1. Executive Summary
1.1 Framework Purpose
The Enterprise Software Governance Platform is an end-to-end lifecycle governance framework for financial services institutions regulated under OCC, Federal Reserve, FDIC, FINRA, or SEC jurisdiction. The platform governs every instance in which an institution acquires, builds, enables, or renews software — from idea inception through post-deployment monitoring and retirement.
The platform integrates four domain disciplines into a single, orchestrated BPMN workflow:
| Domain | Governance Scope |
|---|---|
| TPRM | Full six-stage vendor lifecycle per OCC Bulletin 2023-17, with proportionate due diligence, ongoing monitoring, and termination management calibrated to vendor risk tier |
| AI Governance (MRM) | SR 11-7-aligned architecture for all AI-enabled components, including concurrent risk classification at intake, independent validation pathways, model inventory governance, and mandatory observability telemetry |
| PDLC | Engineering-led build pathway with risk-gated milestones, observability designed from Day 1, and SR 11-7 AI Governance checklist enforcement |
| SLA Management | ISO 8601 timer events, real-time queue visibility, escalation rules, and bottleneck detection across all phases |
1.2 Key Outcomes
2. Product Vision
Deterministic-first, AI-augmented. Every material routing and approval decision is governed by explicit, auditable rules (DMN). Agents and AI accelerate intake, enrichment, classification, and generation — but never make unmonitored, unexplained decisions that affect business or regulatory outcomes. All activities — human, automated, or agent-enabled — are orchestrated and observable within a single BPMN workflow.
2.1 Foundational Principles
| Principle | Description | Primary Benefit |
|---|---|---|
| Deterministic Knowledge Capture | Front-loads information harvesting to compress downstream cycle times | Eliminates 40-60% of downstream rework |
| Automation-First Design | Identifies every opportunity for AI-driven acceleration; automation is evidence-driven after 10+ manual executions with consistent outcomes | ~60% average automation; rising to 75%+ for intake and routing |
| Governance-by-Design | No control gaps between TPRM, AI governance, legal, risk, compliance, security, and regulatory obligations | Zero control gaps at regulatory examination |
2.2 Task Type Classification
| Designation | Definition | BPMN Representation |
|---|---|---|
| A — Automated | Fully automated execution with no human intervention. Inputs and outputs are deterministic. All inputs/outputs logged to decision audit trail. SLA timers enforced. | Service Task or Business Rules Task |
| DA — Deterministic Agent-Enabled | Agent executes using deterministic knowledge bases and DMN rules. Outputs are reproducible. Knowledge base version logged. Decision provenance captured. | Service Task with agent annotation |
| H — Human-in-the-Loop | Human judgment required. Human is accountable. Reviewer role logged (not personal identity). Override rationale captured (minimum 50 characters). SLA clock enforced. | User Task with role-based assignment |
2.3 Seven-Phase Architecture
3. Target Personas
Source: Spec Section 4
Business Requestor
- Task Type
- H
- Authority
- Submits requests; no approval authority
- Core Needs
- Self-service intake portal with real-time status visibility; guided elicitation; SLA clock visibility
- Pain Points
- Unclear process; requests lost in email; repeated information requests
Product Owner
- Task Type
- H
- Authority
- Information gating; backlog entry approval
- Core Needs
- Centralized view of in-flight requests; automated PRD generation; deduplication alerts
- Pain Points
- Manual PRD drafting; duplicate requests; cross-portfolio dependency blindness
Enterprise Architect
- Task Type
- H
- Authority
- Technical acceptance; PoC gate sign-off
- Core Needs
- Integration dependency map from CMDB; standardized HLDD template; PoC evaluation rubric
