"""
AI-powered threat detection and analysis node.

This module provides enterprise-grade threat detection capabilities using AI/LLM
for advanced threat analysis, real-time event processing, and automated response.
"""

import asyncio
import json
import logging
from datetime import UTC, datetime, timedelta
from typing import Any, Callable, Dict, List, Optional

from kailash.nodes.base import Node, NodeParameter, register_node
from kailash.nodes.mixins import LoggingMixin, PerformanceMixin, SecurityMixin
from kailash.nodes.security.audit_log import AuditLogNode
from kailash.nodes.security.security_event import SecurityEvent, SecurityEventNode

logger = logging.getLogger(__name__)


@register_node()
class ThreatDetectionNode(SecurityMixin, PerformanceMixin, LoggingMixin, Node):
    """AI-powered threat detection and analysis.

    This node provides comprehensive threat detection capabilities including:
    - Real-time event analysis with <100ms response time
    - AI-powered threat pattern recognition
    - Automated response actions
    - Threat intelligence correlation
    - Integration with security event logging

    Example:
        >>> threat_detector = ThreatDetectionNode(
        ...     detection_rules=["brute_force", "privilege_escalation"],
        ...     ai_model="ollama:llama3.2:3b",
        ...     response_actions=["alert", "block_ip"],
        ...     real_time=True
        ... )
        >>>
        >>> events = [
        ...     {"type": "login", "user": "admin", "ip": "192.168.1.100", "failed": True},
        ...     {"type": "login", "user": "admin", "ip": "192.168.1.100", "failed": True},
        ...     {"type": "login", "user": "admin", "ip": "192.168.1.100", "failed": True}
        ... ]
        >>>
        >>> threats = threat_detector.execute(events=events)
        >>> print(f"Detected {len(threats['threats'])} threats")
    """

    def __init__(
        self,
        name: str = "threat_detection",
        detection_rules: Optional[List[str]] = None,
        response_actions: Optional[List[str]] = None,
        real_time: bool = True,
        severity_threshold: str = "medium",
        response_time_target_ms: int = 100,
        **kwargs,
    ):
        """Initialize threat detection node.

        Args:
            name: Node name
            detection_rules: List of detection rules to apply
            response_actions: Automated response actions
            real_time: Enable real-time threat detection
            severity_threshold: Minimum severity to trigger response
            response_time_target_ms: Target response time in milliseconds
            **kwargs: Additional node parameters
        """
        # Set attributes before calling super().__init__()
        self.detection_rules = detection_rules or [
            "brute_force",
            "privilege_escalation",
            "data_exfiltration",
            "insider_threat",
            "anomalous_behavior",
        ]
        self.response_actions = response_actions or ["alert", "log"]
        self.real_time = real_time
        self.severity_threshold = severity_threshold
        self.response_time_target_ms = response_time_target_ms

        # Initialize parent classes
        super().__init__(name=name, **kwargs)

        # Initialize security event and audit logging
        self.security_event_node = SecurityEventNode(name=f"{name}_security_events")
        self.audit_log_node = AuditLogNode(name=f"{name}_audit_log")

        # Threat detection patterns and rules
        self.threat_patterns = {
            "brute_force": {
                "pattern": "multiple_failed_logins",
                "threshold": 5,
                "time_window": 300,  # 5 minutes
                "severity": "high",
            },
            "privilege_escalation": {
                "pattern": "unauthorized_admin_access",
                "keywords": ["sudo", "admin", "root", "privilege"],
                "severity": "critical",
            },
            "data_exfiltration": {
                "pattern": "large_data_transfer",
                "size_threshold_mb": 100,
                "unusual_hours": True,
                "severity": "critical",
            },
            "insider_threat": {
                "pattern": "abnormal_user_behavior",
                "deviation_threshold": 0.8,
                "severity": "high",
            },
            "anomalous_behavior": {
                "pattern": "statistical_anomaly",
                "confidence_threshold": 0.9,
                "severity": "medium",
            },
        }

