┌──────────────────────────────────────────────────────────────────────────────┐
│                        WhitePact  v1.2.6                                     │
│                                                                              │
│  ┌──────────────┐  ┌─────────────┐  ┌──────────────┐  ┌──────────────────┐  │
│  │ Governance   │  │ Trust Score │  │  Compliance  │  │  Guardrails      │  │
│  │ 5-way decide │  │ 6-dim A–F   │  │ NIST/EU/ISO  │  │  PII + Tox       │  │
│  └──────────────┘  └─────────────┘  └──────────────┘  └──────────────────┘  │
│  ┌──────────────┐  ┌─────────────┐  ┌──────────────┐  ┌──────────────────┐  │
│  │ Hallucination│  │ Cost Intel  │  │   Red Team   │  │  Drift Monitor   │  │
│  │ Self-consist.│  │ Route+Budget│  │ 10 attacks   │  │  Alerts+Trend    │  │
│  └──────────────┘  └─────────────┘  └──────────────┘  └──────────────────┘  │
│  ┌──────────────┐  ┌─────────────┐  ┌──────────────┐  ┌──────────────────┐  │
│  │ AI Passport  │  │  BiasBuster │  │ PrivacyLabel │  │  MCP Server      │  │
│  │ SHA-256 cert │  │ 6 probes+CI │  │  Federated   │  │  30 tools/HTTP   │  │
│  └──────────────┘  └─────────────┘  └──────────────┘  └──────────────────┘  │
│  ┌──────────────────────────────────────────────────────────────────────────┐ │
│  │   Governance Dashboard — FastAPI · Per-org rate limit · Alembic · OTEL  │ │
│  └──────────────────────────────────────────────────────────────────────────┘ │
└──────────────────────────────────────────────────────────────────────────────┘

What this solves

Every team deploying AI in production faces the same gap: no unified way to prove a model — or an autonomous agent's actions — is safe, fair, compliant, and accountable. Audits are manual, bias is discovered in production, compliance is a spreadsheet, an agent's tool calls go ungoverned, and nobody knows what the LLM bill will be next month.

WhitePact gives you one platform — a REST API, a Python SDK, an MCP server, and a live dashboard — that covers the full governance lifecycle:

Install

# Governance platform + REST API
pip install "rai-governance-platform[dashboard]"

# With PostgreSQL support
pip install "rai-governance-platform[dashboard,postgres]"

# With Redis + OpenTelemetry
pip install "rai-governance-platform[dashboard,redis,telemetry]"

# With LLM providers
pip install "rai-governance-platform[dashboard,openai,anthropic]"

# Everything
pip install "rai-governance-platform[all]"

The published PyPI package name (rai-governance-platform) and the import name (responsibleai) predate the WhitePact rename and are kept as-is — see MIGRATION_WHITEPACT_V2.md Section 3 for why an alias package (whitepact) was added instead of renaming the published package outright.

30-second quickstart

# Start the governance dashboard
pip install "rai-governance-platform[dashboard]"
uvicorn responsibleai.dashboard.app:app --port 8765

# Evaluate a model (no LLM key needed — supply your own scores)
curl -X POST http://localhost:8765/api/evaluate \
  -H "Content-Type: application/json" \
  -d '{
    "model_name": "gpt-4o",
    "provider": "openai",
    "fairness": 0.80,
    "privacy": 0.85,
    "security": 0.82,
    "robustness": 0.78,
    "compliance": 0.90,
    "authenticity": 0.88
  }'

{
  "trust_score": { "trust_score": 83.65, "grade": "B", "risk": "LOW" },
  "compliance": { "overall_score": 80.5, "eu_ai_act_tier": "limited_risk", "violations": 0 },
  "passport_id": "rai-a3f7c2b1",
  "passport_hash": "4d8e1f2a9c3b7e6d...",
  "drift_alert": null
}

Open http://localhost:8765 for the live dashboard and http://localhost:8765/api/docs for interactive API docs.

Governance core — five-way decisions, not a binary block/allow

src/responsibleai/governance/ (see SPEC.md Sections 4-8 for the full architecture contract) is a deterministic runtime authority sitting in front of agent tool calls:

from responsibleai.governance import WhitePactRuntimeGateway, ActionRequest, AuthorityContext

gateway = WhitePactRuntimeGateway()
result = gateway.evaluate(
    action=ActionRequest(tool_name="rai_scan", arguments={"text": "..."}),
    authority=AuthorityContext(org_id="acme", agent_id="agent-1"),
)
print(result.decision)  # GovernanceDecision.ALLOW | ALLOW_WITH_REDACTION | REQUIRE_APPROVAL | DENY | QUARANTINE

• Risk tiering (governance/risk.py) — every MCP tool is classified against a hardcoded, drift-tested table, not inferred at call time.