- Pain Points
- Ad hoc review requests; no visibility into downstream integration impacts
Third-Party Risk Manager
- Task Type
- H DA
- Authority
- Vendor risk tier assignment; RAE approval
- Core Needs
- Automated vendor risk tiering via DMN-06; Trust Center integration; agent swarm for evidence analysis
- Pain Points
- Manual SOC 2 review (35-60 min/report); evidence delays; no continuous monitoring
AI / Model Risk Governance Lead
- Task Type
- H DA
- Authority
- Model inventory entry; AI use approval; escalation to exec sponsor
- Core Needs
- Automated AI risk tier classification via DMN-09; model card pre-population; drift monitoring dashboard
- Pain Points
- AI deployments discovered post-facto; inconsistent model documentation; no systematic drift monitoring
Legal Counsel
- Task Type
- H
- Authority
- Contract approval; legal sign-off
- Core Needs
- Legal Knowledge Graph; AI-assisted redline review; mandatory clause validation
- Pain Points
- Manual clause selection; no systematic clause version tracking; ad hoc regulatory lookup
Portfolio Governance Council
- Task Type
- H
- Authority
- Go/No-Go; Buy/Build; budget release authority
- Core Needs
- Consolidated portfolio view with DMN-04 and DMN-05 pre-computed recommendations; human override logging
- Pain Points
- Incomplete submissions requiring re-review; override rationale not captured for audit
Internal Audit
- Task Type
- H
- Authority
- Audit findings; control gap reporting
- Core Needs
- Read-only WORM audit log access; pre-built regulatory exam package; AI model inventory with validation evidence
- Pain Points
- Manual evidence collection for exams; AI deployments not systematically inventoried
4. Problem Statement
4.1 Root Cause Analysis
| Root Cause | Description | Impact |
|---|---|---|
| Fragmented Process Architecture | TPRM, AI governance, and procurement managed through disconnected processes with no single orchestrating workflow | Handoff failures; duplicate reviews; governance gaps at function seams |
| Reactive, Serial Information Collection | Information required in later phases only requested after earlier phases complete | Primary driver of 90-120 day cycle times |
| Informal Decision Logic | Material governance decisions made through informal email reviews and ad hoc spreadsheet scoring | Inconsistent outcomes; unexplainable decisions; regulatory defensibility gaps |
| Duplicate Spend and Shadow IT | No continuously-maintained Software Asset Registry | Routine duplicate procurement; shadow IT discovered only at regulatory examination |
4.2 Quantified Impact
| Problem | Current State | Target State |
|---|---|---|
| End-to-end cycle time (standard risk) | 90-120 days industry average | 29-45 days (68-75% reduction) |
| Manual governance decision logic | Informal, inconsistent | 15 formalized DMN tables, 100% rule coverage |
| AI model inventory coverage | Partial, ad hoc | 100% SR 11-7 tiered and monitored |
| Duplicate procurement rate | 20-30% estimated | <5% with registry-driven reuse gate |
| Regulatory exam preparation | 5-10 days manual assembly | 24-hour automated retrieval |
| Shadow IT detection | Reactive (audit-discovered) | Continuous SSO/spend detection |
5. Feature Requirements by Phase
Phase 0: Software Asset Intelligence
BPMN: processes/phase-0-asset-intelligence/
The system SHALL maintain a continuously-updated Software Registry as the single authoritative source of all software assets.
The Software Registry SHALL expose a real-time API to the Phase 1 Intake Bot and Phase 2 Routing Engine. Every intake query SHALL include an automated Software Registry lookup before human review begins. A
The system SHALL perform automated nightly reconciliation across all data sources with delta alerting for new, changed, or expired assets. A
Registry refresh SLAs: any change to a production system SHALL be reflected within 24 hours; new contract entries within 48 hours of signature.
