Chapter 6 – Human‑AI Collaboration Models

6.1 The Analyst‑AI Workflow

  1. Alert Reception – Analysts view alerts in Elastic SIEM or a custom dashboard.
  2. AI‑Assisted Triage – LLMs provide concise summaries and risk scores.
  3. Contextual Enrichment – OpenCTI and FAISS supply IOC context and similar past incidents.
  4. Decision Support – The RL playbook engine suggests containment actions.
  5. Human Override – Analysts can approve, modify, or reject suggested actions.
  6. 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

  1. Data Collection – Capture analyst actions and outcomes over a 3‑month period.
  2. Supervised Fine‑Tuning – Use the collected data to fine‑tune the LLM for better summarization.
  3. RL Retraining – Periodically retrain the playbook policy with the latest replay buffer.
  4. 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.