Claude Code企业安全权限管理详解：构建AI时代的全方位数据保护体系

# 企业级智能数据分类保护系统
class EnterpriseDataClassificationSystem:
    """企业级智能数据分类和保护系统"""
    
    def __init__(self):
        self.ml_classifier = MLDataClassifier()
        self.policy_engine = DataProtectionPolicyEngine()
        self.encryption_service = EnterpriseCryptographyService()
        self.compliance_monitor = ComplianceMonitor()
        self.audit_logger = SecurityAuditLogger()
    
    async def comprehensive_data_discovery_and_protection(self, data_sources: List[str]) -> Dict:
        """全面的数据发现和保护流程"""
        
        print("🔍 启动企业级数据发现和保护流程...")
        
        protection_results = {
            "discovery_summary": {},
            "classification_results": {},
            "protection_status": {},
            "compliance_validation": {},
            "risk_assessment": {}
        }
        
        # 1. 全面数据发现
        discovery_results = await self.comprehensive_data_discovery(data_sources)
        protection_results["discovery_summary"] = discovery_results
        
        # 2. 智能数据分类
        classification_results = await self.intelligent_data_classification(discovery_results)
        protection_results["classification_results"] = classification_results
        
        # 3. 敏感信息检测
        sensitivity_analysis = await self.advanced_sensitivity_detection(discovery_results)
        
        # 4. 数据保护策略应用
        protection_status = await self.apply_data_protection_policies(
            classification_results, sensitivity_analysis
        )
        protection_results["protection_status"] = protection_status
        
        # 5. 合规性验证
        compliance_validation = await self.validate_regulatory_compliance(
            classification_results, protection_status
        )
        protection_results["compliance_validation"] = compliance_validation
        
        # 6. 风险评估和建议
        risk_assessment = await self.comprehensive_risk_assessment(protection_results)
        protection_results["risk_assessment"] = risk_assessment
        
        await self.audit_logger.log_security_event(
            event_type="data_classification_protection_completed",
            details=protection_results,
            severity="info"
        )
        
        return protection_results
    
    async def intelligent_data_classification(self, discovery_results: Dict) -> Dict:
        """智能数据分类"""
        
        classification_results = {
            "by_sensitivity_level": {},
            "by_data_type": {},
            "by_business_context": {},
            "by_regulatory_requirements": {},
            "classification_confidence": {}
        }
        
        for data_source, discovered_items in discovery_results.items():
            print(f"📊 分类数据源: {data_source}")
            
            for item in discovered_items["items"]:
                # 使用机器学习进行内容分类
                ml_classification = await self.ml_classifier.classify_content(
                    content=item["content_sample"],
                    metadata=item["metadata"]
                )
                
                # 基于规则的分类增强
                rule_based_classification = await self.rule_based_classification(item)
                
                # 业务上下文分析
                business_context = await self.analyze_business_context(item)
                
                # 合规要求分析
                regulatory_requirements = await self.determine_regulatory_requirements(
                    ml_classification, rule_based_classification
                )
                
                # 综合分类结果
                final_classification = await self.synthesize_classification_results(
                    ml_classification, rule_based_classification, business_context, regulatory_requirements
                )
                
                # 按不同维度归类
                sensitivity = final_classification["sensitivity_level"]
                classification_results["by_sensitivity_level"].setdefault(sensitivity, []).append({
                    "item_path": item["path"],
                    "classification": final_classification,
                    "confidence_score": final_classification["confidence"]
                })
                
                data_type = final_classification["primary_data_type"]
                classification_results["by_data_type"].setdefault(data_type, []).append({
                    "item_path": item["path"],
                    "classification": final_classification
                })
                
        return classification_results
    
    async def advanced_sensitivity_detection(self, discovery_results: Dict) -> Dict:
        """高级敏感信息检测"""
        
        # 增强的敏感信息检测模式
        enhanced_sensitivity_patterns = {
            # 个人身份信息 (PII) - 增强版
            "enhanced_pii_patterns": {
                "us_social_security": r"\b(?!000|666|9\d{2})\d{3}[-\s]?(?!00)\d{2}[-\s]?(?!0000)\d{4}\b",
                "credit_card_all_types": r"\b(?:4[0-9]{12}(?:[0-9]{3})?|5[1-5][0-9]{14}|3[47][0-9]{13}|3[0-9]{13}|6(?:011|5[0-9]{2})[0-9]{12})\b",
                "international_phone": r"\b(?:\+?1[-.\s]?)?(?:\(?[0-9]{3}\)?[-.\s]?)?[0-9]{3}[-.\s]?[0-9]{4}\b",
                "email_comprehensive": r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b",
                "passport_numbers": r"\b[A-Z]{1,2}[0-9]{6,9}\b",
                "drivers_license": r"\b[A-Z]{1,2}[0-9]{6,8}\b"
            },
            