Software assets with utilization below 40% of licensed seats SHALL generate a reuse recommendation workflow. DA
SSO-identified applications not registered in the procurement system SHALL trigger an automated intake workflow for shadow IT triage. A
Registry-to-Intake Query Interface
| Match Type | Similarity | Action | Type |
|---|---|---|---|
| Exact match | >90% | Redirect to ITSM service catalog; close intake | A |
| Partial match | 70-90% | Product Owner notified; reuse assessment within 5 business days | H |
| In-flight backlog match | Any | Requestor offered merge or proceed with documented rationale | H |
| Retired/decommissioned match | Any | Decommission rationale surfaced and included in intake record | DA |
Phase 1: Conversational AI Intake
BPMN: processes/phase-1-needs-assessment/intake-risk-classification.bpmn
The system SHALL implement a structured conversational AI intake mechanism — not a static form. The bot SHALL enforce completeness through progressive elicitation and branch question logic based on prior answers. A + DA
The system SHALL implement a Mandatory Capability Reuse Gate (DMN-15) that fires automatically after the Intake Bot has collected sufficient capability definition data, before any downstream design or build activity proceeds. DA
The system SHALL implement a concurrent, automated risk assessment engine classifying each request across five dimensions simultaneously during intake. A + DA
Upon completion of intake, reuse gate, and risk classification, the system SHALL automatically generate a structured Product Requirements Document (PRD). A + DA
Upon Product Owner approval of the PRD, the system SHALL automatically push artifacts (Epic, Feature Stories, Git branch) to the designated development project management tool. A
Five-Dimension Concurrent Risk Classification
| Dimension | Classification Logic | Downstream Impact |
|---|---|---|
| 1 — Model Risk Tier (SR 11-7) | Tier 1 (High): credit/capital/fraud/compliance decisions; Tier 2 (Moderate): material operational; Tier 3 (Low): productivity tools | Observability tier; validation requirements; exec sponsor |
| 2 — Data Privacy & Residency | PII/NPI triggers GDPR/CCPA/GLB review, mandatory DPA, data residency verification | DPA requirement; privacy review; deletion/portability provisions |
| 3 — Cybersecurity Risk Score | Composite across external connectivity, authentication model, data sensitivity, network segmentation | Security routing depth; pen test requirements |
| 4 — Operational & Concentration Risk | Critical activity designation per OCC; concentration risk per BCBS d577 | Vendor tier; BCP requirements; executive oversight |
| 5 — Regulatory Classification | Automated tagging of applicable frameworks based on business function, data types, AI flags, distribution channel | Compliance governance routing; contract terms; observability retention |
Phase 2: AI Routing Engine
The system SHALL implement a three-tier routing architecture: (1) Deterministic DMN rules A; (2) Deterministic Agent-Enabled Semantic Classification DA; (3) Human Review Escalation when confidence <85% H.
Upon pathway assignment, the system SHALL start the SLA clock. Pathway assignment, composite score, confidence level, and DMN rule ID SHALL be logged to the decision audit trail.
Composite Scoring Model
| Dimension | Weight | Key Inputs |
|---|---|---|
| Strategic Value | 25% | Business value quantification; strategic alignment; user population; SLA criticality |
| Risk Score | 30% | AI risk tier; data classification; cybersecurity score; regulatory flags; concentration risk |
| Complexity Score | 20% | Integration count; data domain breadth; build vs. buy complexity estimate |
| Portfolio Fit | 15% | Backlog capacity; resource availability; strategic theme alignment; duplicate probability |
| Urgency | 10% | Go-live date delta; regulatory/contractual deadline; business event driver |
Phase 3: Product Management Review
The system SHALL apply DMN-02 (Information Completeness Gate) to govern whether the request proceeds to portfolio or returns for enrichment. All conditions must be satisfied for Proceed to fire. A
The system SHALL implement structured loop-back to requestor via the conversational bot for any identified gaps. All communication SHALL flow through the workflow system — not email.
Phase 4: Portfolio Governance
The system SHALL apply DMN-04 (Go/No-Go Viability) with PRIORITY hit policy, where a single veto condition overrides all positive signals. The Council retains full human override authority, which SHALL be logged with reviewer role and rationale (minimum 50 characters). H
The system SHALL apply DMN-05 (Buy vs. Build Analysis) to all GO decisions, evaluated across: market solution availability, 5-year TCO, IP differentiation, build complexity, and strategic fit.
