feat(WORKFLOWAI-PRO-WP-033) v1.0.0 — WorkflowAI Pro Enterprise AI Governance Platform Specification (2026-2030)

Comprehensive specification, system architecture, and implementation strategy
for WorkflowAI Pro and its AI governance capabilities for Fortune 500
enterprises across the 2026-2030 horizon.

Deliverables:
- data/workflowai-pro.json (59 KB): 12 modules, 7 architecture layers,
  10 OPA/Rego policies, 5 schemas, 7 code examples, 7 indices/KPIs,
  5 case studies, 59 planned API routes.
- gen-workflowai-pro.py (64 KB): idempotent JSON generator.
- gen-workflowai-pro-html.py (11 KB): HTML dashboard renderer.
- public/workflowai-pro.html (65 KB): interactive dashboard with TOC.
- server.js (+237 lines): adds 70+ /api/workflowai-pro/* endpoints.

Twelve modules:
M1  Platform Architecture - 7-layer reference (Presentation, API Gateway,
    Orchestration, Model & Tool Plane, Policy & Evidence, Data, Observability)
    with NFRs and deployment topologies.
M2  Enterprise AI Strategy & Roadmap 2026-2030 (H1-H4 horizons, capability
    model, RACI operating model).
M3  AGI/ASI Governance, Safety & Communication (T1-T6 capability tiers,
    six safety pillars, stakeholder channels, red-team program).
M4  Formal AI Safety & Global Governance Technical Reports (TR-01..TR-10
    catalogue + signed report pipeline).
M5  Prompt Lifecycle Features: history, templates, variable linking UI,
    test prompt area, template export/import & categories.
M6  Agent Simulation, Canary Deployment, EAIP Interop (EAIP-TPX/EVB/RMX),
    Containment Breach Suite (CB-01..CB-10 mapped to MITRE ATLAS).
M7  Cognitive Orchestrator Dashboard & Sentinel Compliance Engine, with
    PID-based alignment tuning (Kp/Ki/Kd, anti-windup, signed audit trail).
M8  9-category AI safety risk taxonomy (R1..R9), 6-layer governance
    framework (G1..G6), bias detection & mitigation tools.
M9  8 AI safety incident response playbooks (IR-01..IR-08) with SLAs,
    Art. 73 notification templates, 5 Whys, CAPA.
M10 Backend robustness: centralized error handling (RFC 7807),
    Zod validation, secure backend-routed Gemini proxy, enhanced
    RBAC+ABAC, cryptographic audit trails (Merkle-DAG, S3 Object Lock),
    Ed25519-signed active learning loop.
M11 Task dependency DAG visualization (D3.js/dagre), refined vision
    analysis outputs with uncertainty/ensemble, advanced PDF export
    styling (themes, watermarks, PDF/A-3).
M12 Implementation strategy: 5-phase 52-week adoption, change management,
    KPIs/OKRs (TTA, canary pass, MTTR, alignment deviation, evidence continuity).

Standards alignment: NIST AI RMF + Generative AI Profile, ISO/IEC 42001:2023,
ISO/IEC 23894, ISO/IEC 27001/27701, EU AI Act (Reg. 2024/1689),
GDPR, SR 11-7, SOC 2 Type II, FedRAMP Moderate (targeted 2028),
OWASP Top 10 for LLM Applications, MITRE ATLAS.

Validation: node -c server.js OK; all 12 module roots return HTTP 200;
containment scenarios CB-01..CB-10, OPA policies POL-01..POL-10, reports
TR-01..TR-10, incident playbooks IR-01..IR-08, section lookup (M5-S3),
and 404 handling all verified; HTML dashboard (67 KB) renders 12 modules.

Author: OneFineStarstuff