大模型在安全运营与威胁检测中的应用

安全威胁演变历程
简单攻击 → 复杂攻击 → 高级持续威胁(APT) → 混合威胁 → AI驱动的攻击

大模型与传统安全工具的融合
┌─────────────────────────┐     ┌─────────────────────────┐     ┌─────────────────────────┐
│ 大模型                  │────▶│ 融合层                  │────▶│ 传统安全工具           │
│ 自然语言理解            │     │ 数据预处理与转换        │     │ SIEM                    │
│ 知识推理                │     │ 智能分析与决策          │     │ EDR/XDR                 │
│ 自动生成                │     │ 报告生成与推荐          │     │ 漏洞扫描器              │
└─────────────────────────┘     └─────────────────────────┘     └─────────────────────────┘
                                            ▲                                  │
                                            │                                  │
                                            │                                  ▼
┌─────────────────────────┐     ┌─────────────────────────┐     ┌─────────────────────────┐
│ 安全知识库              │◀────│ 反馈与学习系统          │◀────│ 执行结果与反馈          │
└─────────────────────────┘     └─────────────────────────┘     └─────────────────────────┘

# 智能安全运营系统架构示例代码
class AISecurityOperationsSystem:
    def __init__(self):
        self.data_collector = SecurityDataCollector()
        self.data_processor = SecurityDataProcessor()
        self.llm = LargeLanguageModel()
        self.threat_detector = ThreatDetector()
        self.incident_responder = IncidentResponder()
        self.visualization = Visualization()
        self.knowledge_manager = SecurityKnowledgeManager()
        self.feedback_learning = FeedbackLearning()
        
        # 初始化组件间的连接
        self._init_connections()
    
    def _init_connections(self):
        # 建立组件之间的连接关系
        self.data_collector.set_data_processor(self.data_processor)
        self.data_processor.set_threat_detector(self.threat_detector)
        self.data_processor.set_incident_responder(self.incident_responder)
        self.data_processor.set_knowledge_manager(self.knowledge_manager)
        self.llm.set_threat_detector(self.threat_detector)
        self.llm.set_incident_responder(self.incident_responder)
        self.llm.set_knowledge_manager(self.knowledge_manager)
        self.threat_detector.set_visualization(self.visualization)
        self.incident_responder.set_visualization(self.visualization)
        self.threat_detector.set_feedback_learning(self.feedback_learning)
        self.incident_responder.set_feedback_learning(self.feedback_learning)
        self.visualization.set_feedback_learning(self.feedback_learning)
        self.feedback_learning.set_llm(self.llm)
        self.feedback_learning.set_knowledge_manager(self.knowledge_manager)
    
    def detect_and_respond(self, security_request):
        # 执行安全检测与响应的主流程
        try:
            # 1. 收集安全数据
            raw_data = self.data_collector.collect_data(security_request)
            
            # 2. 处理安全数据
            processed_data = self.data_processor.process_data(raw_data)
            
            # 3. 智能威胁检测
            detection_result = self.threat_detector.detect_threats(processed_data, self.llm)
            
            # 4. 智能事件响应
            response_result = self.incident_responder.respond_to_incidents(detection_result, self.llm)
            
            # 5. 生成可视化结果
            visualizations = self.visualization.generate_visualizations(detection_result, response_result)
            
            # 6. 整合结果
            final_result = {
                "detection_result": detection_result,
                "response_result": response_result,
                "visualizations": visualizations
            }
            
            return final_result
        except Exception as e:
            # 异常处理
            error_info = f"检测与响应过程中发生错误: {str(e)}"
            return {"error": error_info}
    
    def train_model(self, training_data):
        # 训练和优化大模型
        return self.feedback_learning.train_model(training_data)
    
    def update_knowledge(self, new_knowledge):
        # 更新知识库
        return self.knowledge_manager.update_knowledge(new_knowledge)