Phase 5A: PDLC Build Pathway
BPMN: processes/phase-5a-pdlc-build/
| Step | Activity | Type | Owner |
|---|---|---|---|
| 1 | Technology Plan Integration | A / H | Program Management |
| 2 | Initial Risk Evaluation: integration dependencies mapped; InfoSec assessment; data flow diagram | H / DA | Enterprise Architect, AI/MRM Governance |
| 3 | Initial Requirements and Estimates: refine PRD; story points; dependencies; Definition of Done | H | Product Owner, Engineering |
| 4 | HLDD: system context; data flow; API contracts; security controls; observability design | H | Enterprise Architect |
| 5 | Observability and Audit Telemetry Design: event logging schema; drift monitoring metrics; retention | H / DA | Enterprise Architect, AI/MRM Governance |
| 6 | Proof of Concept — DMN-10 gate | H | Enterprise Architect, Cybersecurity Lead |
| 7 | Requirement Refinement: update PRD with PoC learnings; log new risks | H | Product Owner |
| 8 | Technology and Risk Evaluation Gate — DMN-11 | H / DA | Architect, AI/MRM Governance, Compliance |
| 9 | UAT / Pilot — DMN-12 | H | Business Requestor, Product Owner |
| 10 | Go-to-Market: release plan; runbook; observability dashboard activated | H / A | Product Owner, Program Management |
Phase 5B: TPRM Buy Pathway
BPMN: processes/phase-5b-tprm-procurement/
Vendor Risk Tier Classification (DMN-06)
| Tier | Description | Monitoring | DD Depth |
|---|---|---|---|
| Tier 1 — Critical | Critical activity; high data sensitivity (PII/NPI); high concentration risk | Monthly; exec dashboard; onsite audit | Full (financial, security, BCP, legal, AI, 4th-party) |
| Tier 2 — Elevated | Non-critical; high data sensitivity (PII) | Quarterly; annual re-assessment | Enhanced; SOC 2 Type II required |
| Tier 3 — Standard | Moderate data sensitivity; low concentration | Semi-annual; annual review | Standard; SOC 2 Type II or equivalent |
| Tier 4 — Low | Low / public data; no concentration risk | Annual; biennial attestation | Abbreviated; attestation-based |
The system SHALL integrate with vendor Trust Centers (Vanta, TrustCloud, Drata) via API to auto-ingest SOC 2 reports, penetration test results, and compliance attestations at intake. A
The system SHALL deploy an AI Agent Swarm: Investigator Agent (<2 min/report), Compliance Agent (<5 min), Checker Agent (<5 min). Disagreements between Investigator and Checker SHALL be escalated to human review with full context.
The system SHALL implement a Legal Knowledge Graph as a deterministic knowledge base governing all clause selection for RFP and MSA construction. All auto-generated clauses SHALL require Legal Counsel review and approval before RFP issuance. DA / H
The system SHALL implement continuous automated risk triggers: OFAC screening, adverse news monitoring, dark web scanning, and financial stability monitoring for Tier 1-2 vendors. A
Phase 6: Post-Deployment Observability
BPMN: processes/phase-6-observability/
The system SHALL implement mandatory observability designed in Phase 5A and activated at Go-to-Market, per SR 11-7, SEC Cybersecurity Disclosure Rule (2023), FINRA Rule 4511, and SEC Rule 17a-4.
Every AI-assisted, agent-enabled, or automated decision SHALL generate an immutable event record written to an append-only audit log with: decision_id (UUID), request_id, process_phase, decision_type, rule_id or model_version, knowledge_base_version, input_hash (SHA-256), output, confidence_score, human_reviewer_role, override_rationale (min 50 chars), timestamp_utc (ISO 8601), session_id. A
Data retention: regulated financial decisions 7 years minimum (FINRA 4511; SEC 17a-4); operational decisions 3 years minimum. All logs SHALL use WORM-compliant storage.
