# 1. 安裝Python和相關(guān)依賴

pip install casbin flask sqlalchemy redis

# 2. 安裝數(shù)據(jù)庫(kù)（推薦PostgreSQL）

sudo apt-get install postgresql  # Linux

# 或下載安裝包：https://www.postgresql.org/download/

# 3. 安裝Redis（用于緩存和實(shí)時(shí)權(quán)限檢查）

sudo apt-get install redis-server

# 4. 創(chuàng)建項(xiàng)目目錄

mkdir ai-permission-system
cd ai-permission-system

# 創(chuàng)建項(xiàng)目目錄結(jié)構(gòu)
mkdir -p src/{models,controllers,utils,config}
mkdir -p tests/{unit,integration}
mkdir -p data/{logs,backups}
mkdir -p docs/{architecture,api}

# 創(chuàng)建基礎(chǔ)配置文件
touch config/settings.yaml
touch config/database.yaml
touch config/permission_policy.yaml
touch src/main.py

"""
AI資產(chǎn)分類模型
定義了AI系統(tǒng)中的各種資源及其敏感度級(jí)別
"""
class AIAsset:
    """AI資產(chǎn)基類"""
    def __init__(self, asset_id, asset_type, sensitivity_level):
        self.asset_id = asset_id
        self.asset_type = asset_type  # data, model, api, log等
        self.sensitivity_level = sensitivity_level  # critical/high/medium/low
        # 自動(dòng)計(jì)算權(quán)限基線
        self.base_permission = self._calculate_base_permission()
    def _calculate_base_permission(self):
        """根據(jù)敏感度自動(dòng)計(jì)算基礎(chǔ)權(quán)限"""
        if self.sensitivity_level == 'critical':
            return {'read': False, 'write': False, 'delete': False}
        elif self.sensitivity_level == 'high':
            return {'read': True, 'write': False, 'delete': False}
        elif self.sensitivity_level == 'medium':
            return {'read': True, 'write': True, 'delete': False}
        elif self.sensitivity_level == 'low':
            return {'read': True, 'write': True, 'delete': True}
        else:
            return {'read': False, 'write': False, 'delete': False}
    def __repr__(self):
        return f"AIAsset({self.asset_id}, {self.asset_type}, {self.sensitivity_level})"
# 具體的AI資產(chǎn)類型定義
class TrainingDataAsset(AIAsset):
    """訓(xùn)練數(shù)據(jù)集"""
    def __init__(self, asset_id, data_size, contains_personal_data=False):
        sensitivity = 'critical' if contains_personal_data else 'high'
        super().__init__(asset_id, 'training_data', sensitivity)
        self.data_size = data_size
        self.contains_personal_data = contains_personal_data
class ModelParameterAsset(AIAsset):
    """模型參數(shù)"""
    def __init__(self, asset_id, model_type, training_cost):
        # 根據(jù)訓(xùn)練成本和模型類型確定敏感度
        if training_cost > 10000 or model_type == 'proprietary':
            sensitivity = 'critical'
        elif training_cost > 1000:
            sensitivity = 'high'
        else:
            sensitivity = 'medium'
        super().__init__(asset_id, 'model_parameters', sensitivity)
        self.model_type = model_type
        self.training_cost = training_cost
class InferenceAPIAsset(AIAsset):
    """推理API"""
    def __init__(self, asset_id, request_limit_per_minute):
        sensitivity = 'medium' if request_limit_per_minute > 100 else 'low'
        super().__init__(asset_id, 'inference_api', sensitivity)
        self.request_limit = request_limit_per_minute
class TrainingLogAsset(AIAsset):
    """訓(xùn)練日志"""
    def __init__(self, asset_id, contains_metrics=False):
        sensitivity = 'medium' if contains_metrics else 'low'
        super().__init__(asset_id, 'training_log', sensitivity)
        self.contains_metrics = contains_metrics
# 使用示例
if __name__ == "__main__":
    # 創(chuàng)建一些示例資產(chǎn)
    user_data = TrainingDataAsset('user_dataset_v1', 1000000, contains_personal_data=True)
    llm_model = ModelParameterAsset('llm_v2', 'llm', 50000)
    api_endpoint = InferenceAPIAsset('chat_api_v1', 50)
    training_log = TrainingLogAsset('training_log_2026_03_22', contains_metrics=True)
    print(f"用戶數(shù)據(jù)集權(quán)限: {user_data.base_permission}")
    print(f"LLM模型權(quán)限: {llm_model.base_permission}")
    print(f"API端點(diǎn)權(quán)限: {api_endpoint.base_permission}")
    print(f"訓(xùn)練日志權(quán)限: {training_log.base_permission}")

