Implement Phase 4: ML threat detection, automated playbooks, and advanced reporting

Co-authored-by: mblanke <9078342+mblanke@users.noreply.github.com>

backend/app/core/playbook_engine.py

@@ -0,0 +1,165 @@
+"""
+Playbook Execution Engine
+
+Executes automated response playbooks based on triggers.
+"""
+
+from typing import Dict, Any, List
+from datetime import datetime, timezone
+import asyncio
+
+
+class PlaybookEngine:
+    """Engine for executing playbooks"""
+    
+    def __init__(self):
+        """Initialize playbook engine"""
+        self.actions_registry = {
+            "send_notification": self._action_send_notification,
+            "create_case": self._action_create_case,
+            "isolate_host": self._action_isolate_host,
+            "collect_artifact": self._action_collect_artifact,
+            "block_ip": self._action_block_ip,
+            "send_email": self._action_send_email,
+        }
+    
+    async def execute_playbook(
+        self,
+        playbook: Dict[str, Any],
+        context: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """
+        Execute a playbook
+        
+        Args:
+            playbook: Playbook definition
+            context: Execution context with relevant data
+        
+        Returns:
+            Execution result
+        """
+        results = []
+        errors = []
+        
+        actions = playbook.get("actions", [])
+        
+        for action in actions:
+            action_type = action.get("type")
+            action_params = action.get("params", {})
+            
+            try:
+                # Get action handler
+                handler = self.actions_registry.get(action_type)
+                if not handler:
+                    errors.append(f"Unknown action type: {action_type}")
+                    continue
+                
+                # Execute action
+                result = await handler(action_params, context)
+                results.append({
+                    "action": action_type,
+                    "status": "success",
+                    "result": result
+                })
+            except Exception as e:
+                errors.append(f"Error in action {action_type}: {str(e)}")
+                results.append({
+                    "action": action_type,
+                    "status": "failed",
+                    "error": str(e)
+                })
+        
+        return {
+            "status": "completed" if not errors else "completed_with_errors",
+            "results": results,
+            "errors": errors
+        }
+    
+    async def _action_send_notification(
+        self,
+        params: Dict[str, Any],
+        context: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Send a notification"""
+        # In production, this would create a notification in the database
+        # and push it via WebSocket
+        return {
+            "notification_sent": True,
+            "message": params.get("message", "Playbook notification")
+        }
+    
+    async def _action_create_case(
+        self,
+        params: Dict[str, Any],
+        context: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Create a new case"""
+        # In production, this would create a case in the database
+        return {
+            "case_created": True,
+            "case_title": params.get("title", "Automated Case"),
+            "case_id": "placeholder_id"
+        }
+    
+    async def _action_isolate_host(
+        self,
+        params: Dict[str, Any],
+        context: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Isolate a host"""
+        # In production, this would call Velociraptor or other tools
+        # to isolate the host from the network
+        host_id = params.get("host_id")
+        return {
+            "host_isolated": True,
+            "host_id": host_id
+        }
+    
+    async def _action_collect_artifact(
+        self,
+        params: Dict[str, Any],
+        context: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Collect an artifact from a host"""
+        # In production, this would trigger Velociraptor collection
+        return {
+            "collection_started": True,
+            "artifact": params.get("artifact_name"),
+            "client_id": params.get("client_id")
+        }
+    
+    async def _action_block_ip(
+        self,
+        params: Dict[str, Any],
+        context: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Block an IP address"""
+        # In production, this would update firewall rules
+        ip_address = params.get("ip_address")
+        return {
+            "ip_blocked": True,
+            "ip_address": ip_address
+        }
+    
+    async def _action_send_email(
+        self,
+        params: Dict[str, Any],
+        context: Dict[str, Any]
+    ) -> Dict[str, Any]:
+        """Send an email"""
+        # In production, this would send an actual email
+        return {
+            "email_sent": True,
+            "to": params.get("to"),
+            "subject": params.get("subject")
+        }
+
+
+def get_playbook_engine() -> PlaybookEngine:
+    """
+    Factory function to create playbook engine
+    
+    Returns:
+        Configured PlaybookEngine instance
+    """
+    return PlaybookEngine()