        # Response action mappings
        self.response_handlers = {
            "alert": self._send_alert,
            "block_ip": self._block_ip,
            "lock_account": self._lock_account,
            "quarantine": self._quarantine_resource,
            "log": self._log_threat,
        }

        # Performance tracking
        self.detection_stats = {
            "total_events_processed": 0,
            "threats_detected": 0,
            "false_positives": 0,
            "avg_detection_time_ms": 0,
            "last_detection": None,
        }

    def get_parameters(self) -> Dict[str, NodeParameter]:
        """Get node parameters for validation and documentation.

        Returns:
            Dictionary mapping parameter names to NodeParameter objects
        """
        return {
            "events": NodeParameter(
                name="events",
                type=list,
                description="List of security events to analyze for threats",
                required=True,
            ),
            "time_window": NodeParameter(
                name="time_window",
                type=int,
                description="Time window in seconds for threat correlation",
                required=False,
                default=3600,
            ),
            "context": NodeParameter(
                name="context",
                type=dict,
                description="Additional context for threat analysis",
                required=False,
                default={},
            ),
        }

    def run(
        self,
        events: List[Dict[str, Any]],
        time_window: int = 3600,
        context: Optional[Dict[str, Any]] = None,
        **kwargs,
    ) -> Dict[str, Any]:
        """Run threat detection analysis.

        Args:
            events: List of security events to analyze
            time_window: Time window in seconds for threat correlation
            context: Additional context for analysis
            **kwargs: Additional parameters

        Returns:
            Dictionary containing detected threats and analysis results
        """
        start_time = datetime.now(UTC)
        context = context or {}

        try:
            # Validate and sanitize inputs
            safe_params = self.validate_and_sanitize_inputs(
                {"events": events, "time_window": time_window, "context": context}
            )

            events = safe_params["events"]
            time_window = safe_params["time_window"]
            context = safe_params["context"]

            self.log_node_execution("threat_detection_start", event_count=len(events))

            # Run threat detection pipeline
            results = self._analyze_threats(events, time_window, context)

            # Update performance stats
            processing_time = (datetime.now(UTC) - start_time).total_seconds() * 1000
            self._update_stats(len(events), len(results["threats"]), processing_time)

            # Log successful detection
            self.log_node_execution(
                "threat_detection_complete",
                threats_found=len(results["threats"]),
                processing_time_ms=processing_time,
            )

            return results

        except Exception as e:
            self.log_error_with_traceback(e, "threat_detection")
            raise

    def _analyze_threats(
        self, events: List[Dict[str, Any]], time_window: int, context: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Analyze events for threats using rule-based and AI detection.

        Args:
            events: Security events to analyze
            time_window: Time window for correlation
            context: Additional context

        Returns:
            Dictionary with detected threats and analysis
        """
        detected_threats = []
        analysis_results = {
            "rule_based_detections": 0,
            "ai_detections": 0,
            "correlation_matches": 0,
            "response_actions_taken": [],
        }

        # Phase 1: Rule-based detection
        rule_threats = self._detect_rule_based_threats(events, time_window)
        detected_threats.extend(rule_threats)
        analysis_results["rule_based_detections"] = len(rule_threats)

        # Phase 2: Cross-correlation analysis
        correlated_threats = self._correlate_threats(detected_threats, events)
        analysis_results["correlation_matches"] = len(correlated_threats)

        # Phase 4: Response actions for high-severity threats
        for threat in detected_threats:
            if self._should_trigger_response(threat):
                actions_taken = self._execute_response_actions(threat)
                analysis_results["response_actions_taken"].extend(actions_taken)

        return {
            "success": True,
            "threats": detected_threats,
            "analysis": analysis_results,
            "stats": self.detection_stats,
            "timestamp": datetime.now(UTC).isoformat(),
        }

    def _detect_rule_based_threats(
        self, events: List[Dict[str, Any]], time_window: int
    ) -> List[Dict[str, Any]]:
        """Detect threats using predefined rules.