"""
用戶角色體系設(shè)計(jì)
定義了AI系統(tǒng)中的各種角色及其權(quán)限基線
"""
class UserRole:
    """用戶角色基類"""
    def __init__(self, role_name, role_description):
        self.role_name = role_name
        self.role_description = role_description
        self.permission_matrix = {}  # 權(quán)限矩陣
    def add_permission(self, asset_type, permissions):
        """為特定資產(chǎn)類型添加權(quán)限"""
        self.permission_matrix[asset_type] = permissions
    def get_permission_for(self, asset_type):
        """獲取對(duì)特定資產(chǎn)類型的權(quán)限"""
        if asset_type in self.permission_matrix:
            return self.permission_matrix[asset_type]
        else:
            return {'read': False, 'write': False, 'delete': False}
    def __repr__(self):
        return f"UserRole({self.role_name})"
# 具體的AI系統(tǒng)角色定義
class DataEngineerRole(UserRole):
    """數(shù)據(jù)工程師"""
    def __init__(self):
        super().__init__('data_engineer', '負(fù)責(zé)數(shù)據(jù)處理和準(zhǔn)備')
        # 數(shù)據(jù)工程師的權(quán)限配置
        self.add_permission('training_data', {'read': True, 'write': True, 'delete': False})
        self.add_permission('processed_data', {'read': True, 'write': True, 'delete': False})
        self.add_permission('model_parameters', {'read': False, 'write': False, 'delete': False})
        self.add_permission('inference_api', {'read': False, 'write': False, 'delete': False})
        self.add_permission('training_log', {'read': True, 'write': False, 'delete': False})
class MLEngineerRole(UserRole):
    """機(jī)器學(xué)習(xí)工程師"""
    def __init__(self):
        super().__init__('ml_engineer', '負(fù)責(zé)模型訓(xùn)練和優(yōu)化')
        # ML工程師的權(quán)限配置
        self.add_permission('training_data', {'read': True, 'write': False, 'delete': False})
        self.add_permission('processed_data', {'read': True, 'write': True, 'delete': False})
        self.add_permission('model_parameters', {'read': True, 'write': True, 'delete': False})
        self.add_permission('inference_api', {'read': True, 'write': True, 'delete': False})
        self.add_permission('training_log', {'read': True, 'write': True, 'delete': False})
class DeploymentEngineerRole(UserRole):
    """部署工程師"""
    def __init__(self):
        super().__init__('deployment_engineer', '負(fù)責(zé)模型部署和API管理')
        # 部署工程師的權(quán)限配置
        self.add_permission('training_data', {'read': False, 'write': False, 'delete': False})
        self.add_permission('processed_data', {'read': False, 'write': False, 'delete': False})
        self.add_permission('model_parameters', {'read': True, 'write': False, 'delete': False})
        self.add_permission('inference_api', {'read': True, 'write': True, 'delete': True})
        self.add_permission('training_log', {'read': True, 'write': False, 'delete': False})
class ProductManagerRole(UserRole):
    """產(chǎn)品經(jīng)理"""
    def __init__(self):
        super().__init__('product_manager', '負(fù)責(zé)產(chǎn)品需求和用戶體驗(yàn)')
        # 產(chǎn)品經(jīng)理的權(quán)限配置
        self.add_permission('training_data', {'read': True, 'write': False, 'delete': False})
        self.add_permission('processed_data', {'read': True, 'write': False, 'delete': False})
        self.add_permission('model_parameters', {'read': True, 'write': False, 'delete': False})
        self.add_permission('inference_api', {'read': True, 'write': True, 'delete': False})
        self.add_permission('training_log', {'read': True, 'write': False, 'delete': False})
class CustomerRole(UserRole):
    """客戶/用戶"""
    def __init__(self):
        super().__init__('customer', '最終使用AI服務(wù)的用戶')
        # 客戶的權(quán)限配置
        self.add_permission('training_data', {'read': False, 'write': False, 'delete': False})
        self.add_permission('processed_data', {'read': False, 'write': False, 'delete': False})
        self.add_permission('model_parameters', {'read': False, 'write': False, 'delete': False})
        self.add_permission('inference_api', {'read': True, 'write': False, 'delete': False})
        self.add_permission('training_log', {'read': False, 'write': False, 'delete': False})
# 使用示例
if __name__ == "__main__":
    # 創(chuàng)建各種角色
    data_engineer = DataEngineerRole()
    ml_engineer = MLEngineerRole()
    deployment_engineer = DeploymentEngineerRole()
    print(f"數(shù)據(jù)工程師權(quán)限矩陣: {data_engineer.permission_matrix}")
    print(f"ML工程師權(quán)限矩陣: {ml_engineer.permission_matrix}")
    print(f"部署工程師權(quán)限矩陣: {deployment_engineer.permission_matrix}")

"""
使用Casbin實(shí)現(xiàn)AI系統(tǒng)權(quán)限控制
"""
import casbin
from casbin import persist
class AIPermissionController:
    """AI權(quán)限控制器"""
    def __init__(self):
        # 加載權(quán)限策略
        self.enforcer = casbin.Enforcer(
            "config/permission_model.conf",  # 模型配置文件
            "config/permission_policy.csv"   # 策略配置文件
        )
        # 創(chuàng)建適配器（連接到數(shù)據(jù)庫(kù)）
        self.adapter = persist.Adapter()
        # 初始化上下文存儲(chǔ)
        self.context_store = {}
    def check_access(self, user_id, resource_id, action):
        """檢查用戶是否有權(quán)限執(zhí)行操作"""
        # 基礎(chǔ)權(quán)限檢查
        result = self.enforcer.enforce(user_id, resource_id, action)
        # 如果基礎(chǔ)檢查通過，進(jìn)行上下文檢查
        if result:
            context_result = self._check_context(user_id, resource_id, action)
            return context_result
        return False
    def _check_context(self, user_id, resource_id, action):
        """上下文檢查：時(shí)間、地點(diǎn)、系統(tǒng)狀態(tài)等"""
        context = self._get_context(user_id, resource_id)
        # 檢查時(shí)間限制
        if context['time_restricted'] and not self._is_in_time_window():
            return False
        # 檢查地點(diǎn)限制
        if context['location_restricted'] and not self._is_in_location():
            return False
        # 檢查系統(tǒng)狀態(tài)
        if context['system_status'] != 'normal':
            return False
        # 檢查歷史行為
        if self._has_abnormal_history(user_id):
            return False
        return True
    def _get_context(self, user_id, resource_id):
        """獲取當(dāng)前權(quán)限上下文"""
        if (user_id, resource_id) in self.context_store:
            return self.context_store[(user_id, resource_id)]
        else:
            return {
                'time_restricted': False,
                'location_restricted': False,
                'system_status': 'normal'
            }
    def grant_permission(self, user_id, resource_id, action, reason=""):
        """授予權(quán)限（需要審計(jì)）"""
        # 記錄授予原因
        grant_record = {
            'timestamp': self._get_current_time(),
            'user_id': user_id,
            'resource_id': resource_id,
            'action': action,
            'reason': reason,
            'granted_by': self._current_admin()
        }
        # 保存到審計(jì)日志
        self._save_to_audit_log(grant_record)
        # 實(shí)際授予權(quán)限
        self.enforcer.add_policy(user_id, resource_id, action)
        return True
    def revoke_permission(self, user_id, resource_id, action, reason=""):
        """撤銷權(quán)限"""
        # 記錄撤銷原因
        revoke_record = {
            'timestamp': self._get_current_time(),
            'user_id': user_id,
            'resource_id': resource_id,
            'action': action,
            'reason': reason,
            'revoked_by': self._current_admin()
        }
        # 保存到審計(jì)日志
        self._save_to_audit_log(revoke_record)
        # 實(shí)際撤銷權(quán)限
        self.enforcer.remove_policy(user_id, resource_id, action)
        return True
    def _save_to_audit_log(self, record):
        """保存審計(jì)記錄"""
        # 這里可以連接到數(shù)據(jù)庫(kù)或文件系統(tǒng)
        print(f"[審計(jì)日志] {record}")
        # 實(shí)際實(shí)現(xiàn)中應(yīng)該寫入數(shù)據(jù)庫(kù)
    def _get_current_time(self):
        """獲取當(dāng)前時(shí)間"""
        import datetime
        return datetime.datetime.now()
    def _current_admin(self):
        """獲取當(dāng)前管理員"""
        return "system_admin"  # 實(shí)際實(shí)現(xiàn)中應(yīng)該根據(jù)會(huì)話確定
    def _is_in_time_window(self):
        """檢查是否在允許的時(shí)間窗口內(nèi)"""
        import datetime
        now = datetime.datetime.now().hour
        # 假設(shè)工作時(shí)間是9-18點(diǎn)
        return 9 <= now <= 18
    def _is_in_location(self):
        """檢查是否在允許的地理位置"""
        # 這里可以集成IP地理位置檢查
        return True  # 簡(jiǎn)化實(shí)現(xiàn)
    def _has_abnormal_history(self, user_id):
        """檢查用戶是否有異常歷史"""
        # 這里可以檢查用戶的歷史訪問記錄
        return False  # 簡(jiǎn)化實(shí)現(xiàn)
# 使用示例
if __name__ == "__main__":
    # 初始化權(quán)限控制器
    controller = AIPermissionController()
    # 測(cè)試權(quán)限檢查
    result = controller.check_access("data_engineer_001", "user_dataset_v1", "read")
    print(f"數(shù)據(jù)工程師讀取用戶數(shù)據(jù)集: {result}")
    # 測(cè)試授予權(quán)限
    controller.grant_permission("ml_engineer_002", "llm_model_v2", "write", 
                                "需要修改模型參數(shù)以優(yōu)化性能")
    # 測(cè)試撤銷權(quán)限
    controller.revoke_permission("product_manager_003", "training_log_2026", "delete",
                                "誤操作，不應(yīng)刪除日志")

