The audit-evidence layer for AI-era software.
AnyAudit turns legacy applications into audit-ready systems — repository analysis, stack-native instrumentation, and a verifiable evidence trail for every decision, data mutation and AI invocation.
The software regulatory clock
Five obligations are converging on the same engineering surface: logging, human oversight and traceable decisions. None of them are met by policy documents alone.
By the time an auditor asks, the evidence chain needs to already exist — not be reconstructed after the fact.
How it works
- Repository analysis — map the audit surface: decision points, data mutations, AI invocations.
- Stack-native instrumentation — AspectJ/Spring AOP for Java, decorators/middleware for Python and Node, interceptors for .NET, an OpenTelemetry sidecar for deep legacy.
- Evidence rail — trace_id carried end-to-end, a tamper-evident hash-chain, confidence gating with mandatory human review of every generated change.
- Evidence pack — an arc42 audit annex mapping each mechanism to the regulation it supports: AI Act Art. 12/14, DORA, NIS2, GoBD.
Principles
Default-deny
Nothing is assumed audit-ready. Every mechanism has to earn its place in the evidence chain.
Human review, always
Every generated change is confidence-scored and reviewed by a human before it merges.
Audit-ready, organisationally owned
We build evidence infrastructure. Legal conformity remains an organisational responsibility.
Open standards first
OpenTelemetry, arc42 and W3C — evidence that outlives any single vendor.
Standards & stewardship
- Built on OpenTelemetry for distributed tracing and arc42 for architecture documentation.
- Aligned with the Semantic Trust Framework for evidence composition and confidence gating.
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Created and stewarded by AnyLAI — the evidence infrastructure company behind AnyDPP. anylai.eu