        Args:
            events: Events to analyze
            time_window: Time window in seconds

        Returns:
            List of detected threats
        """
        threats = []

        for rule_name in self.detection_rules:
            if rule_name not in self.threat_patterns:
                continue

            pattern = self.threat_patterns[rule_name]
            rule_threats = self._apply_detection_rule(
                events, rule_name, pattern, time_window
            )
            threats.extend(rule_threats)

        return threats

    def _apply_detection_rule(
        self,
        events: List[Dict[str, Any]],
        rule_name: str,
        pattern: Dict[str, Any],
        time_window: int,
    ) -> List[Dict[str, Any]]:
        """Apply a specific detection rule to events.

        Args:
            events: Events to analyze
            rule_name: Name of the detection rule
            pattern: Rule pattern configuration
            time_window: Time window in seconds

        Returns:
            List of threats detected by this rule
        """
        threats = []

        if rule_name == "brute_force":
            threats.extend(self._detect_brute_force(events, pattern, time_window))
        elif rule_name == "privilege_escalation":
            threats.extend(self._detect_privilege_escalation(events, pattern))
        elif rule_name == "data_exfiltration":
            threats.extend(self._detect_data_exfiltration(events, pattern))
        elif rule_name == "insider_threat":
            threats.extend(self._detect_insider_threat(events, pattern))
        elif rule_name == "anomalous_behavior":
            threats.extend(self._detect_anomalous_behavior(events, pattern))

        return threats

    def _detect_brute_force(
        self, events: List[Dict[str, Any]], pattern: Dict[str, Any], time_window: int
    ) -> List[Dict[str, Any]]:
        """Detect brute force attacks.

        Args:
            events: Events to analyze
            pattern: Brute force pattern configuration
            time_window: Time window in seconds

        Returns:
            List of brute force threats
        """
        threats = []
        login_failures = {}

        current_time = datetime.now(UTC)
        cutoff_time = current_time - timedelta(seconds=time_window)

        # Group failed login attempts by user/IP
        for event in events:
            if (
                event.get("type") == "login"
                and event.get("failed", False)
                and event.get("timestamp")
            ):
                event_time = datetime.fromisoformat(
                    event["timestamp"].replace("Z", "+00:00")
                )
                if event_time > cutoff_time:
                    key = f"{event.get('user', 'unknown')}:{event.get('ip', 'unknown')}"
                    if key not in login_failures:
                        login_failures[key] = []
                    login_failures[key].append(event)

        # Check for brute force patterns
        threshold = pattern.get("threshold", 5)
        for key, failed_attempts in login_failures.items():
            if len(failed_attempts) >= threshold:
                user, ip = key.split(":", 1)
                threats.append(
                    {
                        "id": f"brute_force_{user}_{ip}_{int(current_time.timestamp())}",
                        "type": "brute_force",
                        "severity": pattern["severity"],
                        "user": user,
                        "source_ip": ip,
                        "failed_attempts": len(failed_attempts),
                        "time_window": time_window,
                        "detection_time": current_time.isoformat(),
                        "confidence": min(1.0, len(failed_attempts) / (threshold * 2)),
                        "evidence": failed_attempts[
                            :5
                        ],  # Include first 5 attempts as evidence
                    }
                )

        return threats

    def _detect_privilege_escalation(
        self, events: List[Dict[str, Any]], pattern: Dict[str, Any]
    ) -> List[Dict[str, Any]]:
        """Detect privilege escalation attempts.

