Kernel-Level Audit & Privacy: Building Resilient Audit Chains in the AI Coding Era
In the era of AI Coding, business code may be co-generated and modified by human developers, AI agents, or automated tools. This brings a new challenge:
While business logic is becoming increasingly easy to generate automatically, the audit chain must not become fragile as a result.
Traditional audit systems often rely on business code to actively record logs. However, in AI Coding scenarios, this approach carries clear risks:
- AI might forget to write audit logs;
- AI might accidentally disable logs;
- AI might generate code that bypasses audits;
- Business code might unintentionally record sensitive plain text;
- A
custom audit hookmight access raw data it shouldn't see; - Long strings, JSON payloads, or execution logs might cause audit log bloating or even out-of-memory (OOM) errors.
Therefore, TeaQL underwent a low-level refactoring to move auditing capabilities into the framework kernel rather than leaving them entirely to the business code. We established the following core principles:
Audit must be kernel-level.
Business code may enrich audit trails, but it cannot erase them.
Sensitive fields do not disappear; only their plain text disappears.
