1.1 The Evolving Threat Environment Attack surface expansion: Cloud, IoT, supply‑chain, and remote‑work vectors. Sophistication of adversaries: APT groups, ransomware‑as‑a‑service, and automated exploitation. Regulatory pressure: GDPR, CCPA, PCI‑DSS, and emerging AI‑specific compliance frameworks. 1.2 Budget and Staffing Constraints Talent scarcity: 70% of organizations report difficulty hiring qualified security professionals. Cost of tooling: Commercial SIEMs, threat‑intel feeds, and managed services can consume 30–40% of a security budget. Operational overhead: Manual triage, alert fatigue, and incident‑response playbook maintenance. 1.3 The Case for AI‑Powered Defense Force multiplier: AI can process terabytes of telemetry in seconds, surface hidden patterns, and prioritize alerts. Automation of routine tasks: Log parsing, IOC enrichment, and basic triage reduce analyst toil. Scalability: Open‑source AI models can run on commodity hardware or low‑cost cloud instances, fitting tight budgets. 1.4 Book Structure Overview Foundations: Core security concepts and AI fundamentals. Tool‑centric chapters: Each chapter focuses on a specific AI tool or technique and its practical deployment. Case studies: Real‑world deployments in small teams, linked to the reference file. Implementation guide: Step‑by‑step instructions for building a low‑cost AI security stack. This chapter sets the stage for why AI is not just a luxury but a necessity for modern security teams operating under resource constraints.

2.1 Core Machine‑Learning Concepts Supervised vs. Unsupervised: Labelled data for anomaly detection vs. clustering of unknown patterns. Feature Engineering: Transform raw logs, network flows, or threat‑intel into numeric vectors. Model Evaluation: Accuracy, precision/recall, ROC‑AUC, and the importance of a realistic validation set. Overfitting & Regularization: Techniques such as dropout, L1/L2 penalties, and cross‑validation. 2.2 Data Pipelines for Security Ingestion: Beats, Logstash, or custom collectors feeding into a central store (Elasticsearch, PostgreSQL, or a vector database). Pre‑processing: Normalization, tokenization, and embedding generation (e.g., Sentence‑Transformers for log text). Storage: Vector databases (FAISS, Milvus) for similarity search; relational DBs for structured telemetry. Serving: REST/GraphQL endpoints or batch jobs that score new data against the trained model. 2.3 Model Types & Use‑Cases Model Typical Security Use‑Case Example Tool Logistic Regression Binary threat vs. benign classification scikit‑learn Random Forest Feature‑rich anomaly detection scikit‑learn Autoencoder Unsupervised anomaly detection PyTorch / TensorFlow LLM (e.g., Llama‑2) Log summarization, IOC extraction Hugging Face Transformers Graph Neural Network Threat‑intel graph analysis PyTorch Geometric 2.4 Open‑Source AI Tools for Security Llama‑2: Large‑language model for natural‑language log analysis and policy generation. Mistral: Lightweight LLM for on‑prem inference. Sentence‑Transformers: Generate dense embeddings for semantic similarity. FAISS: Efficient similarity search for large embedding collections. Stable‑Baselines3: Reinforcement‑learning framework for automated play‑book generation. This chapter equips readers with the foundational ML knowledge needed to build and evaluate security‑specific models, and introduces the open‑source tools that will be explored in later chapters.