• Policy engine (governance/policy.py) — first-match-wins rules with ALLOW / DENY / REQUIRE_APPROVAL effects.

• Evidence (governance/evidence.py) — every decision is written to a per-org, hash-chained EvidenceRecord; verify_chain() detects tampering. Raw argument values are never stored, only field-name keys.

• Approval workflow (governance/approval.py) — REQUIRE_APPROVAL decisions queue a real, race-safe ApprovalRequest with a resolution API, not just a log line.

• Supply-chain scanner (src/responsibleai/supplychain/) — before an agent trusts a third-party MCP server or tool, SupplyChainScanner returns one of three explicit verdicts (VERIFIED_FACT / INFERRED_SIGNAL / UNKNOWN) — never a single opaque trust score — from typosquat detection, tool-description scanning, and known-incident cross-reference.

• Identity Bridge (integrations/identity_bridge.py) — maps Entra ID, Google Workspace, Okta, and AWS (Cognito / IAM Identity Center) ID token claims into IdentityContext, plus map_groups_to_authority() to turn IdP group membership into a granted-action-types AuthorityContext. See MACHINE_AUTHORITY_V1.md's Identity Bridge section for exactly what's verified (claim-shape correctness against each provider's public docs) versus not (live-tenant testing, Graph/Admin-SDK group-name resolution, AWS's non-JWT SigV4 path).

No governance decision is LLM-based; see DETERMINISTIC_VS_PROBABILISTIC.md for why.

See it end-to-end: examples/08_whitepact_enterprise_scenario.py runs a full scenario (an org onboarding an autonomous finance agent) through all eight machine-authority invariants — ceiling, delegation, attenuation, approval quorum, workflow composition, autonomy budget, memory firewall, evidence bundle — against real code, no API keys required:

python examples/08_whitepact_enterprise_scenario.py

MCP Server — govern every AI call from Claude Code, Claude Desktop, or any MCP client

The MCP (Model Context Protocol) server exposes WhitePact as 30 tools and 20 resources (10 canonical resource URIs, dual-advertised under both whitepact:// and rai:// schemes — see MIGRATION_WHITEPACT_V2.md) to any MCP-compatible client — Claude Code, Claude Desktop, Cursor, Windsurf, or your own agent runtime. Three transports are supported: stdio, Streamable HTTP (/mcp, current MCP spec), and legacy HTTP+SSE (/sse + /messages/, kept for older clients). When a team's client points at this server, every AI interaction is automatically governed — five-way governance decisions, trust scoring, guardrails, compliance checks (NIST AI RMF / EU AI Act / ISO 42001), bias evaluation, drift detection, cost tracking, and hash-chained audit evidence run on any call without code changes.

Setup

# Install
pip install "rai-governance-platform[dashboard,mcp]"

# Start the REST API (MCP tools call it internally)
RAI_DB_PATH=/var/lib/rai/governance.db \
RAI_API_KEYS=your-key-here \
uvicorn responsibleai.dashboard.app:app --host 127.0.0.1 --port 8765 &

# Add to Claude Code (~/.claude/claude_desktop_config.json or via /mcp)

{
  "mcpServers": {
    "whitepact": {
      "command": "whitepact-mcp",
      "env": {
        "RAI_API_URL": "http://localhost:8765",
        "RAI_API_KEY": "your-key-here"
      }
    }
  }
}

whitepact-mcp and responsibleai-mcp are the same entry point — see pyproject.toml's [project.scripts]; both will keep working, use whichever name you prefer.

Available tools (27)

Agent-framework integrations — LangChain, LangGraph, Google ADK

src/responsibleai/integrations/ wires rai_check_trust directly into three agent frameworks so an agent can be gated on a tool's public trust score before invoking it, not just log the call after the fact:

• LangChain (langchain_middleware.py) — TrustGateMiddleware, a wrap_tool_call middleware that blocks a call outright when its score is below threshold. Requires pip install "rai-governance-platform[langchain]".

• LangGraph (langgraph_gate.py) — make_trust_gate_node(), a node that pauses the graph with interrupt() for a human approve/reject decision on a below-threshold call, instead of a hard block. Requires pip install "rai-governance-platform[langgraph]".