# Casbin權(quán)限模型配置文件
[request_definition]
r = sub, obj, act

[policy_definition]
p = sub, obj, act

[role_definition]
g = _, _

[policy_effect]
e = some(where (p.eft == allow))

[matchers]
m = g(r.sub, p.sub) && r.obj == p.obj && r.act == p.act

p, data_engineer, training_data, read
p, data_engineer, training_data, write
p, data_engineer, training_data, delete
p, ml_engineer, model_parameters, read
p, ml_engineer, model_parameters, write
p, deployment_engineer, inference_api, read
p, deployment_engineer, inference_api, write
p, deployment_engineer, inference_api, delete
p, product_manager, training_log, read
p, customer, inference_api, read

"""
AI權(quán)限控制系統(tǒng)主程序
整合所有組件，提供完整的權(quán)限管理功能
"""
from models.ai_assets import TrainingDataAsset, ModelParameterAsset, InferenceAPIAsset, TrainingLogAsset
from models.user_roles import DataEngineerRole, MLEngineerRole, DeploymentEngineerRole, ProductManagerRole, CustomerRole
from controllers.permission_controller import AIPermissionController
import json
class AIPermissionSystem:
    """完整的AI權(quán)限控制系統(tǒng)"""
    def __init__(self):
        # 初始化所有組件
        self.assets = {}  # AI資產(chǎn)存儲(chǔ)
        self.users = {}   # 用戶存儲(chǔ)
        self.controller = AIPermissionController()
        # 初始化角色
        self.roles = {
            'data_engineer': DataEngineerRole(),
            'ml_engineer': MLEngineerRole(),
            'deployment_engineer': DeploymentEngineerRole(),
            'product_manager': ProductManagerRole(),
            'customer': CustomerRole()
        }
        # 初始化審計(jì)日志
        self.audit_log = []
    def register_asset(self, asset):
        """注冊(cè)AI資產(chǎn)"""
        self.assets[asset.asset_id] = asset
        # 自動(dòng)根據(jù)資產(chǎn)敏感度設(shè)置基礎(chǔ)權(quán)限
        self._set_base_permissions(asset)
        # 記錄審計(jì)日志
        self.log_audit('asset_registered', f"注冊(cè)資產(chǎn): {asset}")
        return asset.asset_id
    def _set_base_permissions(self, asset):
        """根據(jù)資產(chǎn)敏感度自動(dòng)設(shè)置基礎(chǔ)權(quán)限"""
        if asset.sensitivity_level == 'critical':
            # 關(guān)鍵資產(chǎn)：只有管理員可以訪問
            self.controller.grant_permission('system_admin', asset.asset_id, 'read', '自動(dòng)設(shè)置')
            self.controller.grant_permission('system_admin', asset.asset_id, 'write', '自動(dòng)設(shè)置')
        elif asset.sensitivity_level == 'high':
            # 高敏感資產(chǎn)：特定角色可讀
            for role_name, role in self.roles.items():
                if role.get_permission_for(asset.asset_type)['read']:
                    self.controller.grant_permission(role_name, asset.asset_id, 'read', '自動(dòng)設(shè)置')
        elif asset.sensitivity_level == 'medium':
            # 中等敏感資產(chǎn)：按角色矩陣設(shè)置
            for role_name, role in self.roles.items():
                permissions = role.get_permission_for(asset.asset_type)
                for action, allowed in permissions.items():
                    if allowed:
                        self.controller.grant_permission(role_name, asset.asset_id, action, '自動(dòng)設(shè)置')
    def register_user(self, user_id, role_name):
        """注冊(cè)用戶"""
        if role_name not in self.roles:
            raise ValueError(f"角色 {role_name} 不存在")
        self.users[user_id] = {
            'role': role_name,
            'created_at': self._get_current_time(),
            'last_access': None
        }
        # 記錄審計(jì)日志
        self.log_audit('user_registered', f"注冊(cè)用戶: {user_id} 角色: {role_name}")
        return True
    def check_user_access(self, user_id, asset_id, action):
        """檢查用戶訪問權(quán)限"""
        # 檢查用戶是否存在
        if user_id not in self.users:
            self.log_audit('access_denied', f"用戶不存在: {user_id}")
            return False
        # 獲取用戶角色
        user_role = self.users[user_id]['role']
        # 檢查資產(chǎn)是否存在
        if asset_id not in self.assets:
            self.log_audit('access_denied', f"資產(chǎn)不存在: {asset_id}")
            return False
        # 使用控制器檢查權(quán)限
        result = self.controller.check_access(user_role, asset_id, action)
        # 記錄審計(jì)日志
        if result:
            self.log_audit('access_granted', 
                          f"用戶 {user_id} ({user_role}) 成功訪問 {asset_id} ({action})")
            self.users[user_id]['last_access'] = self._get_current_time()
        else:
            self.log_audit('access_denied', 
                          f"用戶 {user_id} ({user_role}) 被拒絕訪問 {asset_id} ({action})")
        return result
    def log_audit(self, event_type, message):
        """記錄審計(jì)日志"""
        audit_entry = {
            'timestamp': self._get_current_time(),
            'event_type': event_type,
            'message': message,
            'system_state': self._get_system_state()
        }
        self.audit_log.append(audit_entry)
        # 打印到控制臺(tái)（實(shí)際應(yīng)用中應(yīng)該寫入數(shù)據(jù)庫(kù)）
        print(f"[審計(jì)] {audit_entry}")
    def _get_current_time(self):
        """獲取當(dāng)前時(shí)間"""
        import datetime
        return datetime.datetime.now().isoformat()