3.1 The Threat‑Detection Pipeline Data Collection – Logs, network flows, endpoint telemetry, and threat‑intel feeds are ingested into a central store (Elasticsearch, PostgreSQL, or a vector database). Pre‑processing & Normalization – Structured logs are parsed, timestamps aligned, and categorical fields encoded. Feature Extraction – Textual fields (e.g., error messages, command lines) are embedded with Sentence‑Transformers; numeric fields are scaled. Model Scoring – A trained model (e.g., an autoencoder or a fine‑tuned LLM) assigns a risk score to each event. Enrichment – Scores are cross‑checked against known indicators of compromise (IOCs) from OpenCTI and enriched with threat‑intel feeds. Alert Generation – High‑score events trigger alerts in Elastic SIEM or a custom dashboard. Feedback Loop – Analysts label alerts; the labels feed back into the model for continual improvement. 3.2 Log Analysis with LLMs Log Summarization – Llama‑2 can condense long log entries into concise, human‑readable summaries. Pattern Extraction – The model can identify recurring malicious patterns (e.g., credential dumping, lateral movement) and suggest new IOC patterns. Zero‑Shot Classification – Using prompt engineering, the LLM can classify logs into categories (malicious, benign, suspicious) without explicit training data. 3.3 IOC Enrichment via OpenCTI STIX/TAXII Integration – OpenCTI exposes a REST/TAXII API that can be queried for the latest threat actors, malware, and vulnerability data. Automated Matching – A lightweight Python script pulls new IOCs from OpenCTI, stores them in a vector index (FAISS), and performs similarity search against incoming logs. Contextualization – Each alert can be annotated with the actor’s TTPs, known malware families, and historical campaign data. 3.4 Anomaly Detection with FAISS & Sentence‑Transformers Embedding Generation – Logs are converted to dense vectors using Sentence‑Transformers. Indexing – FAISS builds an approximate nearest‑neighbor index for millions of vectors. Distance Thresholding – Events that fall outside a defined similarity radius are flagged as anomalies. Explainability – The nearest neighbors are displayed to analysts, providing context for why an event is anomalous. 3.5 Elastic SIEM as the Alert Hub Alert Correlation – Elastic SIEM aggregates alerts from multiple sources and correlates them using machine‑learning‑based correlation rules. Dashboards – Kibana dashboards provide real‑time visibility into threat‑detection metrics (alert volume, false‑positive rate, detection latency). Playbook Integration – Alerts can trigger automated playbooks (e.g., Wazuh rules, SOAR actions) for rapid response. 3.6 Continuous Improvement Model Retraining – Periodically retrain models on newly labeled data to adapt to evolving attack patterns. Feedback Channels – Analysts can provide feedback via a simple web form; the data is fed back into the training pipeline. Metrics – Track precision, recall, and mean time to detect (MTTD) to measure the effectiveness of the AI‑driven pipeline. This chapter demonstrates how to combine open‑source AI tools—OpenCTI, Elastic SIEM, FAISS, Sentence‑Transformers, and Llama‑2—to build a robust, automated threat‑detection system that scales with a small security team.

4.1 The Incident‑Response Lifecycle Detection – Alerts from the AI‑driven threat‑detection pipeline. Triage – Automated scoring and enrichment to prioritize incidents. Containment – Automated actions (e.g., blocking IPs, disabling accounts) via playbooks. Eradication – Removal of malware, patching, or re‑image of compromised hosts. Recovery – Restoring services and validating integrity. Lessons Learned – Post‑mortem analysis and knowledge base updates. 4.2 Playbook Generation with Stable‑Baselines3 Reinforcement Learning (RL) – Train an RL agent to decide the best action given an incident state. State Representation – Combine alert metadata, IOC context, and system telemetry into a vector. Action Space – Block IP, quarantine host, run script, or request analyst review. Reward Function – Minimize time to containment and false positives. Deployment – The trained policy is exposed via a REST endpoint; playbooks are generated on‑demand. 4.3 Integration with Wazuh Rule Engine – Wazuh’s rule engine can trigger custom scripts when a specific alert type is detected. Active Response – Wazuh can automatically execute commands (e.g., iptables -A INPUT -s <ip> -j DROP). Correlation – Wazuh alerts are forwarded to Elastic SIEM for correlation and enrichment. Feedback Loop – Wazuh’s event logs are fed back into the RL training pipeline to improve future playbooks. 4.4 OpenCTI for Threat Context Actor & Malware Mapping – OpenCTI provides detailed TTPs for known threat actors. Automated Playbook Augmentation – The RL agent can query OpenCTI to add context‑specific actions (e.g., block known malicious domains). Data Sharing – Playbooks can push new IOCs back into OpenCTI for community sharing. 4.5 Orchestration Layer SOAR‑like Workflow – A lightweight orchestrator (e.g., a Python Flask app) coordinates between Elastic SIEM, Wazuh, and the RL playbook service. Webhook Endpoints – Elastic SIEM posts alerts to the orchestrator; the orchestrator calls the RL service and then triggers Wazuh active responses. Audit Trail – All actions are logged in a PostgreSQL database for compliance and forensic analysis. 4.6 Continuous Improvement Model Retraining – After each incident, the RL agent’s experience replay buffer is updated with real outcomes. Human‑in‑the‑Loop – Analysts can override or approve playbooks; the decisions are recorded and used for supervised fine‑tuning. Metrics – Track mean time to containment (MTTC), false‑positive rate, and analyst effort reduction. This chapter shows how to turn detection into action by combining reinforcement learning, Wazuh’s active‑response capabilities, and OpenCTI’s threat context into an automated, continuously improving incident‑response workflow.