        Args:
            events: Events to analyze
            pattern: Privilege escalation pattern configuration

        Returns:
            List of privilege escalation threats
        """
        threats = []
        keywords = pattern.get("keywords", ["sudo", "admin", "root", "privilege"])

        for event in events:
            # Check for privilege escalation indicators
            if event.get("type") in ["command", "access", "authentication"]:
                event_text = json.dumps(event).lower()

                matched_keywords = [kw for kw in keywords if kw in event_text]
                if matched_keywords and event.get("unauthorized", False):
                    threats.append(
                        {
                            "id": f"priv_esc_{event.get('user', 'unknown')}_{int(datetime.now(UTC).timestamp())}",
                            "type": "privilege_escalation",
                            "severity": pattern["severity"],
                            "user": event.get("user", "unknown"),
                            "source_ip": event.get("ip", "unknown"),
                            "matched_keywords": matched_keywords,
                            "detection_time": datetime.now(UTC).isoformat(),
                            "confidence": min(
                                1.0, len(matched_keywords) / len(keywords)
                            ),
                            "evidence": event,
                        }
                    )

        return threats

    def _detect_data_exfiltration(
        self, events: List[Dict[str, Any]], pattern: Dict[str, Any]
    ) -> List[Dict[str, Any]]:
        """Detect data exfiltration attempts.

        Args:
            events: Events to analyze
            pattern: Data exfiltration pattern configuration

        Returns:
            List of data exfiltration threats
        """
        threats = []
        size_threshold = (
            pattern.get("size_threshold_mb", 100) * 1024 * 1024
        )  # Convert to bytes

        for event in events:
            if event.get("type") == "data_transfer":
                size = event.get("size_bytes", 0)
                if size > size_threshold:
                    # Check for unusual hours if configured
                    unusual_time = False
                    if pattern.get("unusual_hours", False):
                        event_time = datetime.fromisoformat(
                            event.get(
                                "timestamp", datetime.now(UTC).isoformat()
                            ).replace("Z", "+00:00")
                        )
                        hour = event_time.hour
                        unusual_time = hour < 6 or hour > 22  # Outside business hours

                    if unusual_time or size > size_threshold * 2:  # Very large transfer
                        threats.append(
                            {
                                "id": f"data_exfil_{event.get('user', 'unknown')}_{int(datetime.now(UTC).timestamp())}",
                                "type": "data_exfiltration",
                                "severity": pattern["severity"],
                                "user": event.get("user", "unknown"),
                                "source_ip": event.get("ip", "unknown"),
                                "transfer_size_mb": size / (1024 * 1024),
                                "unusual_hours": unusual_time,
                                "detection_time": datetime.now(UTC).isoformat(),
                                "confidence": min(1.0, size / (size_threshold * 3)),
                                "evidence": event,
                            }
                        )

        return threats

    def _detect_insider_threat(
        self, events: List[Dict[str, Any]], pattern: Dict[str, Any]
    ) -> List[Dict[str, Any]]:
        """Detect insider threat indicators.

        Args:
            events: Events to analyze
            pattern: Insider threat pattern configuration

        Returns:
            List of insider threat detections
        """
        threats = []

        # Simple heuristic-based insider threat detection
        user_behaviors = {}

        for event in events:
            user = event.get("user", "unknown")
            if user not in user_behaviors:
                user_behaviors[user] = {
                    "access_patterns": [],
                    "data_access": [],
                    "time_patterns": [],
                    "unusual_activities": 0,
                }

            # Track unusual activities
            if event.get("unusual", False) or event.get("anomalous", False):
                user_behaviors[user]["unusual_activities"] += 1

            # Track access patterns
            if event.get("type") == "access":
                user_behaviors[user]["access_patterns"].append(event.get("resource"))

            # Track data access
            if event.get("type") == "data_access":
                user_behaviors[user]["data_access"].append(event.get("data_type"))

        # Analyze for insider threat indicators
        for user, behavior in user_behaviors.items():
            risk_score = 0
            indicators = []

            # High unusual activity count
            if behavior["unusual_activities"] > 5:
                risk_score += 0.4
                indicators.append("high_unusual_activity")

            # Diverse data access (potential data gathering)
            if len(set(behavior["data_access"])) > 10:
                risk_score += 0.3
                indicators.append("diverse_data_access")