• Google ADK (adk_toolset.py) — build_stdio_toolset() / build_http_toolset(), thin factories over ADK's McpToolset, which auto-discovers this project's MCP server's tools with no custom glue code. Requires pip install "rai-governance-platform[adk]".

All three, or any subset, install via pip install "rai-governance-platform[agent-frameworks]". See GAME_CHANGER_BUILD_PLAN.md Phase B for the reasoning behind each.

Available resources (20)

10 canonical resources, each advertised under both the whitepact:// and rai:// URI schemes (dual scheme is additive — see MIGRATION_WHITEPACT_V2.md; the table below shows the canonical URI):

MCP directory listings

WhitePact is listed and queryable today on real MCP directories — not aspirational, all verified live:

• Official MCP Registry — server.json at the repository root (schema 2025-12-11, listing version 1.2.3) is published as io.github.Guruprasath-Annadurai/whitepact, confirmed queryable at registry.modelcontextprotocol.io. Advertises both the PyPI/stdio package (whitepact-mcp, self-hosted, free, unrestricted) and a remotes entry pointing at the hosted Streamable HTTP and SSE transports (whitepact-mcp-http.onrender.com) — a one-click remote connector, not just an installable package.

• Antigravity CLI plugin — plugins/whitepact/ at the repository root follows the official Antigravity plugin manifest format, connecting to the same hosted Streamable HTTP transport via serverUrl. No official Antigravity plugin directory exists yet, so this is distributed directly from the repo — see plugins/whitepact/README.md.

• Smithery — listed as guruprasathannadurai-official/whitepact, 30 tools and 20 resources discovered against the hosted Streamable HTTP transport (whitepact-mcp-http.onrender.com/mcp, a separate Render service from the main dashboard). This deployment has no OAuth authorization server configured — only static Bearer API keys — so a public, unauthenticated /.well-known/mcp/server-card.json serves the same live TOOL_DEFS/RESOURCE_DEFS the server itself advertises, for directories whose scanners can't complete a live authenticated crawl.

See compliance/MCP_DISTRIBUTION_GUIDE.md for the full distribution plan, including directories not yet submitted to.

Platform integrations

WhitePact connects to the major AI platforms as one MCP server through standards-compliant clients — no per-platform forks, no per-platform governance logic. See docs/integrations/ for the canonical compatibility matrix (PLATFORM_COMPATIBILITY.md), per-platform setup docs (GitHub Copilot, Microsoft Copilot, Claude, Grok, Gemini, Amazon Q, AWS Bedrock AgentCore, Mistral Le Chat, Cursor), and FOUNDER_ACTIONS.md for what still needs a human. Run python scripts/integration_smoke.py for a live protocol-level preflight against the hosted endpoint.

Python SDK

Trust scoring

from responsibleai import TrustScoreEngine, PassportGenerator

engine = TrustScoreEngine()
score = engine.compute(
    fairness=0.80, privacy=0.85, security=0.82,
    robustness=0.78, compliance=0.90, authenticity=0.88,
)
print(f"{score.overall:.1f} / 100  Grade: {score.grade}  Risk: {score.risk_level}")
# → 83.7 / 100  Grade: B  Risk: LOW

passport = PassportGenerator().generate(
    model_name="gpt-4o", provider="openai", trust_score=score,
    compliance_summary={"overall": 80.5},
)
print(passport.passport_id)
passport.export_html("passport.html")

Guardrails — block PII before it reaches a log

from responsibleai import GuardrailsEngine

guardrails = GuardrailsEngine()
result = guardrails.scan("Customer SSN is 123-45-6789, email: alice@company.com")

print(result.is_blocked)      # True
print(result.pii_count)       # 2
print(result.redacted_text)   # "Customer SSN is [SSN], email: [EMAIL]"

Hallucination detection

from responsibleai import HallucinationDetector

detector = HallucinationDetector()
result = detector.analyze(
    "AI will replace all human jobs by 2025.",
    candidates=[
        "AI will automate some repetitive tasks.",
        "AI creates new job categories alongside displacing others.",
    ],
)
print(f"Risk: {result.hallucination_risk:.2f}  Level: {result.risk_level}")

Compliance — NIST AI RMF, EU AI Act, ISO 42001

from responsibleai import ComplianceEngine

engine = ComplianceEngine()
report = engine.evaluate(
    fairness_score=0.80, privacy_score=0.85,
    security_score=0.82, robustness_score=0.78,
    compliance_maturity=0.90, use_case="credit_scoring",
)
print(f"Score: {report.compliance_score * 100:.1f}%")
print(f"EU AI Act tier: {report.eu_ai_act_tier.value}")  # high_risk