# 系统组件类
class SecurityDataCollector:
    # 安全数据采集组件
    pass

class SecurityDataProcessor:
    # 安全数据处理组件
    pass

class LargeLanguageModel:
    # 大模型组件
    pass

class ThreatDetector:
    # 威胁检测组件
    pass

class IncidentResponder:
    # 事件响应组件
    pass

class Visualization:
    # 可视化组件
    pass

class SecurityKnowledgeManager:
    # 安全知识管理组件
    pass

class FeedbackLearning:
    # 反馈学习组件
    pass

# 创建并使用系统
system = AISecurityOperationsSystem()
security_request = {
    "organization": "example_company",
    "data_sources": ["siem", "edr", "firewall", "ids", "vulnerability_scanner"],
    "time_range": {"start": "2023-01-01T00:00:00", "end": "2023-01-01T23:59:59"},
    "request_type": "comprehensive_analysis"
}
result = system.detect_and_respond(security_request)
print(result)

智能安全运营系统数据流
┌─────────────────────────┐     ┌─────────────────────────┐     ┌─────────────────────────┐
│ 数据采集                │────▶│ 数据处理与存储          │────▶│ 智能威胁检测与响应      │
└─────────────────────────┘     └─────────────────────────┘     └─────────────────────────┘
                                                                        │
                                                                        ▼
┌─────────────────────────┐     ┌─────────────────────────┐     ┌─────────────────────────┐
│ 反馈收集                │◀────│ 用户交互与结果展示      │◀────│ 结果生成与推荐          │
└─────────────────────────┘     └─────────────────────────┘     └─────────────────────────┘
        │                                                                   │
        ▼                                                                   ▼
┌─────────────────────────┐     ┌─────────────────────────┐     ┌─────────────────────────┐
│ 模型训练与优化          │────▶│ 知识更新与维护          │────▶│ 持续优化循环            │
└─────────────────────────┘     └─────────────────────────┘     └─────────────────────────┘

# 智能告警分析与降噪示例代码
import json
import pandas as pd
from transformers import pipeline

# 初始化大模型
alert_analyzer = pipeline("text-generation", model="gpt2")

# 加载告警数据
def load_alert_data(alert_file=None):
    # 在实际应用中，这里应该从SIEM或其他安全系统加载告警数据
    # 这里我们使用模拟的告警数据
    alerts = [
        {
            "alert_id": "ALERT-001",
            "timestamp": "2023-01-01T10:00:00",
            "source": "SIEM",
            "severity": "high",
            "alert_type": "Potential Ransomware Activity",
            "description": "Multiple file encryption activities detected on host server-01",
            "source_ip": "192.168.1.100",
            "dest_ip": "10.0.0.5",
            "host_name": "server-01",
            "user": "admin",
            "raw_log": "2023-01-01T10:00:00, server-01, admin, Multiple files with .encrypted extension created",
            "status": "new"
        },
        {
            "alert_id": "ALERT-002",
            "timestamp": "2023-01-01T10:05:00",
            "source": "IDS",
            "severity": "medium",
            "alert_type": "Port Scan",
            "description": "Port scan detected from external IP",
            "source_ip": "203.0.113.10",
            "dest_ip": "192.168.1.0/24",
            "host_name": "N/A",
            "user": "N/A",
            "raw_log": "2023-01-01T10:05:00, IDS, External IP 203.0.113.10 scanning ports 22, 23, 80, 443 on network 192.168.1.0/24",
            "status": "new"
        },
        {
            "alert_id": "ALERT-003",
            "timestamp": "2023-01-01T10:10:00",
            "source": "Firewall",
            "severity": "low",
            "alert_type": "Blocked Connection",
            "description": "Outbound connection to known malicious IP blocked",
            "source_ip": "192.168.1.10",
            "dest_ip": "198.51.100.10",
            "host_name": "workstation-01",
            "user": "user1",
            "raw_log": "2023-01-01T10:10:00, Firewall, Blocked outbound connection from 192.168.1.10 to 198.51.100.10 (known malicious IP)",
            "status": "new"
        },
        {
            "alert_id": "ALERT-004",
            "timestamp": "2023-01-01T10:15:00",
            "source": "EDR",
            "severity": "high",
            "alert_type": "Suspicious Process",
            "description": "Suspicious process running on endpoint",
            "source_ip": "192.168.1.50",
            "dest_ip": "N/A",
            "host_name": "workstation-05",
            "user": "user5",
            "raw_log": "2023-01-01T10:15:00, EDR, Suspicious process 'cmd.exe' spawning multiple child processes on workstation-05",
            "status": "new"
        },
        {
            "alert_id": "ALERT-005",
            "timestamp": "2023-01-01T10:20:00",
            "source": "SIEM",
            "severity": "medium",
            "alert_type": "Failed Login Attempts",
            "description": "Multiple failed login attempts detected",
            "source_ip": "10.0.0.100",
            "dest_ip": "192.168.1.200",
            "host_name": "server-05",
            "user": "root",
            "raw_log": "2023-01-01T10:20:00, server-05, Multiple failed login attempts for user 'root' from IP 10.0.0.100",
            "status": "new"
        }
    ]
    
