Add behavior analytics EDA notebook

behavior_analytics_eda.ipynb

@@ -0,0 +1,62 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.11.0"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Security Behavior Analytics \u2014 Cohort & Peer EDA\n",
+        "\n",
+        "**Aria AI Security Data Science Team**\n",
+        "\n",
+        "Exploratory analysis on synthetic enterprise IAM/access behavior events.\n",
+        "\n",
+        "> Artifacts are pre-computed \u2014 no external downloads or model training required.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "import json\n",
+        "from pathlib import Path\n",
+        "\n",
+        "ROOT = Path('.')\n",
+        "summary = json.loads((ROOT / 'assets/demo/summary.json').read_text())\n",
+        "cohorts = json.loads((ROOT / 'assets/demo/cohort_analysis.json').read_text())\n",
+        "risk = json.loads((ROOT / 'assets/demo/risk_analysis.json').read_text())\n",
+        "print('Events:', summary['total_events'])\n",
+        "print('Users:', summary['total_users'])\n",
+        "print('Critical risk:', risk['summary']['critical'])\n"
+      ],
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "import plotly.graph_objects as go\n",
+        "\n",
+        "dept = cohorts['dimensions']['department']['cohorts'][:10]\n",
+        "fig = go.Figure(go.Bar(x=[c['cohort'] for c in dept], y=[c['event_count'] for c in dept]))\n",
+        "fig.update_layout(title='Events by Department Cohort', template='plotly_dark')\n",
+        "fig.show()\n"
+      ],
+      "outputs": [],
+      "execution_count": null
+    }
+  ]
+}