MariaIsCoding/Week-8-LLM-chains-pipeline-3-Flowise-chains-n8n-integration

# Week 8: Prompt Engineering & LLM Security Analysis Pipeline ## Overview This lab focused on prompt engineering, LLM chain development, and workflow automation using Flowise, Groq, and n8n. The objective was to design a modular AI-assisted cybersecurity analysis pipeline capable of classifying security alerts, analyzing threat indicators, and recommending incident response actions through chained LLM calls. The final system simulates a lightweight SOC (Security Operations Center) workflow by transforming raw alert data into structured threat intelligence and actionable response recommendations. ## Objectives - Build three independent Flowise LLM chains for cybersecurity analysis - Apply prompt engineering principles for structured AI output - Connect Flowise chatflows to n8n using HTTP API requests - Automate a multi-stage threat analysis workflow - Produce machine-readable JSON outputs for downstream automation ## Architecture The pipeline consists of three sequential AI components: **1. Alert Classifier** - Classifies raw security alerts by severity - Output format: { "severity": "HIGH", "confidence": 0.8, "reasoning": "..." } **2. Threat Analyzer** - Interprets classified alerts - Maps indicators to attack behavior and MITRE ATT&CK techniques - Output format: { "attack_type": "C2", "indicators": [], "potential_impact": "...", "related_mitre_techniques": [], "confidence_assessment": "HIGH" } **3. Response Recommender** - Generates actionable incident response recommendations - Output format: { "immediate_actions": [], "investigation_steps": [], "containment_strategy": "...", "escalation_needed": true } ## Tech Stack - **Flowise** - **Groq API** - **Llama 3.3 70B Versatile** - **n8n** - **Prompt Engineering** - **HTTP API Integration** - **MITRE ATT&CK Framework** ## Prompt Engineering Concepts Applied This lab emphasized five core prompt engineering patterns: - Role assignment - Structured JSON output formatting - Constraint enforcement - Domain-specific instruction design - Multi-stage reasoning through chained workflows Each model was instructed to return strict JSON outputs to ensure compatibility with workflow automation. ## Workflow Pipeline The n8n automation pipeline executes the following sequence: Manual Trigger ↓ Set Security Alert Input ↓ HTTP Request → Alert Classifier ↓ HTTP Request → Threat Analyzer ↓ HTTP Request → Response Recommender This enables automated progression from raw alert ingestion to incident response planning. ## Example Test Scenario ### Input Alert Outbound DNS queries to known C2 domain detected from workstation-47 ### Classification Output { "severity": "HIGH", "confidence": 0.8, "reasoning": "Outbound DNS queries to a known C2 domain indicate likely malicious activity requiring immediate investigation." } ### Threat Analysis Output - Attack Type: Command & Control (C2) - MITRE ATT&CK Techniques: - T1041 - T1064 - Potential impact: Data exfiltration, lateral movement, persistent compromise ### Recommended Response - Isolate affected workstation - Review DNS/system logs - Run malware scans - Rotate credentials - Escalate to incident response team ## Improvements / Future Enhancements Several improvements could strengthen this pipeline for real-world deployment: - **Prompt refinement for classification consistency** The Alert Classifier occasionally showed inconsistency in severity assignment for borderline alerts. Prompt constraints and additional few-shot examples could improve classification reliability. - **Structured output validation** Adding JSON schema validation between workflow steps would ensure malformed model outputs do not break downstream automation. - **Confidence threshold logic** Low-confidence classifications could trigger manual analyst review instead of automatically progressing through the pipeline. - **Expanded threat intelligence context** Integrating a retrieval-based knowledge source such as MITRE ATT&CK documentation or internal threat intelligence could improve analysis accuracy. - **Persistent logging and audit tracking** Storing alerts, classifications, and recommendations in a database would improve traceability and support incident review. - **Human-in-the-loop escalation workflows** Critical alerts could trigger analyst approval steps rather than relying entirely on automated recommendations. ## Key Takeaways This project demonstrated how LLMs can be orchestrated as modular security analysis components rather than standalone chatbots. By combining prompt engineering, API-based LLM deployment, and workflow automation, the system transforms free-text alerts into structured threat intelligence and operational response guidance.