    return alerts

# 智能告警分析与降噪函数
def analyze_and_triage_alerts(alerts):
    analyzed_alerts = []
    
    for alert in alerts:
        # 构建告警分析提示
        prompt = f"""
        作为一名资深安全分析师，你需要分析以下安全告警：
        
        告警信息：
        - 告警ID: {alert['alert_id']}
        - 时间戳: {alert['timestamp']}
        - 来源: {alert['source']}
        - 严重性: {alert['severity']}
        - 告警类型: {alert['alert_type']}
        - 描述: {alert['description']}
        - 源IP: {alert['source_ip']}
        - 目标IP: {alert['dest_ip']}
        - 主机名: {alert['host_name']}
        - 用户: {alert['user']}
        - 原始日志: {alert['raw_log']}
        
        请按照以下步骤进行分析：
        1. 判断告警的真实性（是否为误报）
        2. 评估告警的实际风险等级（低、中、高、严重）
        3. 分析告警可能的原因和影响
        4. 提供初步的调查建议
        5. 建议下一步的响应措施
        6. 确定告警的处理优先级（立即处理、尽快处理、常规处理、低优先级）
        
        请以JSON格式返回分析结果：
        """
        
        # 使用大模型进行告警分析
        try:
            analysis_result = alert_analyzer(prompt, max_length=2000, temperature=0.7)[0]["generated_text"]
            
            # 解析分析结果
            try:
                # 简单的JSON提取（实际应用中可能需要更复杂的解析）
                json_start = analysis_result.find("{")
                json_end = analysis_result.rfind("}") + 1
                alert_analysis = json.loads(analysis_result[json_start:json_end])
            except:
                # 如果无法解析为JSON，返回原始文本
                alert_analysis = {"raw_analysis": analysis_result}
            
            analyzed_alert = alert.copy()
            analyzed_alert["analysis"] = alert_analysis
            
            analyzed_alerts.append(analyzed_alert)
        except Exception as e:
            print(f"告警分析时出错 (Alert ID: {alert['alert_id']}): {str(e)}")
            # 添加默认分析结果
            alert["analysis"] = {
                "error": f"分析失败: {str(e)}",
                "recommendation": "需要人工分析"
            }
            analyzed_alerts.append(alert)
    
    return analyzed_alerts

# 告警聚合与关联分析函数
def aggregate_and_correlate_alerts(analyzed_alerts):
    correlation_prompt = f"""
    作为一名资深安全分析师，你需要对以下分析过的安全告警进行聚合和关联分析：
    
    分析过的告警：
    {json.dumps(analyzed_alerts, indent=2)}
    
    请按照以下步骤进行聚合和关联分析：
    1. 根据告警的特征（时间、源IP、目标IP、主机名、用户等）进行聚合
    2. 分析告警之间的关联性，识别可能的攻击链或攻击活动
    3. 评估整体的安全威胁态势
    4. 提供综合的处理建议和优先级
    5. 识别需要重点关注的告警和资产
    