            # 财务和金融信息
            "financial_intelligence_patterns": {
                "bank_account_numbers": r"\b[0-9]{8,17}\b",
                "routing_numbers": r"\b[0-9]{9}\b",
                "iban_international": r"\b[A-Z]{2}[0-9]{2}[A-Z0-9]{4,30}\b",
                "swift_codes": r"\b[A-Z]{4}[A-Z]{2}[A-Z0-9]{2}([A-Z0-9]{3})?\b",
                "tax_identification": r"\b[0-9]{2}-[0-9]{7}\b",
                "investment_accounts": r"\b[A-Z]{1,4}[0-9]{6,12}\b"
            },
            
            # 技术凭据和密钥 - 加强检测
            "enhanced_credential_patterns": {
                "api_keys_generic": r"(?i)(?:api[_-]?key|apikey)[\s]*[=:]\s*['\"]?([a-zA-Z0-9_\-]{16,})['\"]?",
                "passwords": r"(?i)password[\s]*[=:]\s*['\"]?([a-zA-Z0-9_@#$%^&*\-!]{8,})['\"]?",
                "secret_keys": r"(?i)(?:secret[_-]?key|secretkey)[\s]*[=:]\s*['\"]?([a-zA-Z0-9_\-]{20,})['\"]?",
                "private_keys": r"-----BEGIN (?:RSA )?PRIVATE KEY-----",
                "aws_credentials": r"(?:AKIA[0-9A-Z]{16}|aws_access_key_id)",
                "jwt_tokens": r"eyJ[a-zA-Z0-9_\-]+\.eyJ[a-zA-Z0-9_\-]+\.[a-zA-Z0-9_\-]+",
                "database_connections": r"(?i)(?:mongodb|mysql|postgresql|oracle)://[^\s]+",
                "encryption_keys": r"(?i)(?:encryption[_-]?key|encryptionkey)[\s]*[=:]\s*['\"]?([a-zA-Z0-9+/=]{24,})['\"]?"
            },
            
            # 健康信息 (PHI) - HIPAA合规
            "healthcare_patterns": {
                "medical_record_numbers": r"\b[A-Z]{2,4}[0-9]{6,10}\b",
                "insurance_member_ids": r"\b[A-Z]{3}[0-9]{6,9}\b",
                "prescription_numbers": r"\b[A-Z]{2}[0-9]{6,8}\b",
                "npi_numbers": r"\b[0-9]{10}\b"  # National Provider Identifier
            },
            
            # 企业机密信息
            "corporate_intelligence_patterns": {
                "employee_ids": r"\b(?:EMP|ID|EMPL)[0-9]{4,8}\b",
                "project_codes": r"\b[A-Z]{2,4}-[0-9]{3,6}\b",
                "contract_numbers": r"\b(?:CONTRACT|CNT)-[A-Z0-9]{6,12}\b",
                "intellectual_property": r"(?i)(?:patent|trademark|copyright|trade[_\s]?secret)",
                "financial_projections": r"(?i)(?:revenue|profit|forecast|budget)[_\s]?\w*[:\s]*\$?[0-9,]+\.?[0-9]*[MmKkBb]?"
            }
        }
        
        sensitivity_analysis = {
            "critical_sensitivity_items": [],
            "high_sensitivity_items": [],
            "medium_sensitivity_items": [],
            "pattern_detection_summary": {},
            "risk_heat_map": {}
        }
        
        for data_source, discovered_items in discovery_results.items():
            for item in discovered_items["items"]:
                item_content = await self.safely_read_content(item["path"])
                detected_patterns = []
                total_risk_score = 0
                
                # 扫描所有敏感信息模式
                for category, patterns in enhanced_sensitivity_patterns.items():
                    for pattern_name, pattern_regex in patterns.items():
                        try:
                            matches = re.findall(pattern_regex, item_content, re.MULTILINE | re.IGNORECASE)
                            
                            if matches:
                                risk_weight = self.get_pattern_risk_weight(category, pattern_name)
                                pattern_risk_score = len(matches) * risk_weight
                                total_risk_score += pattern_risk_score
                                
                                detected_patterns.append({
                                    "category": category,
                                    "pattern_name": pattern_name,
                                    "match_count": len(matches),
                                    "risk_score": pattern_risk_score,
                                    "sample_matches": self.sanitize_matches(matches[:2])  # 只保存2个样本用于分析
                                })
                                