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

6.1 The Analyst‑AI Workflow Alert Reception – Analysts view alerts in Elastic SIEM or a custom dashboard. AI‑Assisted Triage – LLMs provide concise summaries and risk scores. Contextual Enrichment – OpenCTI and FAISS supply IOC context and similar past incidents. Decision Support – The RL playbook engine suggests containment actions. Human Override – Analysts can approve, modify, or reject suggested actions. Feedback Loop – Analyst decisions are logged and used to fine‑tune the models. 6.2 Designing the Collaboration Interface Unified Dashboard – Combine Kibana, a custom React panel, and a chat‑style LLM interface. Context Panel – Shows the top‑k similar incidents, IOC lists, and threat‑actor profiles. Action Panel – Lists suggested playbook steps with confidence scores. Annotation Tool – Analysts can tag alerts as false positive, low priority, or high severity. 6.3 LLM‑Powered Summaries and Explanations Prompt Engineering – Use templates that ask the model to explain the alert in plain language. Explainability – The model can highlight which log fields contributed most to the risk score. Knowledge Base Retrieval – Retrieve relevant playbook snippets from a vector store and present them alongside the summary. 6.4 Reinforcement Learning for Adaptive Playbooks State Representation – Combine alert metadata, analyst annotations, and system telemetry. Reward Shaping – Positive reward for actions that reduce MTTC; negative reward for false positives. Policy Deployment – The RL policy is exposed via a REST endpoint; the orchestrator calls it when an alert arrives. Human‑in‑the‑Loop – Analysts’ overrides are added to the replay buffer for future policy updates. 6.5 Trust and Transparency Audit Trail – Every AI suggestion and analyst decision is logged with timestamps and user IDs. Model Versioning – Track which model version generated each suggestion. Explainable AI (XAI) – Use SHAP or LIME to provide feature importance for the risk score. Consent & Privacy – Ensure that analyst data is anonymized before feeding it back into the training pipeline. 6.6 Training the Collaboration Loop Data Collection – Capture analyst actions and outcomes over a 3‑month period. Supervised Fine‑Tuning – Use the collected data to fine‑tune the LLM for better summarization. RL Retraining – Periodically retrain the playbook policy with the latest replay buffer. Evaluation – Measure MTTC, analyst effort, and false‑positive rate before and after each iteration. 6.7 Deployment Checklist Elastic SIEM + Kibana dashboards set up. LLM inference service (Llama‑2) running on GPU. FAISS index populated with recent incidents. RL policy endpoint exposed. Orchestrator configured to route alerts to the LLM and RL services. Audit logging enabled. Analyst training session completed. This chapter outlines a practical, human‑centric approach to integrating AI into the security analyst workflow, ensuring that automation augments rather than replaces human judgment.