    请以JSON格式返回聚合和关联分析结果：
    """
    
    # 使用大模型进行告警聚合和关联分析
    try:
        correlation_result = alert_analyzer(correlation_prompt, max_length=3000, temperature=0.7)[0]["generated_text"]
        
        # 解析关联分析结果
        try:
            # 简单的JSON提取（实际应用中可能需要更复杂的解析）
            json_start = correlation_result.find("{")
            json_end = correlation_result.rfind("}") + 1
            correlation_analysis = json.loads(correlation_result[json_start:json_end])
        except:
            # 如果无法解析为JSON，返回原始文本
            correlation_analysis = {"raw_correlation_analysis": correlation_result}
        
        return correlation_analysis
    except Exception as e:
        print(f"告警聚合与关联分析时出错: {str(e)}")
        return {
            "error": f"聚合与关联分析失败: {str(e)}",
            "recommendation": "需要人工进行聚合和关联分析"
        }

# 加载告警数据
alerts = load_alert_data()

# 智能告警分析与降噪
analyzed_alerts = analyze_and_triage_alerts(alerts)

print("\n智能告警分析与降噪结果:")
for alert in analyzed_alerts:
    print(f"\n告警ID: {alert['alert_id']}")
    print(f"告警类型: {alert['alert_type']}")
    print(f"原始严重性: {alert['severity']}")
    print(f"分析结果: {json.dumps(alert['analysis'], ensure_ascii=False, indent=2)}")

# 告警聚合与关联分析
correlation_result = aggregate_and_correlate_alerts(analyzed_alerts)

print("\n\n告警聚合与关联分析结果:")
print(json.dumps(correlation_result, ensure_ascii=False, indent=2))

# 智能威胁检测与异常行为分析示例代码
import json
import pandas as pd
from transformers import pipeline

# 初始化大模型
threat_detector = pipeline("text-generation", model="gpt2")

# 加载安全数据
def load_security_data(data_file=None):
    # 在实际应用中，这里应该从各种安全数据源加载数据
    # 这里我们使用模拟的安全数据
    security_data = [
        {
            "timestamp": "2023-01-01T09:00:00",
            "source": "SIEM",
            "event_type": "login",
            "source_ip": "192.168.1.100",
            "dest_ip": "10.0.0.5",
            "host_name": "server-01",
            "user": "admin",
            "status": "success",
            "details": "User 'admin' logged in from IP 192.168.1.100"
        },
        {
            "timestamp": "2023-01-01T09:05:00",
            "source": "SIEM",
            "event_type": "file_access",
            "source_ip": "192.168.1.100",
            "dest_ip": "10.0.0.5",
            "host_name": "server-01",
            "user": "admin",
            "status": "success",
            "details": "User 'admin' accessed file 'C:\\financial\\report.xlsx'"
        },
        {
            "timestamp": "2023-01-01T09:10:00",
            "source": "SIEM",
            "event_type": "file_modification",
            "source_ip": "192.168.1.100",
            "dest_ip": "10.0.0.5",
            "host_name": "server-01",
            "user": "admin",
            "status": "success",
            "details": "User 'admin' modified file 'C:\\financial\\report.xlsx'"
        },
        {
            "timestamp": "2023-01-01T09:15:00",
            "source": "SIEM",
            "event_type": "file_copy",
            "source_ip": "192.168.1.100",
            "dest_ip": "10.0.0.5",
            "host_name": "server-01",
            "user": "admin",
            "status": "success",
            "details": "User 'admin' copied file 'C:\\financial\\report.xlsx' to '\\\\share\\temp\\'"
        },
        {
            "timestamp": "2023-01-01T09:20:00",
            "source": "SIEM",
            "event_type": "network_connection",
            "source_ip": "10.0.0.5",
            "dest_ip": "203.0.113.20",
            "host_name": "server-01",
            "user": "admin",
            "status": "success",
            "details": "Outbound connection from server-01 (10.0.0.5) to 203.0.113.20 on port 443"
        },
        {
            "timestamp": "2023-01-01T09:25:00",
            "source": "SIEM",
            "event_type": "file_transfer",
            "source_ip": "10.0.0.5",
            "dest_ip": "203.0.113.20",
            "host_name": "server-01",
            "user": "admin",
            "status": "success",
            "details": "File 'report.xlsx' transferred from server-01 to 203.0.113.20"
        }
    ]
    