                        except re.error as e:
                            print(f"⚠️ 正则表达式错误: {pattern_name} - {str(e)}")
                            continue
                
                # 分类敏感度级别
                sensitivity_item = {
                    "item_path": item["path"],
                    "total_risk_score": total_risk_score,
                    "detected_patterns": detected_patterns,
                    "sensitivity_classification": self.calculate_sensitivity_classification(total_risk_score),
                    "recommended_protection": self.recommend_protection_measures(total_risk_score, detected_patterns)
                }
                
                if total_risk_score >= 80:
                    sensitivity_analysis["critical_sensitivity_items"].append(sensitivity_item)
                elif total_risk_score >= 40:
                    sensitivity_analysis["high_sensitivity_items"].append(sensitivity_item)
                elif total_risk_score >= 15:
                    sensitivity_analysis["medium_sensitivity_items"].append(sensitivity_item)
                
                # 更新模式检测统计
                for pattern in detected_patterns:
                    pattern_key = f"{pattern['category']}.{pattern['pattern_name']}"
                    sensitivity_analysis["pattern_detection_summary"][pattern_key] = \
                        sensitivity_analysis["pattern_detection_summary"].get(pattern_key, 0) + pattern["match_count"]
        
        # 生成风险热力图
        sensitivity_analysis["risk_heat_map"] = await self.generate_risk_heat_map(sensitivity_analysis)
        
        return sensitivity_analysis
    
    def get_pattern_risk_weight(self, category: str, pattern_name: str) -> float:
        """获取模式风险权重"""
        
        risk_weight_mapping = {
            "enhanced_pii_patterns": 15.0,
            "financial_intelligence_patterns": 18.0,
            "enhanced_credential_patterns": 25.0,  # 最高风险
            "healthcare_patterns": 20.0,
            "corporate_intelligence_patterns": 12.0
        }
        
        # 特定模式的额外权重调整
        high_risk_patterns = ["private_keys", "aws_credentials", "database_connections", "encryption_keys"]
        
        base_weight = risk_weight_mapping.get(category, 10.0)
        
        if pattern_name in high_risk_patterns:
            return base_weight * 1.5  # 高危模式权重加成
        
        return base_weight
    
    async def apply_data_protection_policies(self, classification_results: Dict, 
                                           sensitivity_analysis: Dict) -> Dict:
        """应用数据保护策略"""
        
        protection_results = {
            "encryption_applied": [],
            "access_controls_configured": [],
            "backup_policies_set": [],
            "monitoring_enabled": [],
            "dlp_rules_activated": []
        }
        
        # 处理关键敏感数据
        for critical_item in sensitivity_analysis["critical_sensitivity_items"]:
            file_path = critical_item["item_path"]
            
            # 应用最高级别加密
            encryption_result = await self.encryption_service.apply_quantum_resistant_encryption(
                file_path=file_path,
                encryption_algorithm="kyber_1024",  # 量子抗性算法
                key_escrow=True,
                multi_party_key_management=True
            )
            protection_results["encryption_applied"].append(encryption_result)
            
            # 配置严格访问控制
            access_control_result = await self.configure_enhanced_access_control(
                file_path=file_path,
                access_level="critical_restricted",
                approval_workflow="multi_level_executive",
                monitoring_level="real_time_with_alerts"
            )
            protection_results["access_controls_configured"].append(access_control_result)
            
            # 设置高频备份和版本控制
            backup_result = await self.setup_critical_data_backup(
                file_path=file_path,
                backup_frequency="real_time",
                retention_period="indefinite",
                geographic_distribution=True
            )
            protection_results["backup_policies_set"].append(backup_result)
            
            # 启用高级监控
            monitoring_result = await self.enable_advanced_monitoring(
                file_path=file_path,
                monitoring_type="file_integrity_behavioral_analysis",
                alert_sensitivity="high",
                forensic_logging=True
            )
            protection_results["monitoring_enabled"].append(monitoring_result)
        
        # 处理高敏感数据
        for high_item in sensitivity_analysis["high_sensitivity_items"]:
            file_path = high_item["item_path"]
            
            # 应用企业级加密
            encryption_result = await self.encryption_service.apply_enterprise_encryption(
                file_path=file_path,
                encryption_algorithm="aes_256_gcm",
                key_rotation_schedule="monthly",
                compliance_logging=True
            )
            protection_results["encryption_applied"].append(encryption_result)
            