Red team simulation

from responsibleai import RedTeamSimulator

simulator = RedTeamSimulator()
report = simulator.run_all()

print(f"Security score: {report.security_score:.1f}/100")
print(f"Vulnerabilities: {len(report.vulnerabilities)}")
for v in report.critical_vulnerabilities:
    print(f"  [{v['cwe_id']}] {v['name']}")

Cost intelligence

from responsibleai import CostTracker, ModelRouter, TokenUsage, BudgetPolicy

tracker = CostTracker(db_path="~/.responsibleai/data.db",
                      policy=BudgetPolicy(monthly_limit_usd=500.0))
usage = TokenUsage.create(
    provider="openai", model="gpt-4o",
    input_tokens=2000, output_tokens=800, team="product",
)
record = tracker.record(usage)
print(f"This call: ${record.total_cost:.4f}")
print(f"Month to date: ${tracker.total_cost(30):.2f}")

router = ModelRouter()
decision = router.route("Classify this email as spam or not spam", "balanced")
print(f"Recommended: {decision.recommended_model}  ${decision.estimated_cost_per_1k:.4f}/1k tokens")

Trust drift monitoring

from responsibleai import TrustScoreEngine, TrustDriftMonitor

monitor = TrustDriftMonitor(db_path=":memory:", alert_threshold=5.0)
engine = TrustScoreEngine()

for fairness in [0.90, 0.88, 0.85, 0.72]:
    score = engine.compute(fairness=fairness, privacy=0.85, security=0.80,
                           robustness=0.80, compliance=0.85, authenticity=0.85)
    alert = monitor.record("gpt-4o", "openai", score)
    if alert:
        print(f"Drift alert! {alert.severity}: {alert.delta:.1f} pt drop")

Governance Dashboard

A production FastAPI application with a dark-mode SPA. A live instance is hosted at whitepact.com.

# Development (auth off, SQLite in-memory)
RAI_AUTH_ENABLED=false uvicorn responsibleai.dashboard.app:app --port 8765

# Production (auth + persistent DB)
RAI_API_KEYS=your-key-here \
RAI_DB_PATH=/data/responsibleai.db \
uvicorn responsibleai.dashboard.app:app --host 0.0.0.0 --port 8765 --workers 4

# Docker
docker compose up -d

REST API endpoints

Interactive docs at /api/docs. Public leaderboard page at /leaderboard — see compliance/LEADERBOARD_METHODOLOGY.md for the published scoring methodology and scripts/run_leaderboard_eval.py to run evaluations. Open Trust Index standard and passport verification at /verify/{id} — see compliance/TRUST_INDEX_SPEC.md. Free, zero-signup self-assessment at /assess; browse every assessed model/tool at /registry. /llms.txt points AI crawlers/answer engines at these as canonical sources — see GAME_CHANGER_STRATEGY.md for why.

Production features

Database migrations (Alembic)

Schema changes are managed with Alembic. Run alembic history for the current, authoritative migration count and table list — this number changes frequently enough that a hardcoded count here goes stale fast; the command itself is the source of truth.

# Upgrade to latest schema
RAI_DB_PATH=/var/lib/rai/governance.db alembic upgrade head

# PostgreSQL
RAI_DB_URL=postgresql://user:pass@host:5432/responsibleai alembic upgrade head

# Show migration history
alembic history

# Generate a new migration after changing engine.py
alembic revision --autogenerate -m "add_new_column"

All migrations use render_as_batch=True so they run on both SQLite and PostgreSQL without changes.

Webhook notifications

Register an endpoint and receive signed events when governance thresholds fire.

# Register a Slack webhook
curl -X POST http://localhost:8765/api/webhooks \
  -H "Authorization: Bearer your-key" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "ops-slack",
    "url": "https://hooks.slack.com/services/...",
    "events": ["drift_alert", "budget_exceeded", "guardrail_triggered"],
    "provider": "slack",
    "secret": "hmac-secret-for-signature-verification",
    "max_retries": 5
  }'

Deliveries are persisted to the database. If the server restarts during a retry cycle, the background worker picks up where it left off on next boot. Retry schedule: 1 s → 5 s → 30 s → 2 min → 10 min.

Verify payloads with the X-RAI-Signature-256: sha256=<hex> header.