    return security_data

# 加载资产和用户行为基线
def load_baselines(baseline_file=None):
    # 在实际应用中，这里应该加载预先建立的资产和用户行为基线
    # 这里我们使用模拟的基线数据
    baselines = {
        "users": {
            "admin": {
                "typical_login_time": "09:00-18:00",
                "typical_login_locations": ["192.168.1.0/24", "10.0.0.0/8"],
                "typical_file_access": ["C:\\admin\\", "C:\\shared\\"],
                "typical_network_connections": ["10.0.0.0/8", "192.168.0.0/16"],
                "typical_data_transfer_volume": "< 100MB/day"
            },
            "user1": {
                "typical_login_time": "08:30-17:30",
                "typical_login_locations": ["192.168.1.0/24"],
                "typical_file_access": ["C:\\users\\user1\\", "C:\\shared\\projects\\"],
                "typical_network_connections": ["10.0.0.0/8", "192.168.0.0/16"],
                "typical_data_transfer_volume": "< 50MB/day"
            }
        },
        "assets": {
            "server-01": {
                "typical_operating_hours": "24/7",
                "typical_processes": ["explorer.exe", "svchost.exe", "winlogon.exe"],
                "typical_network_connections": ["192.168.1.0/24", "10.0.0.0/8"],
                "typical_resource_usage": {"cpu": "< 50%", "memory": "< 60%", "disk": "< 70%"},
                "typical_data_transfer_volume": "< 500MB/day"
            },
            "workstation-01": {
                "typical_operating_hours": "08:00-18:00",
                "typical_processes": ["explorer.exe", "chrome.exe", "outlook.exe"],
                "typical_network_connections": ["192.168.1.0/24", "10.0.0.0/8", "internet"],
                "typical_resource_usage": {"cpu": "< 30%", "memory": "< 40%", "disk": "< 80%"},
                "typical_data_transfer_volume": "< 200MB/day"
            }
        }
    }
    
    return baselines

# 智能威胁检测与异常行为分析函数
def detect_threats_and_anomalies(security_data, baselines):
    detection_results = []
    
    # 构建检测提示
    prompt = f"""
    作为一名资深安全分析师，你需要分析以下安全数据，检测潜在的威胁和异常行为：
    
    安全数据：
    {json.dumps(security_data, indent=2)}
    
    行为基线：
    {json.dumps(baselines, indent=2)}
    
    请按照以下步骤进行分析：
    1. 识别数据中的异常行为（与基线不符的行为）
    2. 分析异常行为可能的原因
    3. 评估异常行为的风险等级
    4. 识别可能的威胁活动或攻击模式
    5. 提供详细的调查建议
    6. 建议下一步的响应措施
    
    请以JSON格式返回检测结果：
    """
    
    # 使用大模型进行威胁检测与异常行为分析
    try:
        detection_response = threat_detector(prompt, max_length=3000, temperature=0.7)[0]["generated_text"]
        
        # 解析检测结果
        try:
            # 简单的JSON提取（实际应用中可能需要更复杂的解析）
            json_start = detection_response.find("{")
            json_end = detection_response.rfind("}") + 1
            detection_result = json.loads(detection_response[json_start:json_end])
        except:
            # 如果无法解析为JSON，返回原始文本
            detection_result = {"raw_detection_result": detection_response}
        
