AI governance platform that detects semantic drift between human-authored architectural intent and AI-generated code in real time. Enforces EU AI Act Articles 12, 13, 14 & 50 directly in the execution path — not as a process document.
Built by Vinita Silaparasetty, AI Governance Engineer, Aevoxis Solutions
Normal operating state. Drift within threshold, status SOVEREIGN, all governance indicators green.
Article 14 enforcement end-to-end: drift crossed threshold, human submitted justification, Warden Agent returned APPROVED with Intent Alignment Score 91/100.
| Requirement | Implementation |
|---|---|
| Article 14: Human Oversight | Justification Gate blocks execution until human approval |
| Article 12: Record Keeping | SHA-256-verified audit trail on every governance decision |
| Article 13: Transparency | Real-time drift coefficient visible to all stakeholders |
| Article 50: Disclosure | System identifies itself as AI-governed at every interaction point |
| Feature | Description |
|---|---|
| Drift Detection | Polls a live drift index every 3 seconds. Real-time bar chart coloured by severity. |
| Justification Gate | When drift crosses the sovereign threshold, an Article 14-compliant gate appears. No action proceeds without human sign-off. |
| Warden Agent | Submits justification to Amazon Nova Pro and returns a structured reasoning trace with an Intent Alignment Score. |
| Audit Trail | Every governance event logged to .kiro/audit/last_sync.audit with a verification hash — downloadable from the dashboard. |
| Spec Vault | Human intent specs in .kiro/steering/. The Warden cross-references every decision against these files. |
Requirements: Python ≥ 3.12, Node.js ≥ 18, npm ≥ 9
git clone https://github.com/VinitaSilaparasetty/spec-drift_chronometer.git
cd spec-drift_chronometer
chmod +x dev.sh
DEMO_MODE=true ./dev.shOpen http://localhost:3000. No AWS credentials needed.
Demo flow (~45 seconds):
- Drift rises through MONITORING into CRITICAL_DRIFT
- The Justification Gate modal appears
- Submit a justification → Warden returns APPROVED or REJECTED with reasoning trace
- Click Run Audit → Download Audit to export the Article 12 audit trail
The Spec-Drift Chronometer wraps around AI systems you already have running — it does not replace the pipeline, it governs it. The integration below uses a LangChain RAG chatbot as an example. The same pattern applies to any LangChain-compatible chain, LangGraph graph, or agent.
cd integrations/langchain_rag
cp .env.example .env # add your OPENAI_API_KEY
pip install -r requirements.txt
python check.py # verify setup before running
python rag_chatbot.pyfrom warden_client import WardenClient
from warden_callback import WardenCallbackHandler
warden = WardenClient(base_url="https://your-warden-api.example.com")
handler = WardenCallbackHandler(warden, dashboard_url="https://your-dashboard.example.com")
# This single line wires EU AI Act Article 14 governance into your existing chain
rag_chain = (your_existing_chain).with_config(callbacks=[handler])When the gate triggers, chain execution is blocked and the operator is directed to the governance dashboard to submit a justification. The Warden Agent evaluates it and returns APPROVED or REJECTED. The gate clears only on approval.
Full integration code, a LangGraph example, and setup verification are in integrations/langchain_rag/.
Having trouble connecting? See the Troubleshooting section of the integration guide.
┌──────────────────────────────────────────────────────┐
│ Browser │
│ Next.js Dashboard (port 3000) │
│ ├── DriftDashboard — real-time chart + logs │
│ ├── JustificationGate — Article 14 modal │
│ └── GovernanceActions — audit buttons │
└────────────────────┬─────────────────────────────────┘
│ HTTP (NEXT_PUBLIC_API_URL)
┌────────────────────▼─────────────────────────────────┐
│ FastAPI Warden Engine (port 8000) │
│ ├── GET /drift — live drift index │
│ ├── GET /gate/status — gate state │
│ ├── POST /gate/submit — invoke Warden Agent │
│ ├── POST /audit — generate audit file │
│ └── GET /download-audit — serve audit file │
└────────────────────┬─────────────────────────────────┘
│ boto3 (PRODUCTION only)
┌────────────────────▼─────────────────────────────────┐
│ AWS eu-central-1 │
│ ├── Amazon Bedrock — nova-pro-v1:0 reasoning │
│ └── DynamoDB — Intent Ledger (optional) │
└──────────────────────────────────────────────────────┘
Spec Vault (.kiro/steering/) — human-authored intent the Warden cross-references on every decision:
| File | Purpose |
|---|---|
governance.md |
Warden persona and negotiation protocol |
tech.md |
Technology constraints |
human-intent-specs.md |
Architect declarations (INTENT-001 … INTENT-006) |
spec.json |
Machine-readable thresholds and model config |
cp .env.example .env
# Set AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_REGION=eu-central-1, DEMO_MODE=false
DEMO_MODE=false ./dev.shIAM permissions required:
bedrock:InvokeModel (amazon.nova-pro-v1:0 and amazon.nova-lite-v1:0)
dynamodb:PutItem (optional — for durable audit trail)
dynamodb:GetItem
AWS Lambda deployment is supported via the included Dockerfile and Procfile. See .env.example for all configuration options.