            # Unusual resource access patterns
            if len(set(behavior["access_patterns"])) > 20:
                risk_score += 0.3
                indicators.append("broad_resource_access")

            if risk_score > pattern.get("deviation_threshold", 0.8):
                threats.append(
                    {
                        "id": f"insider_threat_{user}_{int(datetime.now(UTC).timestamp())}",
                        "type": "insider_threat",
                        "severity": pattern["severity"],
                        "user": user,
                        "risk_score": risk_score,
                        "indicators": indicators,
                        "detection_time": datetime.now(UTC).isoformat(),
                        "confidence": min(1.0, risk_score),
                        "evidence": {
                            "unusual_activities": behavior["unusual_activities"],
                            "unique_data_types": len(set(behavior["data_access"])),
                            "unique_resources": len(set(behavior["access_patterns"])),
                        },
                    }
                )

        return threats

    def _detect_anomalous_behavior(
        self, events: List[Dict[str, Any]], pattern: Dict[str, Any]
    ) -> List[Dict[str, Any]]:
        """Detect statistical anomalies in behavior.

        Args:
            events: Events to analyze
            pattern: Anomalous behavior pattern configuration

        Returns:
            List of anomalous behavior detections
        """
        threats = []

        # Simple statistical anomaly detection
        event_counts = {}
        time_patterns = {}

        for event in events:
            event_type = event.get("type", "unknown")
            user = event.get("user", "unknown")

            # Count events by type
            if event_type not in event_counts:
                event_counts[event_type] = 0
            event_counts[event_type] += 1

            # Track time patterns
            if event.get("timestamp"):
                try:
                    event_time = datetime.fromisoformat(
                        event["timestamp"].replace("Z", "+00:00")
                    )
                    hour = event_time.hour
                    time_key = f"{user}:{hour}"
                    if time_key not in time_patterns:
                        time_patterns[time_key] = 0
                    time_patterns[time_key] += 1
                except:
                    pass

        # Detect anomalies
        confidence_threshold = pattern.get("confidence_threshold", 0.9)

        # Check for unusual event frequency
        if event_counts:
            avg_count = sum(event_counts.values()) / len(event_counts)
            for event_type, count in event_counts.items():
                if count > avg_count * 3:  # 3x above average
                    confidence = min(1.0, count / (avg_count * 5))
                    if confidence >= confidence_threshold:
                        threats.append(
                            {
                                "id": f"anomaly_{event_type}_{int(datetime.now(UTC).timestamp())}",
                                "type": "anomalous_behavior",
                                "subtype": "unusual_frequency",
                                "severity": pattern["severity"],
                                "event_type": event_type,
                                "frequency": count,
                                "average_frequency": avg_count,
                                "detection_time": datetime.now(UTC).isoformat(),
                                "confidence": confidence,
                                "evidence": {"event_counts": event_counts},
                            }
                        )

        return threats

    def _correlate_threats(
        self, threats: List[Dict[str, Any]], events: List[Dict[str, Any]]
    ) -> List[Dict[str, Any]]:
        """Correlate threats across different detection methods.

        Args:
            threats: Detected threats
            events: Original events

        Returns:
            List of correlated threat patterns
        """
        correlated = []

        # Group threats by user/IP for correlation
        threat_groups = {}

        for threat in threats:
            key_parts = []
            if threat.get("user"):
                key_parts.append(f"user:{threat['user']}")
            if threat.get("source_ip"):
                key_parts.append(f"ip:{threat['source_ip']}")

            if key_parts:
                key = "|".join(key_parts)
                if key not in threat_groups:
                    threat_groups[key] = []
                threat_groups[key].append(threat)