        # 将检测结果与原始数据关联
        detection_results.append({
            "analysis_time": "2023-01-01T09:30:00",
            "analyzed_data_count": len(security_data),
            "detection_result": detection_result
        })
    except Exception as e:
        print(f"威胁检测与异常行为分析时出错: {str(e)}")
        detection_results.append({
            "analysis_time": "2023-01-01T09:30:00",
            "analyzed_data_count": len(security_data),
            "error": f"检测失败: {str(e)}",
            "recommendation": "需要人工进行威胁检测与异常行为分析"
        })
    
    return detection_results

# 生成威胁情报报告
def generate_threat_intelligence_report(detection_results, threat_intelligence=None):
    # 在实际应用中，这里应该结合外部威胁情报进行分析
    # 这里我们使用模拟的威胁情报
    if threat_intelligence is None:
        threat_intelligence = {
            "malicious_ips": ["203.0.113.10", "203.0.113.20", "198.51.100.10"],
            "malicious_domains": ["malware-example.com", "phishing-example.org"],
            "known_attack_patterns": [
                {"name": "Data Exfiltration", "description": "Unauthorized transfer of data outside the organization"},
                {"name": "Lateral Movement", "description": "Movement from one system to another within the network"}
            ]
        }
    
    report_prompt = f"""
    作为一名安全情报分析师，你需要根据以下威胁检测结果和威胁情报，生成一份威胁情报报告：
    
    威胁检测结果：
    {json.dumps(detection_results, indent=2)}
    
    威胁情报：
    {json.dumps(threat_intelligence, indent=2)}
    
    威胁情报报告应包含以下内容：
    1. 检测到的威胁概述
    2. 详细的威胁分析（包括类型、影响范围、严重程度等）
    3. 威胁关联分析（与已知威胁情报的关联）
    4. 可能的攻击来源和动机分析
    5. 建议的缓解措施和防护策略
    6. 未来的监控重点
    
    请生成一份格式规范、内容详细的威胁情报报告：
    """
    
    # 使用大模型生成威胁情报报告
    try:
        threat_report = threat_detector(report_prompt, max_length=4000, temperature=0.7)[0]["generated_text"]
        return threat_report
    except Exception as e:
        print(f"生成威胁情报报告时出错: {str(e)}")
        return f"生成威胁情报报告失败: {str(e)}"

# 加载安全数据
security_data = load_security_data()

# 加载行为基线
baselines = load_baselines()

# 智能威胁检测与异常行为分析
detection_results = detect_threats_and_anomalies(security_data, baselines)

print("\n威胁检测与异常行为分析结果:")
for result in detection_results:
    print(f"\n分析时间: {result['analysis_time']}")
    print(f"分析数据量: {result['analyzed_data_count']}")
    print(f"检测结果: {json.dumps(result['detection_result'], ensure_ascii=False, indent=2)}")

# 生成威胁情报报告
threat_report = generate_threat_intelligence_report(detection_results)

print("\n\n威胁情报报告:")
print(threat_report)

# 智能事件响应与处置建议生成示例代码
import json
from transformers import pipeline

# 初始化大模型
incident_responder = pipeline("text-generation", model="gpt2")

# 加载安全事件数据
def load_incident_data(incident_file=None):
    # 在实际应用中，这里应该从安全事件管理系统加载事件数据
    # 这里我们使用模拟的安全事件数据
    incidents = [
        {
            "incident_id": "INC-001",
            "incident_name": "Potential Data Exfiltration",
            "discovery_time": "2023-01-01T09:30:00",
            "status": "open",
            "severity": "high",
            "description": "Unauthorized file transfer detected from server-01 to external IP address",
            "affected_assets": ["server-01"],
            "detailed_findings": [
                "User 'admin' logged in at 09:00:00",
                "Multiple financial files accessed and modified",
                "Files copied to shared folder",
                "Outbound connection to 203.0.113.20 established",
                "File 'report.xlsx' transferred to external IP"
            ],
            "related_alerts": ["ALERT-001", "ALERT-004"],
            "assigned_analyst": "analyst1",
            "incident_type": "Data Exfiltration"
        },
        {
            "incident_id": "INC-002",
            "incident_name": "Suspicious Process Execution",
            "discovery_time": "2023-01-01T10:00:00",
            "status": "open",
            "severity": "medium",
            "description": "Suspicious process running on workstation-05",
            "affected_assets": ["workstation-05"],
            "detailed_findings": [
                "Process 'cmd.exe' spawned multiple child processes",
                "Network connections to unknown domains detected",
                "High CPU usage observed"
            ],
            "related_alerts": ["ALERT-004"],
            "assigned_analyst": "analyst2",
            "incident_type": "Malware Infection"
        }
    ]
    