            # 配置DLP规则
            dlp_result = await self.activate_dlp_protection(
                file_path=file_path,
                sensitivity_patterns=high_item["detected_patterns"],
                prevention_actions=["block_external_share", "watermark_output", "require_justification"]
            )
            protection_results["dlp_rules_activated"].append(dlp_result)
        
        return protection_results
    
    async def comprehensive_risk_assessment(self, protection_results: Dict) -> Dict:
        """全面风险评估"""
        
        risk_assessment = {
            "overall_risk_score": 0.0,
            "risk_breakdown": {
                "data_exposure_risk": 0.0,
                "compliance_violation_risk": 0.0,
                "operational_disruption_risk": 0.0,
                "financial_impact_risk": 0.0
            },
            "critical_vulnerabilities": [],
            "mitigation_recommendations": [],
            "risk_trend_analysis": {}
        }
        
        # 计算数据暴露风险
        unprotected_critical_count = len([
            item for item in protection_results["classification_results"].get("by_sensitivity_level", {}).get("critical", [])
            if not self.is_adequately_protected(item)
        ])
        
        risk_assessment["risk_breakdown"]["data_exposure_risk"] = min(unprotected_critical_count * 0.2, 1.0)
        
        # 计算合规违规风险
        non_compliant_items = await self.identify_non_compliant_items(protection_results)
        risk_assessment["risk_breakdown"]["compliance_violation_risk"] = min(len(non_compliant_items) * 0.15, 1.0)
        
        # 生成缓解建议
        risk_assessment["mitigation_recommendations"] = await self.generate_risk_mitigation_recommendations(
            protection_results, risk_assessment["risk_breakdown"]
        )
        
        # 计算总体风险分数
        risk_assessment["overall_risk_score"] = sum(risk_assessment["risk_breakdown"].values()) / len(risk_assessment["risk_breakdown"])
        
        return risk_assessment

# 使用示例
classification_system = EnterpriseDataClassificationSystem()

# 执行全面数据保护流程
data_sources = [
    "git://github.com/company/critical-project",
    "file:///enterprise/shared/documents",
    "database://production.company.com/customer_data"
]

protection_result = await classification_system.comprehensive_data_discovery_and_protection(data_sources)

print("🛡️ 企业数据保护流程完成:")
print(f"  发现数据项: {protection_result['discovery_summary']['total_items']}")
print(f"  关键敏感文件: {len(protection_result['classification_results']['by_sensitivity_level'].get('critical', []))}")
print(f"  加密保护项: {len(protection_result['protection_status']['encryption_applied'])}")
print(f"  总体风险评分: {protection_result['risk_assessment']['overall_risk_score']:.2f}")

class IntelligentComplianceOrchestrator:
    """智能合规编排系统"""
    
    def __init__(self):
        self.regulation_engines = {
            'gdpr': GDPRIntelligentEngine(),
            'sox': SOXComplianceEngine(),
            'hipaa': HIPAASecurityEngine(),
            'pci_dss': PCIDSSComplianceEngine(),
            'iso27001': ISO27001Engine()
        }
        self.ml_compliance_predictor = MLCompliancePredictor()
        self.automated_remediation = AutomatedRemediationEngine()
        self.executive_reporting = ExecutiveReportingService()
    
    async def continuous_intelligent_compliance_monitoring(self) -> Dict:
        """持续智能合规监控"""
        
        print("📋 启动AI驱动的持续合规监控...")
        
        comprehensive_monitoring = {
            "monitoring_timestamp": datetime.now().isoformat(),
            "regulation_compliance_status": {},
            "predictive_compliance_risks": {},
            "automated_remediation_actions": [],
            "executive_compliance_dashboard": {},
            "regulatory_change_impact_analysis": {}
        }
        
        # 并行监控各项法规
        monitoring_tasks = []
        for regulation, engine in self.regulation_engines.items():
            task = self.monitor_regulation_with_intelligence(regulation, engine)
            monitoring_tasks.append(task)
        
        regulation_results = await asyncio.gather(*monitoring_tasks, return_exceptions=True)
        
        # 处理监控结果
        for idx, (regulation, result) in enumerate(zip(self.regulation_engines.keys(), regulation_results)):
            if isinstance(result, Exception):
                print(f"❌ {regulation.upper()} 监控失败: {str(result)}")
                continue
                
            comprehensive_monitoring["regulation_compliance_status"][regulation] = result
            
            # 检测违规并触发自动修复
            if result["compliance_score"] < 0.85:  # 85%合规阈值
                violations = result.get("detected_violations", [])
                for violation in violations:
                    remediation_result = await self.automated_remediation.attempt_auto_fix(
                        regulation, violation
                    )
                    comprehensive_monitoring["automated_remediation_actions"].append(remediation_result)
        