    def _get_system_state(self):
        """獲取系統(tǒng)狀態(tài)"""
        return {
            'total_assets': len(self.assets),
            'total_users': len(self.users),
            'audit_log_count': len(self.audit_log)
        }
    def export_configuration(self):
        """導(dǎo)出配置"""
        config = {
            'assets': {id: vars(asset) for id, asset in self.assets.items()},
            'users': self.users,
            'roles': {name: vars(role) for name, role in self.roles.items()},
            'audit_log': self.audit_log[-100:]  # 最近100條審計(jì)日志
        }
        return json.dumps(config, indent=2)
    def import_configuration(self, config_json):
        """導(dǎo)入配置"""
        config = json.loads(config_json)
        # 這里可以實(shí)現(xiàn)配置導(dǎo)入邏輯
        print(f"導(dǎo)入配置: {len(config['assets'])} 個(gè)資產(chǎn), {len(config['users'])} 個(gè)用戶")
# 使用示例
if __name__ == "__main__":
    # 創(chuàng)建完整的權(quán)限系統(tǒng)
    system = AIPermissionSystem()
    # 注冊(cè)一些AI資產(chǎn)
    user_dataset = TrainingDataAsset('user_dataset_v1', 1000000, contains_personal_data=True)
    llm_model = ModelParameterAsset('llm_v2', 'llm', 50000)
    chat_api = InferenceAPIAsset('chat_api_v1', 50)
    asset_ids = [
        system.register_asset(user_dataset),
        system.register_asset(llm_model),
        system.register_asset(chat_api)
    ]
    print(f"注冊(cè)了 {len(asset_ids)} 個(gè)AI資產(chǎn)")
    # 注冊(cè)一些用戶
    user_ids = [
        system.register_user('john_data_engineer', 'data_engineer'),
        system.register_user('mary_ml_engineer', 'ml_engineer'),
        system.register_user('tom_product_manager', 'product_manager')
    ]
    print(f"注冊(cè)了 {len(user_ids)} 個(gè)用戶")
    # 測(cè)試權(quán)限檢查
    print("\n=== 權(quán)限測(cè)試 ===\n")
    # 測(cè)試1: 數(shù)據(jù)工程師讀取用戶數(shù)據(jù)集
    result1 = system.check_user_access('john_data_engineer', 'user_dataset_v1', 'read')
    print(f"數(shù)據(jù)工程師讀取用戶數(shù)據(jù)集: {result1}")
    # 測(cè)試2: ML工程師寫入模型參數(shù)
    result2 = system.check_user_access('mary_ml_engineer1', 'llm_v2', 'write')
    print(f"ML工程師寫入模型參數(shù): {result2}")
    # 測(cè)試3: 產(chǎn)品經(jīng)理刪除訓(xùn)練日志（應(yīng)該被拒絕）
    result3 = system.check_user_access('tom_product_manager', 'llm_v2', 'delete')
    print(f"產(chǎn)品經(jīng)理刪除模型參數(shù): {result3}")
    # 導(dǎo)出配置
    print("\n=== 配置導(dǎo)出 ===\n")
    config_json = system.export_configuration()
    print(f"配置導(dǎo)出大小: {len(config_json)} 字符")

"""
AI權(quán)限系統(tǒng)單元測(cè)試
"""
import unittest
from src.main import AIPermissionSystem
from src.models.ai_assets import TrainingDataAsset, ModelParameterAsset
from src.models.user_roles import DataEngineerRole, MLEngineerRole
class TestAIPermissionSystem(unittest.TestCase):
    """AI權(quán)限系統(tǒng)測(cè)試類"""
    def setUp(self):
        """測(cè)試初始化"""
        self.system = AIPermissionSystem()
        # 注冊(cè)測(cè)試資產(chǎn)
        self.user_data = TrainingDataAsset('test_user_data', 1000, contains_personal_data=True)
        self.model_param = ModelParameterAsset('test_model', 'classification', 500)
        self.system.register_asset(self.user_data)
        self.system.register_asset(self.model_param)
        # 注冊(cè)測(cè)試用戶
        self.system.register_user('test_data_engineer', 'data_engineer')
        self.system.register_user('test_ml_engineer', 'ml_engineer')
    def test_critical_asset_access(self):
        """測(cè)試關(guān)鍵資產(chǎn)訪問"""
        # 數(shù)據(jù)工程師應(yīng)該不能刪除包含個(gè)人數(shù)據(jù)的資產(chǎn)
        result = self.system.check_user_access('test_data_engineer', 'test_user_data', 'delete')
        self.assertFalse(result, "數(shù)據(jù)工程師不應(yīng)能刪除包含個(gè)人數(shù)據(jù)的資產(chǎn)")
    def test_role_permission_matrix(self):
        """測(cè)試角色權(quán)限矩陣"""
        # ML工程師應(yīng)該能讀取模型參數(shù)
        result = self.system.check_user_access('test_ml_engineer', 'test_model', 'read')
        self.assertTrue(result, "ML工程師應(yīng)能讀取模型參數(shù)")
    def test_audit_logging(self):
        """測(cè)試審計(jì)日志"""
        # 執(zhí)行一個(gè)訪問操作
        self.system.check_user_access('test_data_engineer', 'test_user_data', 'read')
        # 檢查審計(jì)日志
        audit_log = self.system.audit_log
        self.assertGreater(len(audit_log), 0, "審計(jì)日志應(yīng)包含記錄")
        # 檢查最新的審計(jì)記錄
        latest_event = audit_log[-1]['event_type']
        self.assertIn(latest_event, ['access_granted', 'access_denied'], 
                      "審計(jì)事件類型應(yīng)為access_granted或access_denied")
    def test_asset_auto_permission(self):
        """測(cè)試資產(chǎn)自動(dòng)權(quán)限設(shè)置"""
        # 關(guān)鍵資產(chǎn)應(yīng)只有管理員權(quán)限
        # 這里簡(jiǎn)化測(cè)試：檢查基礎(chǔ)權(quán)限設(shè)置
        print("資產(chǎn)自動(dòng)權(quán)限設(shè)置測(cè)試通過")
    def test_config_export(self):
        """測(cè)試配置導(dǎo)出"""
        config_json = self.system.export_configuration()
        self.assertIsInstance(config_json, str, "配置導(dǎo)出應(yīng)為字符串")
        self.assertGreater(len(config_json), 100, "配置導(dǎo)出應(yīng)有足夠的內(nèi)容")
if __name__ == '__main__':
    unittest.main()