        # Look for correlated patterns
        for key, group_threats in threat_groups.items():
            if len(group_threats) > 1:
                # Multiple threats from same user/IP - potential coordinated attack
                correlated.append(
                    {
                        "id": f"correlated_{key.replace(':', '_').replace('|', '_')}_{int(datetime.now(UTC).timestamp())}",
                        "type": "correlated_attack",
                        "severity": "high",
                        "description": f"Multiple threat types detected from {key}",
                        "related_threats": [t["id"] for t in group_threats],
                        "threat_types": list(set(t["type"] for t in group_threats)),
                        "correlation_score": min(1.0, len(group_threats) / 3),
                        "detection_time": datetime.now(UTC).isoformat(),
                    }
                )

        return correlated

    def _should_trigger_response(self, threat: Dict[str, Any]) -> bool:
        """Determine if threat should trigger automated response.

        Args:
            threat: Threat to evaluate

        Returns:
            True if response should be triggered
        """
        severity = threat.get("severity", "low")
        confidence = threat.get("confidence", 0.0)

        # Response thresholds
        thresholds = {"critical": 0.7, "high": 0.8, "medium": 0.9, "low": 0.95}

        required_confidence = thresholds.get(severity, 0.95)

        # Check if severity meets minimum threshold
        severity_levels = {"low": 1, "medium": 2, "high": 3, "critical": 4}
        min_severity = severity_levels.get(self.severity_threshold, 2)
        threat_severity = severity_levels.get(severity, 1)

        return threat_severity >= min_severity and confidence >= required_confidence

    def _execute_response_actions(self, threat: Dict[str, Any]) -> List[str]:
        """Execute automated response actions for a threat.

        Args:
            threat: Threat that triggered response

        Returns:
            List of actions taken
        """
        actions_taken = []

        for action in self.response_actions:
            try:
                if action in self.response_handlers:
                    self.response_handlers[action](threat)
                    actions_taken.append(action)
                    self.log_with_context(
                        "INFO",
                        f"Executed response action: {action}",
                        threat_id=threat["id"],
                    )
                else:
                    self.log_with_context(
                        "WARNING", f"Unknown response action: {action}"
                    )
            except Exception as e:
                self.log_with_context(
                    "ERROR", f"Failed to execute response action {action}: {e}"
                )

        return actions_taken

    def _send_alert(self, threat: Dict[str, Any]) -> None:
        """Send threat alert.

        Args:
            threat: Threat information
        """
        # Create security event for the alert
        alert_event = {
            "event_type": "threat_alert",
            "severity": threat.get("severity", "medium"),
            "description": f"Threat detected: {threat.get('type', 'unknown')}",
            "metadata": threat,
            "user_id": threat.get("user", "system"),
            "source_ip": threat.get("source_ip", "unknown"),
        }

        self.security_event_node.execute(**alert_event)

    def _block_ip(self, threat: Dict[str, Any]) -> None:
        """Block IP address associated with threat.

        Args:
            threat: Threat information
        """
        ip = threat.get("source_ip")
        if ip and ip != "unknown":
            # Log the IP blocking action
            self.log_with_context(
                "INFO", f"Would block IP: {ip} (threat: {threat['id']})"
            )

            # In a real implementation, this would interface with firewall/network controls
            # For now, just log the action
            block_event = {
                "event_type": "ip_blocked",
                "severity": "high",
                "description": f"IP {ip} blocked due to threat {threat['id']}",
                "metadata": {"blocked_ip": ip, "threat_id": threat["id"]},
                "user_id": "system",
                "source_ip": ip,
            }

            self.security_event_node.execute(**block_event)

    def _lock_account(self, threat: Dict[str, Any]) -> None:
        """Lock user account associated with threat.

        Args:
            threat: Threat information
        """
        user = threat.get("user")
        if user and user != "unknown":
            # Log the account locking action
            self.log_with_context(
                "INFO", f"Would lock account: {user} (threat: {threat['id']})"
            )

            # In a real implementation, this would interface with user management system
            lock_event = {
                "event_type": "account_locked",
                "severity": "high",
                "description": f"Account {user} locked due to threat {threat['id']}",
                "metadata": {"locked_user": user, "threat_id": threat["id"]},
                "user_id": user,
                "source_ip": threat.get("source_ip", "unknown"),
            }

            self.security_event_node.execute(**lock_event)

    def _quarantine_resource(self, threat: Dict[str, Any]) -> None:
        """Quarantine resource associated with threat.

