# Nicholas使用的简化检测流程
def detect_vulnerabilities_with_claude():
    # 第一步：让Claude分析代码结构
    analysis_result = claude.analyze_code_structure(target_code)
    
    # 第二步：生成测试用例
    test_cases = claude.generate_test_cases(analysis_result)
    
    # 第三步：自动化测试
    vulnerabilities = claude.execute_tests(test_cases)
    
    # 第四步：验证漏洞真实性
    verified_vulns = claude.verify_vulnerabilities(vulnerabilities)
    
    return verified_vulns

class AI_Vulnerability_Detector:
    def __init__(self):
        self.model = load_claude_model()
        self.test_framework = Automated_Test_Framework()
        
    def detect_vulnerabilities(self, target_code):
        # 代码理解阶段
        code_analysis = self.model.analyze_code(target_code)
        
        # 威胁建模阶段
        threat_model = self.model.build_threat_model(code_analysis)
        
        # 漏洞发现阶段
        test_cases = self.model.generate_test_cases(threat_model)
        vulnerabilities = self.test_framework.execute_tests(test_cases)
        
        # 漏洞验证阶段
        verified_vulns = []
        for vuln in vulnerabilities:
            if self.model.verify_vulnerability(vuln):
                verified_vulns.append(vuln)
        
        # 报告生成阶段
        report = self.model.generate_report(verified_vulns)
        return report

class GhostCMS_Security_Monitor:
    def __init__(self):
        self.ai_detector = AI_Vulnerability_Detector()
        self.monitoring_enabled = True
        
    def continuous_monitoring(self):
        """持续安全监控"""
        while self.monitoring_enabled:
            # 定期扫描代码库
            vulnerabilities = self.ai_detector.scan_codebase()
            
            # 实时警报
            if vulnerabilities:
                self.send_security_alert(vulnerabilities)
            
            # 等待下一次扫描
            time.sleep(3600)  # 每小时扫描一次

class Security_Response_System:
    def __init__(self):
        self.incident_levels = {
            'critical': '立即响应',
            'high': '30分钟内响应',
            'medium': '2小时内响应',
            'low': '24小时内响应'
        }
    
    def handle_incident(self, vulnerability):
        """处理安全事件"""
        level = vulnerability.severity
        
        if level == 'critical':
            self.emergency_response(vulnerability)
        elif level == 'high':
            self.high_priority_response(vulnerability)
        elif level == 'medium':
            self.medium_priority_response(vulnerability)
        else:
            self.low_priority_response(vulnerability)

class Enterprise_AI_Security_Framework:
    def __init__(self):
        self.models = {
            'vulnerability_detection': Claude_Vulnerability_Detector(),
            'code_analysis': AI_Code_Analyzer(),
            'threat_intelligence': AI_Threat_Intel()
        }
        
        self.protocols = {
            'continuous_monitoring': Continuous_Monitoring(),
            'automated_response': Automated_Response(),
            'threat_hunting': Threat_Hunting()
        }
    
    def protect_ai_systems(self):
        """保护AI系统安全"""
        # 对所有AI组件进行安全检测
        for component in self.get_ai_components():
            vulnerabilities = self.models['vulnerability_detection'].detect(component)
            
            if vulnerabilities:
                self.handle_vulnerabilities(vulnerabilities)
    
    def establish_trust(self):
        """建立AI系统信任"""
        # 代码来源验证
        self.verify_code_sources()
        
        # 模型完整性检查
        self.verify_model_integrity()
        
        # 运行时行为监控
        self.monitor_runtime_behavior()

class AI_Supply_Chain_Security:
    def __init__(self):
        self.package_registry = Package_Registry()
        self.model_registry = Model_Registry()
        
    def validate_dependencies(self, project_path):
        """验证项目依赖安全性"""
        # 扫描项目依赖
        dependencies = self.scan_project_dependencies(project_path)
        
        # 检查已知漏洞
        vulnerabilities = self.check_vulnerabilities(dependencies)
        
        # 验证来源可信度
        trust_scores = self.verify_source_trust(dependencies)
        
        return {
            'dependencies': dependencies,
            'vulnerabilities': vulnerabilities,
            'trust_scores': trust_scores
        }
    
    def secure_ai_deployment(self, ai_system):
        """保护AI系统部署"""
        # 容器化部署
        container = self.create_secure_container(ai_system)
        
        # 网络隔离
        isolated_network = self.create_isolated_network()
        
        # 访问控制
        access_control = self.implement_least_privilege()
        
        return {
            'container': container,
            'network': isolated_network,
            'access': access_control
        }

class Secure_Development_Practices:
    def __init__(self):
        self.security_checklist = [
            'input_validation',
            'sql_prevention',
            'xss_protection',
            'authentication',
            'authorization',
            'error_handling'
        ]
    
    def secure_coding(self, code):
        """安全编码实践"""
        # 输入验证
        validated_code = self.validate_input(code)
        
