Chapter 5 – Building a Low‑Cost AI Security Stack

5.1 Architecture Overview

+-------------------+          +-------------------+
|  Data Sources    |  --->   |  Ingestion Layer  |
+-------------------+          +-------------------+
          |                               |
          v                               v
+-------------------+          +-------------------+
|  Feature Store   |  --->   |  Model Serving    |
+-------------------+          +-------------------+
          |                               |
          v                               v
+-------------------+          +-------------------+
|  Alert Hub (SIEM)|  <---   |  Orchestration    |
+-------------------+          +-------------------+
  • Ingestion: Beats, syslog, and custom collectors feed into a central store.
  • Feature Store: A PostgreSQL database holds structured telemetry; FAISS indexes store embeddings.
  • Model Serving: FastAPI endpoints expose LLMs (Llama‑2, Mistral) and RL policies.
  • Alert Hub: Elastic SIEM aggregates alerts and provides dashboards.
  • Orchestration: A lightweight Flask app coordinates playbooks and active responses.

5.2 Hardware & Cost Considerations

Component Minimum Specs Approx. Cost (USD)
VM for Elastic Stack 4 vCPU, 16 GB RAM $30/month (AWS t3.medium)
GPU for LLM inference 1 x NVIDIA T4 $0.35/hr (AWS g4dn.xlarge)
Storage 500 GB SSD $10/month
Backup 1 TB $5/month
Network 1 Gbps $0
  • Total: Roughly $50/month for a small team.
  • Scaling: Add nodes or upgrade GPU as data volume grows.

5.3 Open‑Source Tool Stack

Tool Purpose Key Features
Elastic Stack Log ingestion, search, and SIEM Beats, Logstash, Kibana, SIEM app
Wazuh Host‑based intrusion detection Agent, rule engine, active response
OpenCTI Threat‑intel management STIX/TAXII API, connectors
Llama‑2 Log summarization, IOC extraction Hugging Face Transformers, fine‑tune
Mistral Lightweight LLM for on‑prem inference 7B parameters, fast inference
Sentence‑Transformers Dense embeddings for similarity Pre‑trained models, GPU support
FAISS Approximate nearest‑neighbor search Scales to billions of vectors
Stable‑Baselines3 RL for playbook generation PPO, DQN, SAC implementations
Neo4j + GNNs Graph analytics on threat intel Cypher queries, PyTorch Geometric
OpenVAS Vulnerability scanning Open‑source scanner, CVE database

5.4 Deployment Steps

  1. Provision Infrastructure – Use Terraform or CloudFormation to spin up VMs, GPU instances, and storage.
  2. Install Elastic Stack – Follow the official Elastic quickstart; enable the SIEM app.
  3. Deploy Wazuh – Install the manager and agents; configure rules for common threats.
  4. Set Up OpenCTI – Run the Docker Compose stack; import initial threat feeds.
  5. Model Training & Serving
    • Fine‑tune Llama‑2 on internal logs.
    • Train an RL policy with Stable‑Baselines3.
    • Expose models via FastAPI.
  6. Feature Store & FAISS Index – Load embeddings into FAISS; store metadata in PostgreSQL.
  7. Orchestrator – Deploy the Flask app; configure webhooks in Elastic SIEM.
  8. Testing – Simulate alerts; verify that playbooks trigger and Wazuh responds.
  9. Monitoring & Logging – Use Prometheus + Grafana to monitor resource usage.
  10. Documentation – Write a run‑book for onboarding new analysts.

5.5 Maintenance & Scaling

  • Model Retraining – Schedule nightly retraining jobs; use Airflow for orchestration.
  • Data Retention – Configure ILM policies in Elastic to tier older data to cheaper storage.
  • Security Hardening – Enable TLS for all services; rotate secrets via Vault.
  • Cost Optimization – Spot instances for GPU inference; use auto‑scaling groups.

This chapter provides a practical, low‑cost blueprint for building a full AI‑powered security stack that a small team can deploy, maintain, and scale.