actions – Build Docker images, run unit tests, and deploy to VMs.
• Ansible – Provision and configure services.
• Prometheus + Grafana – Monitor CPU, GPU, memory, and model latency.

10.10 Security Hardening

• Enable TLS for all services.
• Use HashiCorp Vault for secrets.
• Rotate API keys weekly.
• Run regular vulnerability scans with OpenVAS.

10.11 Maintenance Checklist

10.12 Summary

By following this guide, a small security team can deploy a full AI‑powered security stack that delivers real‑time detection, automated response, and continuous improvement—all while staying within budget constraints.