        # SQL注入防护
        sql_safe_code = self.prevent_sql_injection(validated_code)
        
        # XSS防护
        xss_safe_code = self.prevent_xss(sql_safe_code)
        
        return xss_safe_code
    
    def security_review(self, project_path):
        """安全代码审查"""
        # 静态分析
        static_issues = self.static_analysis(project_path)
        
        # 动态测试
        dynamic_issues = self.dynamic_testing(project_path)
        
        # AI辅助审查
        ai_issues = self.ai_assisted_review(project_path)
        
        return {
            'static': static_issues,
            'dynamic': dynamic_issues,
            'ai': ai_issues
        }

class AI_Tool_Security:
    def __init__(self):
        self.ai_services = {
            'chatgpt': ChatGPT_Security(),
            'claude': Claude_Security(),
            'github_copilot': GitHubCopilot_Security()
        }
    
    def safe_ai_usage(self, ai_service, input_data):
        """安全使用AI服务"""
        # 输入清理
        cleaned_input = self.sanitize_input(input_data)
        
        # 权限检查
        permission_check = self.check_permissions(ai_service)
        
        # 输出验证
        validated_output = self.validate_output(ai_service, cleaned_input)
        
        return validated_output
    
    def protect_ai_credentials(self):
        """保护AI服务凭据"""
        # 环境变量管理
        env_vars = self.manage_environment_variables()
        
        # 密钥轮换
        key_rotation = self.implement_key_rotation()
        
        # 访问日志
        access_logs = self.log_access_activity()
        
        return {
            'environment': env_vars,
            'rotation': key_rotation,
            'logs': access_logs
        }

class Vulnerability_Fix_Verification:
    def __init__(self):
        self.test_scenarios = [
            'sql_injection',
            'authentication_bypass',
            'privilege_escalation',
            'data_exfiltration'
        ]
    
    def verify_fix(self, patched_code, vulnerability_type):
        """验证漏洞修复效果"""
        # 构造测试用例
        test_cases = self.generate_test_cases(vulnerability_type)
        
        # 执行安全测试
        results = self.execute_security_tests(patched_code, test_cases)
        
        # 评估修复效果
        assessment = self.assess_fix_effectiveness(results)
        
        return assessment
    
    def regression_testing(self, original_code, patched_code):
        """回归测试"""
        # 功能测试
        functional_results = self.functional_testing(patched_code)
        
        # 性能测试
        performance_results = self.performance_testing(patched_code)
        
        # 安全测试
        security_results = self.security_testing(patched_code)
        
        return {
            'functional': functional_results,
            'performance': performance_results,
            'security': security_results
        }

class AI_Security_Incident_Detector:
    def __init__(self):
        self.baseline_metrics = self._collect_baseline()
        self.detection_rules = self._load_detection_rules()
        
    def detect_anomalies(self, system_data):
        """检测异常行为"""
        # 网络异常检测
        network_anomalies = self._detect_network_anomalies(system_data)
        
        # 行为异常检测
        behavior_anomalies = self._detect_behavior_anomalies(system_data)
        
        # 代码异常检测
        code_anomalies = self._detect_code_anomalies(system_data)
        
        return {
            'network': network_anomalies,
            'behavior': behavior_anomalies,
            'code': code_anomalies
        }
    
    def analyze_threat_patterns(self, incidents):
        """分析威胁模式"""
        # 时间模式分析
        temporal_patterns = self._analyze_temporal_patterns(incidents)
        
        # 攻击链分析
        attack_chain_patterns = self._analyze_attack_chains(incidents)
        
        # 影响范围分析
        impact_analysis = self._analyze_impact_scope(incidents)
        
        return {
            'temporal': temporal_patterns,
            'attack_chain': attack_chain_patterns,
            'impact': impact_analysis
        }

class Automated_Response_System:
    def __init__(self):
        self.response_actions = {
            'isolate': self._isolate_system,
            'patch': self._apply_patch,
            'notify': self._send_notification,
            'backup': self._create_backup,
            'restore': self._restore_system
        }
    
    def execute_response_plan(self, incident, response_plan):
        """执行响应计划"""
        executed_actions = []
        
        for action in response_plan['actions']:
            if action['type'] in self.response_actions:
                result = self.response_actions[action['type']](action['params'])
                executed_actions.append({
                    'action': action['type'],
                    'result': result,
                    'timestamp': datetime.now().isoformat()
                })
        