version: '3.8'
services:
  # AI權(quán)限服務(wù)
  ai-permission-service:
    build: .
    ports:
      - "8000:8000"
    environment:
      - DATABASE_URL=postgresql://admin:password@db:5432/ai_permission_db
      - REDIS_URL=redis://redis:6379
      - LOG_LEVEL=INFO
    depends_on:
      - db
      - redis
  # PostgreSQL數(shù)據(jù)庫(kù)
  db:
    image: postgres:14
    environment:
      - POSTGRES_DB=ai_permission_db
      - POSTGRES_USER=admin
      - POSTGRES_PASSWORD=password
    volumes:
      - db_data:/var/lib/postgresql/data
      - ./config/database_init.sql:/docker-entrypoint-initdb.d/init.sql
  # Redis緩存
  redis:
    image: redis:7
    ports:
      - "6379:6379"
    volumes:
      - redis_data:/data
  # 監(jiān)控服務(wù)
  monitor:
    image: grafana/grafana:latest
    ports:
      - "3000:3000"
    environment:
      - GF_SECURITY_ADMIN_PASSWORD=admin
    volumes:
      - grafana_data:/var/lib/grafana
volumes:
  db_data:
  redis_data:
  grafana_data:

FROM python:3.9-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .

EXPOSE 8000

CMD ["python", "src/main.py"]

# AI權(quán)限系統(tǒng)監(jiān)控配置
monitoring:
  metrics:
    - permission_check_count
    - access_granted_count
    - access_denied_count
    - audit_log_size
    - user_count
    - asset_count
  alerts:
    high_risk_access:
      threshold: 10  # 每小時(shí)超過10次高風(fēng)險(xiǎn)訪問
      action: email_to_admin
    abnormal_pattern:
      threshold: 5   # 連續(xù)5次異常模式
      action: block_user_temporarily
    system_overload:
      threshold: 1000 # 每秒權(quán)限檢查超過1000次
      action: scale_up_service
  dashboards:
    realtime_monitoring:
      panels:
        - permission_heatmap
        - user_activity
        - asset_access_pattern
    security_report:
      panels:
        - risk_assessment
        - audit_summary
        - compliance_check
    performance:
      panels:
        - response_time
        - system_load
        - error_rate

"""
AI權(quán)限系統(tǒng)自動(dòng)化運(yùn)維腳本
"""
import subprocess
import json
import time
from datetime import datetime
class AutomatedOps:
    """自動(dòng)化運(yùn)維"""
    def daily_backup(self):
        """每日備份"""
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        backup_file = f"data/backups/system_backup_{timestamp}.json"
        # 導(dǎo)出當(dāng)前配置
        subprocess.run(["python", "src/main.py", "--export", backup_file])
        print(f"[備份] 創(chuàng)建備份文件: {backup_file}")
    def check_system_health(self):
        """檢查系統(tǒng)健康"""
        health_report = {
            'timestamp': datetime.now().isoformat(),
            'database_connection': self._check_db(),
            'redis_connection': self._check_redis(),
            'service_response': self._check_service(),
            'audit_log_rotation': self._check_log_rotation(),
            'permission_policy_validity': self._check_policy()
        }
        # 保存健康報(bào)告
        with open("data/logs/health_report.json", "w") as f:
            json.dump(health_report, f, indent=2)
        # 如果有問題，發(fā)送警報(bào)
        if not all(health_report.values()):
            self.send_alert(health_report)
    def rotate_audit_logs(self):
        """審計(jì)日志輪轉(zhuǎn)"""
        # 將舊的審計(jì)日志歸檔
        archive_file = f"data/logs/audit_archive_{datetime.now().strftime('%Y%m')}.json"
        subprocess.run(["python", "scripts/log_rotation.py", archive_file])
        print(f"[日志輪轉(zhuǎn)] 歸檔審計(jì)日志: {archive_file}")
    def update_permission_policies(self):
        """更新權(quán)限策略"""
        # 從Git獲取最新策略
        subprocess.run(["git", "pull", "origin", "main"])
        # 重新加載策略
        subprocess.run(["python", "scripts/policy_update.py"])
        print("[策略更新] 更新權(quán)限策略完成")
    def _check_db(self):
        """檢查數(shù)據(jù)庫(kù)連接"""
        try:
            # 這里應(yīng)該實(shí)現(xiàn)實(shí)際的數(shù)據(jù)庫(kù)檢查
            return True
        except Exception as e:
            print(f"[健康檢查] 數(shù)據(jù)庫(kù)連接失敗: {e}")
            return False
    def _check_redis(self):
        """檢查Redis連接"""
        try:
            # 這里應(yīng)該實(shí)現(xiàn)實(shí)際的Redis檢查
            return True
        except Exception as e:
            print(f"[健康檢查] Redis連接失敗: {e}")
            return False
    def _check_service(self):
        """檢查服務(wù)響應(yīng)"""
        try:
            response = subprocess.run(["curl", "http://localhost:8000/health"], 
                                      capture_output=True, text=True)
            return response.stdout.strip() == "OK"
        except Exception as e:
            print(f"[健康檢查] 服務(wù)響應(yīng)失敗: {e}")
            return False
    def _check_log_rotation(self):
        """檢查日志輪轉(zhuǎn)"""
        # 檢查日志文件大小
        log_size = subprocess.run(["du", "-sh", "data/logs/audit.log"], 
                                  capture_output=True, text=True)
        size_str = log_size.stdout.split()[0]
        # 如果大于100MB，需要輪轉(zhuǎn)
        if "M" in size_str:
            size_mb = float(size_str.replace("M", ""))
            return size_mb < 100
        else:
            return True
    def _check_policy(self):
        """檢查權(quán)限策略有效性"""
        # 運(yùn)行策略測(cè)試
        test_result = subprocess.run(["python", "tests/unit/test_permission_policy.py"], 
                                     capture_output=True, text=True)
        return test_result.returncode == 0
    def send_alert(self, health_report):
        """發(fā)送警報(bào)"""
        problem_areas = []
        for area, status in health_report.items():
            if not status:
                problem_areas.append(area)
        alert_message = f"AI權(quán)限系統(tǒng)健康問題: {', '.join(problem_areas)}"
        # 這里可以發(fā)送郵件、短信或通知
        print(f"[警報(bào)] {alert_message}")
if __name__ == "__main__":
    ops = AutomatedOps()
    print("=== 開始自動(dòng)化運(yùn)維 ===\n")
    # 執(zhí)行每日備份
    ops.daily_backup()
    # 檢查系統(tǒng)健康
    ops.check_system_health()
    # 如果需要，輪轉(zhuǎn)審計(jì)日志
    if not ops._check_log_rotation():
        ops.rotate_audit_logs()
    print("\n=== 自動(dòng)化運(yùn)維完成 ===")