| Variable | Default | Description |
|---|---|---|
DEMO_MODE |
true |
false enables live AWS Bedrock |
DRIFT_THRESHOLD |
0.0075 |
Drift value that triggers the gate |
NEXT_PUBLIC_API_URL |
http://localhost:8000 |
Backend URL for the frontend |
AWS_REGION |
eu-central-1 |
Frankfurt — required for EU data sovereignty |
WARDEN_LLM |
(unset) | Override the justification evaluator: gemini, huggingface, or mistral |
GEMINI_API_KEY |
— | Required when WARDEN_LLM=gemini |
HF_API_KEY |
— | Required when WARDEN_LLM=huggingface |
MISTRAL_API_KEY |
— | Required when WARDEN_LLM=mistral |
When WARDEN_LLM is unset the Warden defaults to Amazon Nova Pro via Bedrock in production and the built-in mock in demo mode.
- Frontend: Next.js 16 / React 19 / Tailwind 4 — Cloudflare Pages
- Backend: FastAPI / Python 3.12 / Mangum — Render
- AI: Amazon Bedrock (Nova Pro — justification analysis, Nova Lite — drift scoring)
- Governance: Spec Vault with real semantic git diff analysis
╔══════════════════════════════════════════════════════════════╗
║ SPEC-DRIFT CHRONOMETER — SOVEREIGN AUDIT TRAIL ║
╚══════════════════════════════════════════════════════════════╝
Timestamp: 2026-06-14 15:24:15 UTC
Drift Index: 0.0082 | Threshold: 0.0075 | Gate: RESOLVED
Spec Hash: bf40efdc39297d64 | Run Hash: cdfa7ff9a941820f
── GOVERNANCE COMPLIANCE ──────────────────────────────────────
EU AI Act Article 14 (Human Oversight): VERIFIED
EU AI Act Article 12 (Record Keeping): VERIFIED
Sovereign Region: eu-central-1 (Frankfurt)
── JUSTIFICATION GATE RECORD ──────────────────────────────────
Decision: APPROVED
Justification: Migrating auth layer to OAuth2 to satisfy GDPR Article 7
compliance requirements signed off by legal team on 2026-06-10.
══════════════════════════════════════════════════════════════
Downloadable directly from the dashboard. A weak justification scores 29/100 and is REJECTED — the gate is not a rubber stamp.
The test_research/ folder contains the empirical test suite used to generate data for an IEEE Software paper on EU AI Act compliance failure modes. It includes two test runners:
run_tests.py— three-phase test: real drift measurement across git commits, justification gate evaluation across nine quality levels (WEAK / MEDIUM / STRONG), and audit trail generationrun_failure_modes.py— twelve structured failure mode and gap tests covering Articles 9, 12, 13, 14, 17, and 50
Results across all test runs are in test_research/results/. The headline finding from the failure mode suite: a 10-line addition to the spec vault reduced drift detection for an entire vocabulary domain from 0.0113 to 0.0044, crossing the gate threshold in reverse and silencing detection permanently — a gap not visible from reading Article 13(3b) alone.
The Warden Engine supports three real LLM backends for gate evaluation, controlled by the WARDEN_LLM environment variable:
WARDEN_LLM value |
Model used | API endpoint |
|---|---|---|
mistral |
mistral-small-2506 |
api.mistral.ai |
gemini |
gemini-1.5-flash |
Google AI Studio |
huggingface |
meta-llama/Llama-3.1-8B-Instruct:auto |
router.huggingface.co |
When WARDEN_LLM is unset, the system uses Amazon Nova Pro via Bedrock in production mode (DEMO_MODE=false) and the built-in mock evaluator in demo mode (DEMO_MODE=true). No external API key is required for demo mode.
For IEEE paper reproducibility: All empirical test data in test_research/results/ was generated using WARDEN_LLM=mistral with model mistral-small-2506 and temperature=0. The model is pinned by version alias (not latest) to ensure deterministic, reproducible results. See test_research/README.md for the full reproduction procedure.
To run the justification gate tests against a live backend:
source venv/bin/activate
DEMO_MODE=false WARDEN_LLM=mistral MISTRAL_API_KEY=your-key \
python -m uvicorn backend.main:app --port 8000 &
cd test_research
pip install -r requirements.txt
python run_tests.py --llm mistralLicensed under AGPL-3.0. For commercial licensing or enterprise deployment, contact info@aevoxis.de