        return executed_actions
    
    def adaptive_response(self, incident_data):
        """自适应响应"""
        # 根据 incident 类型选择响应策略
        if incident_data['severity'] == 'critical':
            response_strategy = self.critical_response_strategy
        elif incident_data['severity'] == 'high':
            response_strategy = self.high_response_strategy
        else:
            response_strategy = self.normal_response_strategy
        
        return response_strategy(incident_data)

class AI_Security_Certification:
    def __init__(self):
        self.certification_levels = {
            'basic': '基础安全认证',
            'intermediate': '中级安全认证',
            'advanced': '高级安全认证',
            'expert': '专家级安全认证'
        }
        
        self.assessment_criteria = {
            'code_security': '代码安全性评估',
            'model_security': '模型安全性评估',
            'deployment_security': '部署安全性评估',
            'operational_security': '运营安全性评估'
        }
    
    def certify_ai_system(self, ai_system):
        """AI系统安全认证"""
        # 代码安全评估
        code_security_score = self.assess_code_security(ai_system)
        
        # 模型安全评估
        model_security_score = self.assess_model_security(ai_system)
        
        # 部署安全评估
        deployment_security_score = self.assess_deployment_security(ai_system)
        
        # 运营安全评估
        operational_security_score = self.assess_operational_security(ai_system)
        
        overall_score = (code_security_score + model_security_score + 
                        deployment_security_score + operational_security_score) / 4
        
        certification_level = self.determine_certification_level(overall_score)
        
        return {
            'certification_level': certification_level,
            'scores': {
                'code': code_security_score,
                'model': model_security_score,
                'deployment': deployment_security_score,
                'operational': operational_security_score
            },
            'overall_score': overall_score
        }

class Next_Generation_AI_Security:
    def __init__(self):
        self.detection_models = {
            'behavioral_analysis': Behavioral_Analysis_Model(),
            'predictive_detection': Predictive_Detection_Model(),
            'zero_trust_architecture': Zero_Trust_Architecture()
        }
        
        self.response_systems = {
            'automated_response': Automated_Response_System(),
            'predictive_response': Predictive_Response_System(),
            'adaptive_security': Adaptive_Security_System()
        }
    
    def implement_advanced_detection(self):
        """实施高级检测技术"""
        # 行为分析检测
        behavioral_detection = self.detection_models['behavioral_analysis'].detect_anomalies()
        
        # 预测性检测
        predictive_detection = self.detection_models['predictive_detection'].predict_threats()
        
        # 零信任架构
        zero_trust_validation = self.detection_models['zero_trust_architecture'].validate_trust()
        
        return {
            'behavioral': behavioral_detection,
            'predictive': predictive_detection,
            'zero_trust': zero_trust_validation
        }
    
    def build_ai_security_ecosystem(self):
        """构建AI安全生态系统"""
        # 安全编排自动化响应 (SOAR)
        soar_platform = SOAR_Platform()
        
        # 威胁情报平台
        threat_intel = Threat_Intelligence_Platform()
        
        # 安全编排自动化
        security_orchestration = Security_Orchestration()
        
        # 持续安全监控
        continuous_monitoring = Continuous_Security_Monitoring()
        
        return {
            'soar': soar_platform,
            'threat_intel': threat_intel,
            'orchestration': security_orchestration,
            'monitoring': continuous_monitoring
        }

class Hardware_Level_Security:
    def __init__(self):
        self.security_hardware = {
            'intel_sgx': Intel_SGX(),
            'amd_sev': AMD_SEV(),
            'tpm': Trusted_Platform_Module()
        }
        
        self.security_protocols = {
            'encrypted_memory': Encrypted_Memory(),
            'secure_boot': Secure_Boot(),
            'memory_isolation': Memory_Isolation()
        }
    
    def implement_sgx_protection(self, ai_system):
        """实施SGX保护"""
        # 创建SGX enclave
        enclave = self.security_hardware['intel_sgx'].create_enclave()
        
        # 加密模型数据
        encrypted_model = self.security_protocols['encrypted_memory'].encrypt_model(
            ai_system.model
        )
        
        # 安全加载模型
        secure_model_loading = enclave.load_model(encrypted_model)
        
        return {
            'enclave': enclave,
            'encrypted_model': encrypted_model,
            'secure_loading': secure_model_loading
        }
    
    def implement_sev_protection(self, vm_config):
        """实施SEV保护"""
        # 创建安全虚拟机
        secure_vm = self.security_hardware['amd_sev'].create_secure_vm(vm_config)
        
        # 内存加密
        memory_encryption = self.security_protocols['encrypted_memory'].encrypt_vm_memory(
            secure_vm
        )
        
        # 启动保护
        secure_boot = self.security_protocols['secure_boot'].secure_boot_sequence()
        
        return {
            'vm': secure_vm,
            'memory_encryption': memory_encryption,
            'secure_boot': secure_boot
        }