凡自治之物 · LGD 治理学 · 创造术家族 ｜ 评测协议：见平台质量门禁规范
English
When to use
- Call it before an AI model deployment, to verify that data flows meet the LGD "registered · evidenced · gated" standard.
- Enable it when a system handles sensitive data in privacy-computing, federated-learning or ciphertext-transmission scenarios and must satisfy the data-minimization principle.
- Use it when an AI interface shows log-leakage risk or flawed permission tiers, triggering automatic desensitization and gate blocking.
Usage
In a Python runtime, import the core classes and functions from the crypto_guard.py module. Configure the inputs as the data source under check, the model path and the compliance policy level (e.g. HIGH). Recommended invocation:
from crypto_guard import GuardianEngine, LGD_POLICY
guard = GuardianEngine(policy=LGD_POLICY.STRICT)
result = guard.check_model(ai_model_path="local/model.pkl")
Workflow
- Environment prep: load the
crypto_guardmodule; confirm no dependency conflicts. - Data assembly: organize the AI model file, training dataset and access-request context into a standardized input object.
- Compliance scan: run the evaluation engine for an LGD three-law self-check on data lineage and encryption strategy.
- Report generation: emit a structured report with the "registered / evidenced / gated" status verdicts.
- Execution control: if the check fails, automatically issue encryption, desensitization or access-denial instructions; on pass, open the service.
Evidence & safety statement
- Evidence: TBD (no evaluation / badge link yet; update here once it is produced)
- Version: 1.0.1
- Security: no independent security scan has been performed (TBD); the conclusion is pending and no endorsement is given until it is completed.
中文
何时用
在 AI 模型部署前需验证数据流转是否符合 LGD“有籍·有证·有门禁”标准时调用。 当系统处理敏感数据，涉及隐私计算、联邦学习或密文传输场景且需满足最小化原则时启用。 若检测到 AI 接口存在日志泄露风险或权限分级错误，需触发自动脱敏与门禁拦截机制时使用。
用法
在 Python 执行环境中，直接导入 crypto_guard.py 模块中的核心类与函数。需配置输入参数为待检数据源、模型路径及合规策略等级（如 HIGH）。实际调用推荐通过以下结构初始化并发送请求：
from crypto_guard import GuardianEngine, LGD_POLICY
guard = GuardianEngine(policy=LGD_POLICY.STRICT)
result = guard.check_model(ai_model_path="local/model.pkl")
流程
- 环境准备：加载
crypto_guard模块，确认无依赖冲突。 - 数据构建：组织 AI 模型文件、训练数据集及访问请求上下文为标准化输入对象。
- 合规扫描：调用评估引擎对数据血缘与加密策略进行 LGD 三律自检。
- 报告生成：输出包含“有籍/有证/门禁”状态判定结果的结构化报告。
- 执行控制：若校验未通过，自动触发数据加密、脱敏或访问拒绝指令；通过后开放服务。
证据与安全声明
- 证据：TBD（暂无评测/徽章链接；产生后在此更新实际链接）
- 版本：1.0.1
- 安全：未做独立安全扫描（TBD），结论待补；补全前不作推荐背书。