TraceVault: AI Code Governance Platform

1. Capture — Full Session Tracing

Record every AI interaction with full fidelity. TraceVault hooks into AI coding agents and automatically captures session transcripts, token breakdowns (input/output/cache per model), every tool call invocation, file modifications with diffs, and cost estimates. Secrets and credentials are redacted before storage.

Nothing to configure — once initialized, capture is automatic and invisible.

2. Enforce — Policy Engine

Keep AI within bounds. Define rules per-repository that are evaluated on every push:

• Model allowlists — restrict which AI models can be used
• Sensitive path protection — flag AI edits to critical paths (/payments/, /auth/, /crypto/)
• Required tool calls — mandate security scanners or code review tools
• AI percentage thresholds — warn when AI-authored code exceeds a limit

• Token budgets — cap token usage or cost per session

  Policies can either block the push (exit non-zero) or warn. Fail-closed by default: if the server is unreachable, the push is blocked.

3. Audit — Cryptographically Signed Chain of Events

Every trace pushed to the server is transformed into a tamper-proof record:

1. Hashed — SHA-256 digest of the canonical record
2. Chained — each hash links to the previous, forming a verifiable chain
3. Signed — Ed25519 digital signature proves authenticity
4. Sealed — timestamp marks when the record was finalized

Records are append-only — no updates, no deletes. Corrections create amendment records referencing the original. The entire chain can be verified at any time to prove nothing was altered or reordered.

Built-in compliance modes for SOX (7-year retention), PCI-DSS (1-year, WORM-equivalent), and SR 11-7 (model risk management for banks). RBAC with five roles — including a dedicated Auditor role with read-only access to all traces and the audit log.

4. Analyze — Usage Analytics

Understand how AI is used across your team:

• Token usage trends over time, per model, per author
• Model distribution — which models are used most and where
• Cost tracking — estimated cost breakdown by model and team member
• Cache savings — how much prompt caching saves
• AI attribution — what percentage of your codebase is AI-generated
• Author activity — commits, tokens, and cost per developer

All available through the web dashboard with filterable time ranges and drill-down views.

5. Code — Stories & Documentation

See exactly what AI wrote, line by line. The code browser overlays AI attribution on your source files — highlighting which lines were AI-generated, which function or class they belong to (via tree-sitter scope detection), and linking back to the session that produced them.

Story generation turns raw traces into human-readable narratives: why the AI chose a particular pattern, what alternatives were considered, and what the developer's intent was. Auto-generated Architecture Decision Records give new team members full context without asking anyone.