Docker

git clone https://github.com/Guruprasath-Annadurai/Whitepact.git
cd Whitepact

python3 -c "import secrets; print(secrets.token_urlsafe(32))"

cp .env.example .env
# Edit .env — set RAI_API_KEYS

docker compose up -d
# Dashboard: http://localhost:8765
# API docs:  http://localhost:8765/api/docs

PostgreSQL + Redis (horizontal scaling)

# .env
RAI_DATABASE_URL=postgresql://rai:secret@db-host:5432/responsibleai
RAI_REDIS_URL=redis://redis-host:6379/0
RAI_OTEL_ENDPOINT=http://otel-collector:4318

pip install "rai-governance-platform[dashboard,postgres,redis,telemetry]"

# Run migrations before first start
RAI_DB_URL=postgresql://rai:secret@db-host:5432/responsibleai alembic upgrade head

The async database layer uses SQLAlchemy with connection pooling (pool_size=10, max_overflow=20, pool_pre_ping=True). Rate limiting switches to Redis-backed storage when RAI_REDIS_URL is set.

BiasBuster — bias evaluation in CI

# Fail CI when demographic bias exceeds threshold
biasbuster run \
  --provider openai --model gpt-4o \
  --probes gender-bias,racial-bias,cultural-bias \
  --threshold 0.20 \
  --output report --format html

from biasbuster import BiasBusterRunner, GenderBiasProbe, RacialBiasProbe
from biasbuster.providers import OpenAIProvider
import asyncio

async def main():
    provider = OpenAIProvider(api_key="sk-...", model="gpt-4o")
    runner = BiasBusterRunner(provider=provider)
    suite = await runner.run([
        GenderBiasProbe(threshold=0.20),
        RacialBiasProbe(threshold=0.20),
    ])
    print(f"Score: {suite.overall_score:.4f}  {'PASSED' if suite.passed else 'FAILED'}")

asyncio.run(main())

Available probes: gender-bias, racial-bias, age-bias, religious-bias, occupational-stereotype, cultural-bias

Scoring: TF-IDF cosine divergence + length asymmetry + VADER sentiment divergence, 95% bootstrap confidence intervals, intersectional co-failure amplification (×1.15).

PrivacyLabel — on-device federated labeling

from privacylabel import FederatedClient, FedAvgAggregator

client = FederatedClient(
    node_id="hospital-node-01",
    provider=MyProvider(),
    epsilon_per_round=0.1,
    total_epsilon=1.0,
    delta=1e-6,
    gradient_clip=1.0,
)
# Raw data stays on disk — only privatised gradients leave the device
summary = await client.train_round("data/local_records.jsonl")
print(f"Privacy budget used: ε={summary.privacy_spent['spent_epsilon']:.3f}")

Implements Laplace, Gaussian, Exponential, and DP-SGD mechanisms. Byzantine-robust aggregation via Weiszfeld geometric median.

GitHub Actions — bias gate in CI

- name: Bias evaluation
  run: |
    pip install "rai-governance-platform[openai]"
    biasbuster run \
      --provider openai --model gpt-4o-mini \
      --probes gender-bias,racial-bias,cultural-bias \
      --threshold 0.20
  env:
    OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}

Environment variables

Dual-prefixed WHITEPACT_* equivalents for these are also read where MIGRATION_WHITEPACT_V2.md documents them — the RAI_* names remain the primary, always-supported form.

Development

git clone https://github.com/Guruprasath-Annadurai/Whitepact.git
cd Whitepact

python3 -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"

# Full test suite (run it to see the current test count and coverage —
# see CONTRIBUTING.md's Running Tests section for why no number is
# hardcoded here)
pytest

# Dashboard tests only
RAI_DB_PATH=:memory: RAI_AUTH_ENABLED=false pytest tests/test_dashboard_api.py

# Webhook persistence tests
pytest tests/test_webhook_persistence.py

# MCP server tests
pytest tests/test_mcp_server.py

# Lint + type check
ruff check src/ tests/
mypy src/responsibleai src/biasbuster

Roadmap

See ROADMAP.md for the canonical NOW/NEXT/LATER plan. The list below is a historical, version-by-version changelog summary kept for reference.