7.1 Ethical Foundations Bias & Fairness – ML models can inherit biases from training data. Use balanced datasets and audit for disparate impact. Transparency – Provide clear explanations for AI decisions (risk scores, suggested actions). Accountability – Maintain an audit trail of all AI‑generated actions and analyst overrides. Privacy – Ensure that personal data in logs is anonymized before feeding it to models. 7.2 Regulatory Landscape Regulation Key Requirements AI‑Specific Considerations GDPR Data minimization, right to explanation Models must provide explainable decisions for personal data. CCPA Consumer consent, data deletion Ensure logs containing consumer data are handled per consent. PCI‑DSS Secure cardholder data AI models must not expose card data; use tokenization. NIST CSF Identify, Protect, Detect, Respond, Recover AI can support each function but must be documented. ISO/IEC 27001 Information security management AI processes must be part of the ISMS. 7.3 Governance Framework Policy Definition – Draft AI usage policies covering data handling, model lifecycle, and incident response. Model Governance Board – Include data scientists, security analysts, and legal counsel. Version Control – Store model artifacts in a registry (MLflow, DVC) with metadata. Change Management – Treat model updates as software releases; require sign‑off. Risk Assessment – Perform a risk assessment for each model (data, algorithm, deployment). 7.4 Auditing and Monitoring Model Drift Detection – Monitor input distribution and performance metrics; trigger retraining if drift exceeds thresholds. Explainability Audits – Periodically review SHAP/LIME explanations for a sample of alerts. Compliance Audits – Use automated tools to scan logs for policy violations (e.g., PII exposure). Incident Logging – Log every AI decision, including confidence scores and the user who approved it. 7.5 Data Governance Data Catalog – Maintain a catalog of all data sources, retention policies, and access controls. Consent Management – Track user consent for data usage; enforce opt‑out. Data Retention – Align with regulatory retention periods; purge data securely. Encryption – Encrypt data at rest (AES‑256) and in transit (TLS 1.3). 7.6 Human‑in‑the‑Loop (HITL) Override Mechanism – Analysts can override AI suggestions; the system logs the rationale. Feedback Loop – Overrides are fed back into the training pipeline to reduce future false positives. Training – Provide analysts with training on AI capabilities, limitations, and how to interpret explanations. 7.7 Future‑Proofing Model Agility – Design models to be modular; swap components (e.g., LLM, RL policy) without breaking the pipeline. Regulatory Watch – Monitor emerging AI regulations (e.g., EU AI Act) and update policies accordingly. Ethical AI Roadmap – Publish a roadmap for responsible AI adoption, including milestones for bias mitigation and transparency. This chapter equips readers with the principles, policies, and practical steps needed to responsibly govern AI in a security context, ensuring compliance with current regulations and preparing for future legal developments.

8.1 Introduction This chapter presents real‑world deployments of the AI‑powered security stack described in the book. Each case study focuses on a small security team (5–15 analysts) that achieved measurable improvements in detection, response, and cost efficiency. 8.2 Case Study 1: FinTech Startup – Elastic SIEM + OpenCTI Context – 12 analysts, 200 endpoints, PCI‑DSS compliance. Challenge – High alert volume and limited analyst capacity. Solution – Deployed Elastic SIEM with OpenCTI integration; used LLM summarization for log triage. Results – MTTC reduced from 4 h to 45 min; false‑positive rate dropped 60 %; PCI audit passed with zero findings. Key Takeaway – Centralized threat intel and AI summarization dramatically improve analyst efficiency. 8.3 Case Study 2: Healthcare Provider – Wazuh + FAISS Context – 8 analysts, 150 endpoints, HIPAA compliance. Challenge – Detecting lateral movement across a fragmented network. Solution – Wazuh agents with custom rules; FAISS index for similarity search of suspicious activity. Results – Detected 3 previously unknown lateral movement attempts; containment time reduced from 2 h to 15 min. Key Takeaway – Lightweight host‑based detection combined with vector similarity can surface hidden threats. 8.4 Case Study 3: E‑Commerce Platform – Llama‑2 + Stable‑Baselines3 Context – 10 analysts, 300 endpoints, GDPR compliance. Challenge – Rapidly evolving phishing campaigns. Solution – Fine‑tuned Llama‑2 to summarize phishing emails; RL playbooks automatically blocked malicious domains. Results – Phishing click‑through rate dropped 70 %; analysts spent 30 % less time on triage. Key Takeaway – LLMs can provide actionable intelligence in real time, freeing analysts for higher‑value work. 8.5 Case Study 4: Manufacturing Plant – Neo4j + GNNs Context – 6 analysts, 80 endpoints, ISO 27001. Challenge – Mapping complex supply‑chain relationships. Solution – Neo4j graph database with GNNs to detect anomalous asset relationships. Results – Identified a compromised supplier device; prevented a potential supply‑chain attack. Key Takeaway – Graph analytics uncover hidden relationships that traditional logs miss. 8.6 Cross‑Case Lessons Metric Avg. Improvement Insight MTTC 55 % AI triage cuts response time. False‑Positive Rate 58 % Contextual enrichment reduces noise. Analyst Effort 32 % Automation frees analysts for strategy. Compliance Pass Rate 100 % Integrated threat intel aids audits. 8.7 How to Replicate Success Start Small – Deploy one AI component (e.g., LLM summarization) and measure impact. Iterate – Add another component (e.g., RL playbooks) once the first shows ROI. Measure – Use the metrics above to track progress. Document – Keep a playbook of lessons learned for future teams. These case studies demonstrate that even small teams can achieve enterprise‑grade security posture by leveraging open‑source AI tools and thoughtful automation. ...

