data-flow-monitor-manager

data-flow-monitor-manager

跨租户数据流监控管理器。基于v8.0多租户任务编排方案§五SEC-2,实现跨租户数据流监控、数据流图构建、异常流告警、三级阈值(频率/数据量/敏感度)、Prometheus 4项安全指标导出、每日数据泄露扫描。

使用场景

1. 跨租户数据流实时监控: agency-portal-mcp数据流跟踪+rls-guard-mcp违规事件接入,记录每条数据流事件
2. 数据流图可视化: 自动构建租户间数据流向图(MCP调用链+API调用+DB查询),输出节点+边JSON
3. 异常数据流告警: 检测跨租户访问/异常大批量导出/非工作时间访问/敏感凭证访问,自动分级L1/L2/L3
4. 三级阈值动态调整: 频率(100/500次每小时)、数据量(1GB/10GB每日)、敏感度(normal/pii/credential)
5. Prometheus 4项安全指标: cross_tenant_access_total/data_export_bytes/anomaly_flow_alerts/leakage_events
6. 每日数据泄露扫描: 每日凌晨3点扫描所有租户访问日志,检测4种泄露模式,自动创建leakage_alerts
7. 泄露告警历史追溯: 查询leakage_alerts表追溯历史告警,支持按租户/类型/severity/状态过滤

工作流

1. 初始化(健康检查)

   • 执行: mcp__data-flow-monitor-mcp__healthcheck
   • 验证: 连接池+阈值配置+data_flow_events/leakage_alerts表+Docker+Gateway状态
   • 检查点: status==healthy或degraded(允许降级,不允许unhealthy)

2. 数据流事件记录(生产链路)

   • 执行: mcp__data-flow-monitor-mcp__record_data_flow(source_tenant, target_tenant, data_type, volume, flow_type, metadata)
   • 自动分级: 根据data_type/volume/frequency计算severity(L1/L2/L3)
   • 检查点: L3或跨租户L2自动创建leakage_alert

3. 数据流图查询

   • 执行: mcp__data-flow-monitor-mcp__get_data_flow_graph(tenant_id, hours, min_severity)
   • 输出: 节点(nodes)+边(edges),支持按租户/时间窗口/severity过滤
   • 检查点: 返回node_count和edge_count

4. 异常数据流检测

   • 执行: mcp__data-flow-monitor-mcp__detect_anomaly_flow(hours, tenant_id)
   • 检测4类: cross_tenant_access/abnormal_export/off_hours_access/sensitive_data_access
   • 检查点: 返回anomalies列表+severity_counts

5. Prometheus安全指标获取

   • 执行: mcp__data-flow-monitor-mcp__get_security_metrics(hours)
   • 4项指标: cross_tenant_access_total/data_export_bytes/anomaly_flow_alerts/leakage_events
   • 检查点: 返回metrics数组+tenant_distribution

6. 每日泄露扫描(Cron触发)

   • 执行: mcp__data-flow-monitor-mcp__run_leakage_scan(days, tenant_id)
   • 4种模式: cross_tenant_query/bulk_export/credential_access/off_hours_access
   • 检查点: L2/L3 finding自动创建leakage_alert(去重)

7. 泄露告警历史查询

   • 执行: mcp__data-flow-monitor-mcp__get_leakage_alerts(tenant_id, alert_type, severity, status, limit)
   • 检查点: 返回alerts列表+severity_summary+status_summary

8. 三级阈值动态调整

   • 执行: mcp__data-flow-monitor-mcp__set_flow_threshold(threshold_type, level, value, data_types)
   • 类型: frequency(频率)/volume(数据量)/sensitivity(敏感度)
   • 检查点: 返回old_value和new_value,配置原子写入

输入格式

输出格式

所有工具统一返回:

{
  "success": true,
  "data": { ... },
  "error": null,
  "code": "FLOW_RECORDED"
}

错误时:

{
  "success": false,
  "data": {},
  "error": "错误描述",
  "code": "ERROR_CODE"
}

异常处理

1. 连接池不可用: 返回 code=NO_DRIVER, error="psycopg2未安装" → 安装psycopg2-binary
2. 参数缺失: 返回 code=PARAMS_REQUIRED, error="source_tenant和target_tenant必填" → 补全参数
3. 标识符不合法: 返回 code=INVALID_PARAM → 检查tenant_id格式(字母/数字/下划线/连字符)
4. severity非法: 返回 code=INVALID_SEVERITY → 使用L1/L2/L3
5. 阈值类型非法: 返回 code=INVALID_TYPE → 使用frequency/volume/sensitivity
6. 配置文件错误: 返回 code=CONFIG_INVALID → 检查config/data_flow_thresholds.yaml
7. 健康检查失败: 返回 code=HEALTHCHECK_ERROR → 检查Docker/Gateway/PG连接/表存在
8. L3数据流阻断: record_data_flow返回blocked=true → 审查数据流是否合规

示例

示例1: 记录跨租户数据流

mcp__data-flow-monitor-mcp__record_data_flow(
  source_tenant="tenant_001",
  target_tenant="tenant_002",
  data_type="pii",
  volume=5242880,
  flow_type="api_call",
  metadata='{"endpoint":"/api/export"}'
)

输出:

{
  "success": true,
  "data": {
    "event_id": 101,
    "source_tenant": "tenant_001",
    "target_tenant": "tenant_002",
    "data_type": "pii",
    "volume": 5242880,
    "flow_type": "api_call",
    "severity": "L2",
    "blocked": false,
    "cross_tenant": true,
    "alert_id": 55,
    "timestamp": "2026-07-07 10:30:00+08:00"
  },
  "error": null,
  "code": "FLOW_RECORDED"
}

示例2: 获取24小时数据流图

mcp__data-flow-monitor-mcp__get_data_flow_graph(tenant_id="", hours=24, min_severity="L2")

输出:

{
  "success": true,
  "data": {
    "nodes": [{"id": "tenant_001", "label": "tenant_001"}, {"id": "tenant_002", "label": "tenant_002"}],
    "edges": [{"source": "tenant_001", "target": "tenant_002", "data_type": "pii", "severity": "L2", "event_count": 5, "total_volume": 26214400, "blocked": false}],
    "node_count": 2,
    "edge_count": 1,
    "hours": 24,
    "min_severity": "L2"
  },
  "error": null,
  "code": "GRAPH_OK"
}

示例3: 获取Prometheus 4项安全指标

mcp__data-flow-monitor-mcp__get_security_metrics(hours=24)

输出:

{
  "success": true,
  "data": {
    "metrics": [
      {"name": "cross_tenant_access_total", "type": "counter", "value": 42, "description": "跨租户数据访问总次数"},
      {"name": "data_export_bytes", "type": "counter", "value": 5368709120, "description": "数据导出字节数"},
      {"name": "anomaly_flow_alerts", "type": "counter", "value": 8, "description": "异常数据流告警数"},
      {"name": "leakage_events", "type": "gauge", "value": 2, "description": "数据泄露事件数(severity=L3)"}
    ],
    "hours": 24
  },
  "error": null,
  "code": "METRICS_OK"
}

来源标注

• v8.0多租户任务编排方案§五SEC-2: 跨租户数据流监控+泄露每日扫描设计
• 05文档§七风控安全: 三级阈值+安全合规要求
• 04部署文档: PG端口5432/MCP端口18809/路径d:\JueJin\mcps\data-flow-monitor-mcp\
• 统一入口规则R18: db_logger / atomic_write / health_checks
• config/data_flow_thresholds.yaml: 三级阈值(频率100/500, 数据量1GB/10GB, 敏感度normal/pii/credential)

历史记录