    return incidents

# 加载响应计划和流程
def load_response_playbooks(playbook_file=None):
    # 在实际应用中，这里应该加载预先定义的响应计划和流程
    # 这里我们使用模拟的响应计划
    playbooks = {
        "data_exfiltration": {
            "name": "Data Exfiltration Response Playbook",
            "description": "Guidelines for responding to data exfiltration incidents",
            "steps": [
                {"id": 1, "name": "Containment", "description": "Isolate affected systems and block external connections"},
                {"id": 2, "name": "Eradication", "description": "Remove any malicious software or access points"},
                {"id": 3, "name": "Recovery", "description": "Restore systems to normal operation"},
                {"id": 4, "name": "Post-incident Analysis", "description": "Conduct thorough analysis to identify root cause and lessons learned"}
            ],
            "key_resources": ["Network Team", "Security Team", "Legal Team"],
            "metrics": ["Time to Containment", "Time to Eradication", "Data Loss Assessment"]
        },
        "malware_infection": {
            "name": "Malware Infection Response Playbook",
            "description": "Guidelines for responding to malware infection incidents",
            "steps": [
                {"id": 1, "name": "Isolation", "description": "Isolate infected systems from the network"},
                {"id": 2, "name": "Identification", "description": "Identify the type and scope of malware"},
                {"id": 3, "name": "Removal", "description": "Remove malware from infected systems"},
                {"id": 4, "name": "System Restoration", "description": "Restore systems to clean state"},
                {"id": 5, "name": "Vulnerability Remediation", "description": "Remediate vulnerabilities exploited by malware"}
            ],
            "key_resources": ["Endpoint Security Team", "IT Support Team"],
            "metrics": ["Infection Rate", "Time to Removal", "System Downtime"]
        }
    }
    
    return playbooks

# 智能事件响应与处置建议生成函数
def generate_incident_response_suggestions(incidents, playbooks):
    response_suggestions = []
    
    for incident in incidents:
        # 确定适用的响应计划
        incident_type = incident.get("incident_type", "general").lower().replace(" ", "_").replace("-", "_")
        applicable_playbook = playbooks.get(incident_type, playbooks.get("general", None))
        
        # 构建响应建议提示
        prompt = f"""
        作为一名资深安全事件响应专家，你需要根据以下安全事件信息和响应计划，生成详细的处置建议：
        
        事件信息：
        {json.dumps(incident, indent=2)}
        
        适用的响应计划：
        {json.dumps(applicable_playbook, indent=2) if applicable_playbook else "No specific playbook available"}
        
        请按照以下步骤生成处置建议：
        1. 分析事件的严重性、影响范围和潜在后果
        2. 评估当前的事件处理状态和进展
        3. 根据响应计划，提供详细的处置步骤建议
        4. 针对每个步骤，提供具体的实施指南和注意事项
        5. 确定所需的资源和人员
        6. 评估每个处置步骤的预期效果和潜在风险
        7. 提供时间线和优先级建议
        8. 建议后续的监控和预防措施
        
        请以JSON格式返回处置建议：
        """
        
        # 使用大模型生成处置建议
        try:
            response = incident_responder(prompt, max_length=3000, temperature=0.7)[0]["generated_text"]
            