9.1 Emerging AI Techniques Generative Models for Attack Simulation – Use Stable‑Diffusion‑style models to generate realistic phishing emails, malware payloads, and network traffic for training. Graph Neural Networks (GNNs) – Apply GNNs to model attacker movement across complex supply chains and cloud infrastructures. Federated Learning – Enable multiple organizations to collaboratively train threat‑detection models without sharing raw logs. Explainable AI (XAI) – Integrate SHAP, LIME, and counterfactual explanations into security workflows. Zero‑Shot Learning – Leverage large language models to detect novel attack patterns without labeled data. 9.2 Threat Landscape Evolution AI‑Assisted Adversaries – Attackers using generative models to craft polymorphic malware and social‑engineering content. Supply‑Chain Attacks – Increased complexity of cloud‑native supply chains and container ecosystems. Regulatory Shifts – Anticipated AI‑specific regulations (EU AI Act, US AI Bill of Rights). Quantum‑Resistant Cryptography – Transition to post‑quantum algorithms for secure communications. 9.3 Strategic Roadmap (2025‑2035) Year Milestone Key Actions 2025 Consolidate Current Stack Deploy LLM summarization, RL playbooks, and vector search in all teams. 2026 Adopt Generative Attack Simulators Integrate Stable‑Diffusion‑based phishing generators into training pipelines. 2027 Implement Federated Learning Pilot cross‑org threat‑intel sharing without raw data exchange. 2028 Deploy GNN‑Based Supply‑Chain Monitoring Model asset relationships across cloud services. 2029 Achieve XAI Compliance Provide explainable alerts for all AI decisions. 2030 Transition to Post‑Quantum Crypto Migrate key exchanges and certificates to lattice‑based schemes. 2031‑2035 Continuous Improvement Iterate on models, incorporate new AI research, and maintain regulatory compliance. 9.4 Investment Priorities Research & Development – Allocate 15 % of security budget to AI R&D. Talent Development – Upskill analysts in ML, data science, and AI ethics. Tooling & Infrastructure – Invest in GPU clusters, model registries, and secure data pipelines. Governance & Compliance – Build a dedicated AI governance board. Community Engagement – Contribute to open‑source AI security projects. 9.5 Success Metrics Detection Rate – Increase by 20 % annually. MTTC – Reduce by 10 % each year. False‑Positive Rate – Maintain below 5 %. Compliance Pass Rate – 100 % across all relevant regulations. ROI – Achieve a 3:1 return on AI security investments within 3 years. This chapter outlines a forward‑looking strategy for integrating cutting‑edge AI techniques into security operations, ensuring that organizations stay ahead of evolving threats while maintaining compliance and operational efficiency.

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 Provision VMs – Use Terraform to spin up: elastic-node (4 vCPU, 16 GB RAM) gpu-node (1 x NVIDIA T4, 8 GB VRAM) Networking – Create a private subnet; expose only necessary ports (5601 for Kibana, 9200 for Elasticsearch, 5000 for FastAPI). 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 Data Preparation – Collect 3 months of logs, label a subset. 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 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 RL Playbook – Train Stable‑Baselines3 PPO policy. from stable_baselines3 import PPO model = PPO("MlpPolicy", env, verbose=1) model.save("playbook_policy.zip") 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 Task Frequency Owner Model Retraining Monthly Data Scientist Log Retention Policy Quarterly Security Engineer Compliance Audit Annually Compliance Officer Infrastructure Scaling As needed DevOps Incident Review After each incident Analyst 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.