        # 预测性合规风险分析
        comprehensive_monitoring["predictive_compliance_risks"] = \
            await self.ml_compliance_predictor.predict_future_compliance_risks(
                comprehensive_monitoring["regulation_compliance_status"]
            )
        
        # 生成高管合规仪表板
        comprehensive_monitoring["executive_compliance_dashboard"] = \
            await self.executive_reporting.generate_executive_compliance_dashboard(
                comprehensive_monitoring
            )
        
        # 监控法规变化影响
        comprehensive_monitoring["regulatory_change_impact_analysis"] = \
            await self.analyze_regulatory_change_impact()
        
        return comprehensive_monitoring
    
    async def monitor_regulation_with_intelligence(self, regulation: str, engine: object) -> Dict:
        """智能化法规监控"""
        
        print(f"🔍 智能监控 {regulation.upper()} 合规状态...")
        
        # 执行全面合规检查
        compliance_checks = await engine.comprehensive_compliance_assessment()
        
        # AI增强的违规检测
        ai_enhanced_violations = await self.detect_violations_with_ai(
            regulation, compliance_checks
        )
        
        # 趋势分析和预测
        compliance_trends = await self.analyze_compliance_trends(regulation, compliance_checks)
        
        # 风险量化评估
        risk_quantification = await self.quantify_compliance_risks(
            regulation, compliance_checks, ai_enhanced_violations
        )
        
        # 生成智能建议
        intelligent_recommendations = await self.generate_intelligent_recommendations(
            regulation, compliance_checks, ai_enhanced_violations, risk_quantification
        )
        
        regulation_status = {
            "regulation": regulation,
            "compliance_score": self.calculate_weighted_compliance_score(compliance_checks),
            "detailed_assessments": compliance_checks,
            "detected_violations": ai_enhanced_violations,
            "compliance_trends": compliance_trends,
            "risk_quantification": risk_quantification,
            "intelligent_recommendations": intelligent_recommendations,
            "last_assessment_timestamp": datetime.now().isoformat()
        }
        
        return regulation_status
    
    async def generate_executive_compliance_report(self) -> Dict:
        """生成高管合规报告"""
        
        # 获取最新合规状态
        current_status = await self.continuous_intelligent_compliance_monitoring()
        
        executive_report = {
            "report_metadata": {
                "report_date": datetime.now().isoformat(),
                "report_type": "executive_compliance_summary",
                "scope": "enterprise_wide",
                "classification": "confidential"
            },
            "executive_summary": {},
            "key_performance_indicators": {},
            "risk_heat_map": {},
            "strategic_recommendations": {},
            "regulatory_landscape_analysis": {}
        }
        
        # 生成高管摘要
        executive_report["executive_summary"] = {
            "overall_compliance_score": self.calculate_overall_compliance_score(current_status),
            "critical_violations_count": self.count_critical_violations(current_status),
            "automated_remediation_success_rate": self.calculate_remediation_success_rate(current_status),
            "regulatory_change_impact": self.assess_regulatory_change_impact(current_status),
            "business_risk_exposure": self.calculate_business_risk_exposure(current_status)
        }
        
        # 关键绩效指标
        executive_report["key_performance_indicators"] = {
            "compliance_kpis": await self.calculate_compliance_kpis(current_status),
            "security_kpis": await self.calculate_security_kpis(current_status),
            "operational_efficiency_kpis": await self.calculate_operational_kpis(current_status)
        }
        
        # 风险热力图
        executive_report["risk_heat_map"] = await self.generate_executive_risk_heat_map(current_status)
        
        # 战略建议
        executive_report["strategic_recommendations"] = await self.generate_strategic_recommendations(current_status)
        
        return executive_report

# 使用示例
compliance_orchestrator = IntelligentComplianceOrchestrator()

# 执行持续智能合规监控
monitoring_result = await compliance_orchestrator.continuous_intelligent_compliance_monitoring()

print("📊 智能合规监控完成:")
print(f"  监控法规数量: {len(monitoring_result['regulation_compliance_status'])}")
print(f"  自动修复操作: {len(monitoring_result['automated_remediation_actions'])}")
print(f"  预测风险项目: {len(monitoring_result['predictive_compliance_risks'])}")

# 生成高管报告
executive_report = await compliance_orchestrator.generate_executive_compliance_report()
print(f"🎯 整体合规评分: {executive_report['executive_summary']['overall_compliance_score']:.1%}")
print(f"🚨 关键违规数量: {executive_report['executive_summary']['critical_violations_count']}")