"""
模擬Meta AI數(shù)據(jù)泄露事故
演示權(quán)限配置錯(cuò)誤導(dǎo)致的敏感數(shù)據(jù)暴露
"""
import time
from src.main import AIPermissionSystem
from src.models.ai_assets import TrainingDataAsset, ModelParameterAsset
def simulate_leak_scenario():
    """模擬數(shù)據(jù)泄露場(chǎng)景"""
    print("=== 模擬Meta AI數(shù)據(jù)泄露事故 ===\n")
    # 創(chuàng)建權(quán)限系統(tǒng)
    system = AIPermissionSystem()
    # 創(chuàng)建一些敏感資產(chǎn)
    sensitive_data = TrainingDataAsset('meta_sensitive_data', 5000000, contains_personal_data=True)
    proprietary_model = ModelParameterAsset('meta_proprietary_model', 'llm', 100000)
    system.register_asset(sensitive_data)
    system.register_asset(proprietary_model)
    # 注冊(cè)一些用戶
    system.register_user('engineer_john', 'data_engineer')
    system.register_user('engineer_mary', 'ml_engineer')
    system.register_user('admin_tom', 'system_admin')
    print("初始狀態(tài)：敏感數(shù)據(jù)只有管理員可訪問")
    print(f"管理員訪問敏感數(shù)據(jù): {system.check_user_access('admin_tom', 'meta_sensitive_data', 'read')}")
    print(f"工程師訪問敏感數(shù)據(jù): {system.check_user_access('engineer_john', 'meta_sensitive_data', 'read')}")
    print("\n=== 模擬配置錯(cuò)誤 ===\n")
    # 模擬Meta的配置錯(cuò)誤：將敏感數(shù)據(jù)權(quán)限設(shè)置為"全員可見"
    print("模擬錯(cuò)誤：手動(dòng)將敏感數(shù)據(jù)權(quán)限設(shè)置為全員可見")
    # 錯(cuò)誤配置：授予所有角色讀取權(quán)限
    all_roles = ['data_engineer', 'ml_engineer', 'deployment_engineer', 'product_manager', 'customer']
    for role in all_roles:
        # 模擬權(quán)限配置錯(cuò)誤
        system.controller.grant_permission(role, 'meta_sensitive_data', 'read', '錯(cuò)誤配置：全員可見')
    print("配置錯(cuò)誤已發(fā)生！敏感數(shù)據(jù)現(xiàn)在全員可見")
    print("\n=== 檢測(cè)到泄露 ===\n")
    # 模擬泄露檢測(cè)
    leak_detected = False
    for role in all_roles:
        access_result = system.check_user_access(f'test_{role}', 'meta_sensitive_data', 'read')
        if access_result:
            print(f"檢測(cè)到: {role} 角色可以訪問敏感數(shù)據(jù)")
            leak_detected = True
    if leak_detected:
        print("\n[警報(bào)] 檢測(cè)到敏感數(shù)據(jù)泄露！")
        print("立即執(zhí)行應(yīng)急響應(yīng)...")
        # 模擬應(yīng)急響應(yīng)
        print("1. 立即撤銷錯(cuò)誤權(quán)限")
        for role in all_roles:
            system.controller.revoke_permission(role, 'meta_sensitive_data', 'read', '緊急修復(fù)')
        print("2. 鎖定系統(tǒng)")
        print("3. 通知安全團(tuán)隊(duì)")
        print("4. 審計(jì)日志分析")
        print("\n應(yīng)急響應(yīng)完成，系統(tǒng)已修復(fù)")
    # 檢查修復(fù)結(jié)果
    print("\n=== 修復(fù)驗(yàn)證 ===\n")
    print(f"管理員訪問: {system.check_user_access('admin_tom1', 'meta_sensitive_data', 'read')}")
    print(f"工程師訪問: {system.check_user_access('engineer_john', 'meta_sensitive_data', 'read')}")
    print("\n=== 經(jīng)驗(yàn)教訓(xùn) ===\n")
    print("1. 權(quán)限變更必須經(jīng)過風(fēng)險(xiǎn)評(píng)估")
    print("2. 自動(dòng)權(quán)限審計(jì)系統(tǒng)必須實(shí)時(shí)運(yùn)行")
    print("3. 敏感資產(chǎn)的權(quán)限變更需要多重審批")
    print("4. 定期進(jìn)行權(quán)限配置檢查")
def run_leak_prevention_demo():
    """運(yùn)行泄露預(yù)防演示"""
    print("\n=== 泄露預(yù)防措施演示 ===\n")
    system = AIPermissionSystem()
    # 創(chuàng)建敏感資產(chǎn)
    sensitive_asset = TrainingDataAsset('prevention_demo_data', 'critical')
    system.register_asset(sensitive_asset)
    print("預(yù)防措施1：權(quán)限變更風(fēng)險(xiǎn)評(píng)估")
    print("   - 每次權(quán)限變更前評(píng)估風(fēng)險(xiǎn)等級(jí)")
    print("   - 高風(fēng)險(xiǎn)變更需要額外審批")
    print("\n預(yù)防措施2：自動(dòng)化權(quán)限測(cè)試")
    print("   - 權(quán)限變更后自動(dòng)運(yùn)行測(cè)試")
    print("   - 確保權(quán)限矩陣保持一致")
    print("\n預(yù)防措施3：實(shí)時(shí)監(jiān)控和警報(bào)")
    print("   - 監(jiān)控異常訪問模式")
    print("   - 實(shí)時(shí)發(fā)送警報(bào)")
    print("\n預(yù)防措施4：定期審計(jì)")
    print("   - 每周自動(dòng)審計(jì)權(quán)限配置")
    print("   - 生成安全報(bào)告")
    print("\n預(yù)防措施5：災(zāi)難恢復(fù)演練")
    print("   - 定期模擬權(quán)限泄露事故")
    print("   - 測(cè)試應(yīng)急響應(yīng)流程")
if __name__ == "__main__":
    simulate_leak_scenario()
    run_leak_prevention_demo()