• v0.1 — BiasBuster: gender probe, 4 providers, CLI, CI integration

• v0.2 — Racial / age / religious / occupational probes, HTML reporter, PrivacyLabel federated DP

• v0.3 — Cultural bias, intersectional analysis, DeepfakeDetector ensemble

• v0.4 — Cost Intelligence (CostTracker, ModelRouter, 16-model pricing), Trust Drift Monitor

• v0.5 — Governance Dashboard (FastAPI), Trust Score, AI Passport, Guardrails, Hallucination, Compliance, Red Team, CI/CD, Docker, SLA

• v0.6 — Async PostgreSQL (SQLAlchemy), Redis rate limiting, OpenTelemetry APM, LLM integration tests

• v1.0 — WebSocket drift alerts, Prometheus endpoint, multi-tenant RBAC, org management API

• v1.1 — MCP server (10 tools, 5 resources), audit log API, red team API, billing API, Alembic migrations, per-org rate limiting, DB-persisted webhook retry queue

• v1.2 — Public Leaderboard, Trust Index/Passports + embeddable badges, AI Incident Database, TOTP MFA, expanded field encryption, DB-persisted webhooks, full dashboard UI rebuild, white-label branding, a genuinely live hosted instance — see CHANGELOG.md for the full list

• WhitePact migration (1.2.0 → 1.2.2) — governance decision core, MCP Streamable HTTP + OAuth/OIDC, risk tiering + policy engine, hash-chained evidence, approval workflow, multi-approver quorum + delegation chains, upstream MCP tool discovery, MCP trust/supply-chain scanner, HA Helm deployment, supply chain security (SBOM/provenance), release engineering, open source governance, live listings on the official MCP Registry and Smithery — see MIGRATION_WHITEPACT_V2.md for the full phase-by-phase log and what's still not done

• v2.0 onward — see VERSION_ROADMAP.md for the phase-by-phase plan through v6.0

• Strategic direction — GAME_CHANGER_STRATEGY.md lays out an infrastructure-first bet (free public trust registry, an agent-native trust-check primitive, AI-answer-engine citability) as an alternative to the enterprise-SaaS path, with GAME_CHANGER_BUILD_PLAN.md breaking it into concrete engineering phases against the current codebase

Security & Open Source Assurance

The official OpenSSF/OSPS BadgeApp project currently records OpenSSF Best Practices Silver and OSPS Baseline Level 1. They are voluntary project evidence, not an independent audit, penetration test, SOC 2, or ISO certification. Current technical and claim boundaries are maintained in WHITEPACT_TRUST_STATUS.md and PUBLIC_TRUST_CLAIMS.md.

Release consumers can review the signed-tag evidence, release process, security policy, SLSA evidence boundary, and consumer verification guide. The reusable trusted-builder pipeline is present on main. Release v1.2.6 completed that path: its wheel and sdist were reproduced, hashed, attested, independently verified in the publish job, published to PyPI without rebuilding, hash-matched to PyPI, and attached to the GitHub Release with the CycloneDX SBOM. Independent consumer verification was repeated on 2026-08-31. The release-specific evidence is assessed as satisfying SLSA v1.2 Build L3; SLSA is a conformance framework, not a certification or a guarantee that an artifact is secure.

Further reading

• SPEC.md — the current architecture contract

• MACHINE_AUTHORITY_PROBLEM.md — the problem the v3 authority-layer work answers

• MACHINE_AUTHORITY_V1.md — inventory of the eight core machine-authority invariants (Delegation Graph, Autonomy Budget, Memory Firewall, Evidence Bundle, and more)

• ENFORCEMENT_BOUNDARY.md — precisely where each invariant's authority stops: inline enforcement vs. voluntary chokepoint

• LEGACY_TO_MACHINE_AUTHORITY_MAP.md — mapping RBAC/OAuth/IAM concepts onto their WhitePact equivalents, for readers coming from traditional access control

• MIGRATION_WHITEPACT_V2.md — phase-by-phase migration log, what's done and what's explicitly not

• DEFINITION_OF_DONE.md — closing report: what's real today, what isn't, verifiable

• SECURITY_THREAT_MODEL.md — current security threat and attack-surface model

• DETERMINISTIC_VS_PROBABILISTIC.md — why governance decisions are deterministic

• SLA.md, ENTERPRISE_SECURITY.md, SECURITY.md — enterprise/security posture, stated honestly

• compliance/SOC2_ALTERNATIVE_PATH.md — real, free, independently verifiable trust signals for now; the honest path to a real SOC 2 when there's budget for one

• docs/ACCESSIBILITY.md, docs/INTERNATIONALIZATION.md — WCAG2AA accessibility approach and the dashboard's i18n architecture, both with real automated CI gates

• compliance/PROJECT_CONTINUITY_PLAN.md — the access/recovery checklist a second person would need if the founder became unavailable; stated honestly as a plan, not proof of bus-factor redundancy (no second person holds this access yet)

License

MIT — see LICENSE.