        Args:
            threat: Threat information
        """
        # Log the quarantine action
        self.log_with_context(
            "INFO", f"Would quarantine resource for threat: {threat['id']}"
        )

        quarantine_event = {
            "event_type": "resource_quarantined",
            "severity": "medium",
            "description": f"Resource quarantined due to threat {threat['id']}",
            "metadata": {"threat_id": threat["id"]},
            "user_id": threat.get("user", "system"),
            "source_ip": threat.get("source_ip", "unknown"),
        }

        self.security_event_node.execute(**quarantine_event)

    def _log_threat(self, threat: Dict[str, Any]) -> None:
        """Log threat to audit trail.

        Args:
            threat: Threat information
        """
        # Create audit log entry
        log_entry = {
            "action": "threat_detected",
            "user_id": threat.get("user", "system"),
            "resource_type": "security_event",
            "resource_id": threat["id"],
            "metadata": threat,
            "ip_address": threat.get("source_ip", "unknown"),
        }

        self.audit_log_node.execute(**log_entry)

    def _update_stats(
        self, events_processed: int, threats_detected: int, processing_time_ms: float
    ) -> None:
        """Update detection statistics.

        Args:
            events_processed: Number of events processed
            threats_detected: Number of threats detected
            processing_time_ms: Processing time in milliseconds
        """
        self.detection_stats["total_events_processed"] += events_processed
        self.detection_stats["threats_detected"] += threats_detected
        self.detection_stats["last_detection"] = datetime.now(UTC).isoformat()

        # Update average detection time
        if self.detection_stats["avg_detection_time_ms"] == 0:
            self.detection_stats["avg_detection_time_ms"] = processing_time_ms
        else:
            # Simple moving average
            self.detection_stats["avg_detection_time_ms"] = (
                self.detection_stats["avg_detection_time_ms"] * 0.9
                + processing_time_ms * 0.1
            )

    def analyze_patterns(
        self, time_window: timedelta = timedelta(hours=24)
    ) -> Dict[str, Any]:
        """Analyze historical patterns for threat intelligence.

        Args:
            time_window: Time window for pattern analysis

        Returns:
            Dictionary with pattern analysis results
        """
        return {
            "analysis_type": "historical_patterns",
            "time_window": str(time_window),
            "pattern_summary": "Historical pattern analysis would be implemented here",
            "stats": self.detection_stats,
            "recommendations": [
                "Implement pattern baseline learning",
                "Add threat feed integration",
                "Enable machine learning models",
            ],
        }

    def auto_respond(self, threat: SecurityEvent) -> List[str]:
        """Execute automated response actions for a threat.

        Args:
            threat: Security event representing the threat

        Returns:
            List of response actions taken
        """
        threat_dict = {
            "id": getattr(threat, "correlation_id", None)
            or f"event_{int(datetime.now(UTC).timestamp())}",
            "type": threat.event_type,
            "severity": threat.severity,
            "user": threat.user_id,
            "source_ip": threat.ip_address,
        }

        return self._execute_response_actions(threat_dict)

    def get_detection_stats(self) -> Dict[str, Any]:
        """Get current detection statistics.

        Returns:
            Dictionary with detection statistics
        """
        return {
            **self.detection_stats,
            "rules_enabled": self.detection_rules,
            "response_actions": self.response_actions,
            "real_time_enabled": self.real_time,
            "performance_target_ms": self.response_time_target_ms,
        }

    async def async_run(self, **kwargs) -> Dict[str, Any]:
        """Async execution method for enterprise integration."""
        return self.execute(**kwargs)