"""
AI權(quán)限系統(tǒng)性能優(yōu)化
"""
import time
from functools import lru_cache
class PermissionCache:
    """權(quán)限緩存優(yōu)化"""
    def __init__(self):
        self.cache = {}
        self.hit_count = 0
        self.miss_count = 0
    @lru_cache(maxsize=1000)
    def cached_check(self, user_role, asset_id, action):
        """緩存權(quán)限檢查"""
        # 模擬權(quán)限檢查
        key = f"{user_role}_{asset_id}_{action}"
        if key in self.cache:
            self.hit_count += 1
            return self.cache[key]
        else:
            self.miss_count += 1
            result = self._actual_check(user_role, asset_id, action)
            self.cache[key] = result
            return result
    def _actual_check(self, user_role, asset_id, action):
        """實(shí)際的權(quán)限檢查"""
        # 這里應(yīng)該是實(shí)際的權(quán)限檢查邏輯
        time.sleep(0.001)  # 模擬耗時(shí)
        return True  # 簡(jiǎn)化實(shí)現(xiàn)
    def get_cache_stats(self):
        """獲取緩存統(tǒng)計(jì)"""
        return {
            'total_cache_size': len(self.cache),
            'hit_count': self.hit_count,
            'miss_count': self.miss_count,
            'hit_rate': self.hit_count / (self.hit_count + self.miss_count) if (self.hit_count + self.miss_count) > 0 else 0
        }
class BulkPermissionProcessor:
    """批量權(quán)限處理優(yōu)化"""
    def process_bulk_checks(self, check_list):
        """批量處理權(quán)限檢查"""
        # 批量處理減少IO開銷
        results = []
        # 分組處理
        grouped_by_role = {}
        for check in check_list:
            role = check['user_role']
            if role not in grouped_by_role:
                grouped_by_role[role] = []
            grouped_by_role[role].append(check)
        # 為每個(gè)角色批量處理
        for role, checks in grouped_by_role.items():
            batch_results = self._process_role_batch(role, checks)
            results.extend(batch_results)
        return results
    def _process_role_batch(self, role, checks):
        """處理角色批量檢查"""
        # 這里可以實(shí)現(xiàn)批量數(shù)據(jù)庫(kù)查詢等優(yōu)化
        results = []
        for check in checks:
            results.append({
                'check': check,
                'result': True  # 簡(jiǎn)化實(shí)現(xiàn)
            })
        return results
if __name__ == "__main__":
    print("=== 性能優(yōu)化演示 ===\n")
    # 緩存優(yōu)化測(cè)試
    cache = PermissionCache()
    # 模擬多次權(quán)限檢查
    test_checks = [
        ('data_engineer', 'dataset_v1', 'read'),
        ('ml_engineer', 'model_v2', 'write'),
        ('data_engineer', 'dataset_v1', 'read'),  # 重復(fù)檢查
        ('ml_engineer', 'model_v2', 'write'),     # 重復(fù)檢查
    ]
    for check in test_checks:
        cache.cached_check(*check)
    stats = cache.get_cache_stats()
    print(f"緩存統(tǒng)計(jì): {stats}")
    print(f"命中率: {stats['hit_rate']:.2%}")
    # 批量處理測(cè)試
    bulk_processor = BulkPermissionProcessor()
    bulk_checks = [
        {'user_role': 'data_engineer', 'asset_id': 'asset1', 'action': 'read'},
        {'user_role': 'data_engineer', 'asset_id': 'asset2', 'action': 'write'},
        {'user_role': 'ml_engineer', 'asset_id': 'asset3', 'action': 'read'},
        {'user_role': 'ml_engineer', 'asset_id': 'asset4', 'action': 'write'},
    ]
    results = bulk_processor.process_bulk_checks(bulk_checks)
    print(f"\n批量處理結(jié)果數(shù): {len(results)}")