            # 解析处置建议
            try:
                # 简单的JSON提取（实际应用中可能需要更复杂的解析）
                json_start = response.find("{")
                json_end = response.rfind("}") + 1
                suggestion = json.loads(response[json_start:json_end])
            except:
                # 如果无法解析为JSON，返回原始文本
                suggestion = {"raw_suggestion": response}
            
            # 将处置建议与原始事件关联
            incident_with_suggestion = incident.copy()
            incident_with_suggestion["response_suggestion"] = suggestion
            
            response_suggestions.append(incident_with_suggestion)
        except Exception as e:
            print(f"生成处置建议时出错 (Incident ID: {incident['incident_id']}): {str(e)}")
            # 添加默认处置建议
            incident["response_suggestion"] = {
                "error": f"生成处置建议失败: {str(e)}",
                "recommendation": "需要人工制定处置计划"
            }
            response_suggestions.append(incident)
    
    return response_suggestions

# 生成事件总结报告
def generate_incident_summary_report(incidents_with_suggestions):
    report_prompt = f"""
    作为一名安全经理，你需要根据以下安全事件及其处置建议，生成一份事件总结报告：
    
    事件及其处置建议：
    {json.dumps(incidents_with_suggestions, indent=2)}
    
    事件总结报告应包含以下内容：
    1. 事件概述（数量、类型、严重性分布等）
    2. 每个事件的简要描述和处置建议摘要
    3. 整体的安全态势评估
    4. 资源需求评估
    5. 建议的优先处置顺序
    6. 潜在的组织影响和业务风险
    7. 长期的安全改进建议
    
    请生成一份格式规范、内容详细的事件总结报告：
    """
    
    # 使用大模型生成事件总结报告
    try:
        summary_report = incident_responder(report_prompt, max_length=4000, temperature=0.7)[0]["generated_text"]
        return summary_report
    except Exception as e:
        print(f"生成事件总结报告时出错: {str(e)}")
        return f"生成事件总结报告失败: {str(e)}"

# 加载安全事件数据
incidents = load_incident_data()

# 加载响应计划和流程
playbooks = load_response_playbooks()

# 智能事件响应与处置建议生成
incidents_with_suggestions = generate_incident_response_suggestions(incidents, playbooks)

print("\n事件响应与处置建议:")
for incident in incidents_with_suggestions:
    print(f"\n事件ID: {incident['incident_id']}")
    print(f"事件名称: {incident['incident_name']}")
    print(f"严重性: {incident['severity']}")
    print(f"处置建议: {json.dumps(incident['response_suggestion'], ensure_ascii=False, indent=2)}")

# 生成事件总结报告
summary_report = generate_incident_summary_report(incidents_with_suggestions)

print("\n\n事件总结报告:")
print(summary_report)

实施步骤与方法论
需求分析与规划 → 数据准备与整合 → 技术选型与架构设计 → 原型开发与验证 → 试点与推广 → 运营与优化
  ↓                                                                              ↑
  └────────────────────────────────────────────────────────────────────────────┘

智能安全运营与威胁检测未来发展趋势
大模型深度融合 → 实时性增强 → 预测性安全 → 自动化闭环 → 多模态交互
  ↓                                                                   ↑
边缘计算安全 ← 安全知识自动化 ← 自适应安全 ← 量子安全防护 ← 安全合规融合

参考资料关系图
┌─────────────────────────┐     ┌─────────────────────────┐     ┌─────────────────────────┐
│ 安全运营研究报告        │────▶│ 大模型技术文档          │────▶│ 安全工具厂商资源        │
│ Gartner研究             │     │ OpenAI技术报告          │     │ Splunk白皮书            │
│ Forrester研究           │     │ Hugging Face文档        │     │ IBM最佳实践            │
│ SANS调查研究            │     │ 云厂商AI安全文档        │     │ CrowdStrike威胁报告    │
└─────────────────────────┘     └─────────────────────────┘     └─────────────────────────┘
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                                    ┌───────────────┐
                                    │ 本文          │
                                    └───────────────┘