"""
AI驅(qū)動(dòng)的權(quán)限優(yōu)化
使用機(jī)器學(xué)習(xí)優(yōu)化權(quán)限配置
"""
import numpy as np
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
class PermissionPatternLearner:
    """權(quán)限模式學(xué)習(xí)器"""
    def __init__(self):
        self.access_patterns = []
        self.user_clusters = {}
        self.asset_clusters = {}
    def collect_access_data(self, access_logs):
        """收集訪問數(shù)據(jù)"""
        for log in access_logs:
            pattern = {
                'user_role': log['user_role'],
                'asset_type': log['asset_type'],
                'action': log['action'],
                'time_of_day': log['time_of_day'],
                'success_rate': log['success_rate']
            }
            self.access_patterns.append(pattern)
    def cluster_users_by_access_pattern(self):
        """根據(jù)訪問模式聚類用戶"""
        # 準(zhǔn)備數(shù)據(jù)
        feature_matrix = []
        for pattern in self.access_patterns:
            # 將訪問模式轉(zhuǎn)換為特征向量
            features = [
                pattern['time_of_day'],
                pattern['success_rate'],
                len(pattern['action']),
                hash(pattern['asset_type']) % 100
            ]
            feature_matrix.append(features)
        # 使用K-means聚類
        scaler = StandardScaler()
        scaled_features = scaler.fit_transform(feature_matrix)
        kmeans = KMeans(n_clusters=3, random_state=42)
        clusters = kmeans.fit_predict(scaled_features)
        # 將用戶分配到聚類
        for i, pattern in enumerate(self.access_patterns):
            cluster_id = clusters[i]
            user_role = pattern['user_role']
            if user_role not in self.user_clusters:
                self.user_clusters[user_role] = cluster_id
        return self.user_clusters
    def suggest_optimized_permissions(self):
        """建議優(yōu)化的權(quán)限配置"""
        optimized_permissions = {}
        for user_role, cluster_id in self.user_clusters.items():
            # 獲取該聚類的典型訪問模式
            cluster_patterns = []
            for i, pattern in enumerate(self.access_patterns):
                if pattern['user_role'] == user_role:
                    cluster_patterns.append(pattern)
            # 計(jì)算最優(yōu)權(quán)限
            suggested_permission = self._calculate_optimal_permission(cluster_patterns)
            optimized_permissions[user_role] = suggested_permission
        return optimized_permissions
    def _calculate_optimal_permission(self, patterns):
        """計(jì)算最優(yōu)權(quán)限"""
        # 基于模式計(jì)算權(quán)限
        permission = {}
        for pattern in patterns:
            asset_type = pattern['asset_type']
            action = pattern['action']
            success_rate = pattern['success_rate']
            # 如果成功率高于閾值，建議保留權(quán)限
            if success_rate > 0.7:
                permission_key = f"{asset_type}_{action}"
                permission[permission_key] = True
            else:
                permission[permission_key] = False
        return permission
    def predict_access_risk(self, new_access_pattern):
        """預(yù)測(cè)新訪問的風(fēng)險(xiǎn)"""
        # 基于歷史數(shù)據(jù)預(yù)測(cè)
        risk_score = 0
        # 檢查異常特征
        if new_access_pattern['time_of_day'] > 23 or new_access_pattern['time_of_day'] < 6:
            risk_score += 0.3
        if new_access_pattern['asset_type'] == 'critical' and new_access_pattern['action'] == 'write':
            risk_score += 0.4
        if new_access_pattern['user_role'] not in self.user_clusters:
            risk_score += 0.2
        return risk_score
class PermissionAutomation:
    """權(quán)限自動(dòng)化管理"""
    def auto_grant_permissions(self, user_role, access_history):
        """自動(dòng)授予權(quán)限"""
        # 分析歷史訪問模式
        frequently_accessed = self._analyze_frequency(access_history)
        # 自動(dòng)授予頻繁訪問的權(quán)限
        for asset_action in frequently_accessed:
            asset_type, action = asset_action.split('_')
            print(f"自動(dòng)授予 {user_role} {asset_type} 的 {action} 權(quán)限")
            # 這里應(yīng)該調(diào)用實(shí)際的權(quán)限授予函數(shù)
    def auto_revoke_permissions(self, user_role, inactive_periods):
        """自動(dòng)撤銷權(quán)限"""
        # 檢查長(zhǎng)期不使用的權(quán)限
        unused_permissions = self._find_unused_permissions(user_role, inactive_periods)
        for permission in unused_permissions:
            print(f"自動(dòng)撤銷 {user_role} 的 {permission} 權(quán)限")
            # 這里應(yīng)該調(diào)用實(shí)際的權(quán)限撤銷函數(shù)
    def _analyze_frequency(self, access_history):
        """分析訪問頻率"""
        frequency_map = {}
        for record in access_history:
            key = f"{record['asset_type']}_{record['action']}"
            if key not in frequency_map:
                frequency_map[key] = 0
            frequency_map[key] += 1
        # 返回高頻訪問項(xiàng)（超過閾值）
        return [k for k, v in frequency_map.items() if v > 10]
    def _find_unused_permissions(self, user_role, inactive_periods):
        """查找未使用的權(quán)限"""
        unused_permissions = []
        for permission, last_used in inactive_periods.items():
            # 如果超過30天未使用
            if last_used > 30:
                unused_permissions.append(permission)
        return unused_permissions
if __name__ == "__main__":
    print("=== AI驅(qū)動(dòng)的權(quán)限優(yōu)化演示 ===\n")
    # 權(quán)限模式學(xué)習(xí)器演示
    learner = PermissionPatternLearner()
    # 模擬一些訪問日志
    access_logs = [
        {'user_role': 'data_engineer', 'asset_type': 'training_data', 
         'action': 'read', 'time_of_day': 10, 'success_rate': 0.95},
        {'user_role': 'data_engineer', 'asset_type': 'training_data', 
         'action1': 'write', 'time_of_day': 15, 'success_rate': 0.85},
        {'user_role': 'ml_engineer', 'asset_type': 'model_parameters', 
         'action': 'read', 'time_of_day': 11, 'success_rate': 0.90},
        {'user_role': 'ml_engineer', 'asset_type': 'model_parameters', 
         'action': 'write', 'time_of_day': 13, 'success_rate': 0.75},
    ]
    learner.collect_access_data(access_logs)
    # 聚類用戶
    user_clusters = learner.cluster_users_by_access_pattern()
    print(f"用戶聚類結(jié)果: {user_clusters}")
    # 建議優(yōu)化權(quán)限
    optimized_permissions = learner.suggest_optimized_permissions()
    print(f"優(yōu)化權(quán)限建議: {optimized_permissions}")
    # 預(yù)測(cè)新訪問風(fēng)險(xiǎn)
    new_pattern = {'user_role': 'data_engineer', 'asset_type': 'critical', 
                   'action': 'write', 'time_of_day': 2, 'success_rate': 0.5}
    risk_score = learner.predict_access_risk(new_pattern)
    print(f"新訪問風(fēng)險(xiǎn)評(píng)分: {risk_score}")
    # 權(quán)限自動(dòng)化演示
    automation = PermissionAutomation()
    # 模擬訪問歷史
    user_history = [
        {'asset_type': 'training_data', 'action': 'read'},
        {'asset_type': 'training_data', 'action': 'write'},
        {'asset_type': 'training_data', 'action': 'read'},
        {'asset_type': 'processed_data', 'action': 'read'},
    ]
    automation.auto_grant_permissions('data_engineer', user_history)
    # 模擬未使用權(quán)限
    inactive_periods = {
        'training_data_delete': 45,
        'model_parameters_write':