6. Decision Model Requirements
Source: Spec Section 16
All fifteen DMN decision tables SHALL be implemented as Business Rules Tasks in Camunda Platform 7. Each SHALL be independently versioned, audited, and governed.
| DMN ID | Decision Name | Phase | Hit Policy | Primary Inputs | Primary Outputs |
|---|---|---|---|---|---|
| DMN-01 | AI Routing and Pathway Assignment | 2 | UNIQUE | Channel; existing solution; AI tier; composite score; vendor status | Pathway; fast-track flag; required reviews |
| DMN-02 | Information Completeness Gate | 3 | ANY | Value quantified; data class; integration list; reg flags; PO approval | Proceed / Return (with gap identified) |
| DMN-03 | Duplicate, Merge, and Reuse Decision | 3 | UNIQUE | Registry match score; backlog match score; match type | Close / Reuse assessment / Merge offer / Proceed |
| DMN-04 | Go/No-Go Viability | 4 | PRIORITY | Composite score; strategic alignment; regulatory risk; budget; resources | Priority GO / Standard GO / Defer / NO-GO variants |
| DMN-05 | Buy vs. Build Analysis | 4 | UNIQUE | Market solution; 5yr TCO; IP differentiation; complexity; strategic fit | Buy / Build / Hybrid / Defer |
| DMN-06 | Vendor Risk Tier Assignment | 5B | PRIORITY | Critical activity; data sensitivity; concentration; financial stability; reg exposure | Tier 1–4; TPRM intensity |
| DMN-07 | Vendor Selection and RAE Gate | 5B | UNIQUE | Vendor response count; pilot outcome; RAE findings; AI Gov status | Proceed / Conditional / Restart / No-Go |
| DMN-08 | Funding Confirmation Gate | 5B | UNIQUE | Finance engagement; budget; FP&A completion; budget year | Funded / Deferred / No Funding / Escalate |
| DMN-09 | AI Risk Tier Classification | 1 | PRIORITY | Decision materiality; credit/capital impact; model complexity; data sensitivity | Tier 1 / Tier 2 / Tier 3 |
| DMN-10 | Proof of Concept Gate | 5A | UNIQUE | PoC rubric score; Architecture sign-off; CyberSec sign-off | Proceed / Refine and retry / Reject |
| DMN-11 | Technology and Risk Evaluation Gate | 5A | ANY | Completeness; AI Gov checklist; Architecture; CyberSec; Compliance | Proceed to UAT / Hold (domain) / Return |
| DMN-12 | Observability Tier Assignment | 5A | PRIORITY | AI risk tier; regulatory classification; decision materiality; user population | Log schema tier; retention; monitoring cadence; alerts |
| DMN-13 | Fast-Track Eligibility | 2 | UNIQUE | Internal channel; AI flag; production flag; sensitivity; vendor pre-approval | Fast-track / Standard |
| DMN-14 | TPRM Monitoring Frequency | 5B→Ongoing | UNIQUE | Vendor risk tier; contract value; service criticality; prior outcomes | Monitoring cadence per activity |
| DMN-15 | Capability Reuse Gate | 1 | UNIQUE | Registry match; functional fit; license availability; retired match | Reuse-Redirect / Reuse Assessment / Evaluate Expansion / Proceed / Surface Rationale |
DMN Governance Requirements
DMN tables SHALL be updated within 60 days of any material regulatory guidance change that affects their input conditions or output actions.
All DMN rule invocations SHALL be logged to the decision audit trail with: DMN ID, version, rule number matched, input values, output value, and timestamp.
Human overrides of DMN decisions SHALL be logged with: overriding role (not personal identity), rationale (minimum 50 characters), timestamp, and original DMN output.
7. Agent Framework Requirements
Source: Spec Section 17
Agents are permitted within the workflow only when: (1) outputs are deterministic and reproducible, (2) they use deterministic knowledge bases, (3) decision provenance is logged, and (4) they follow DMN rules. Any agent output below confidence threshold (≥85%) triggers automatic escalation to the appropriate human role.
| Agent | Phase(s) | Knowledge Base | DMN Governed By | Human Escalation Trigger |
|---|---|---|---|---|
| Intake Bot | 1 | Software Registry; Regulatory KB; Data Governance KB | DMN-09, DMN-15 | Ambiguous capability; unresolvable field validation |
| Routing Engine | 2 | Software Registry; Historical routing outcomes | DMN-01, DMN-13 | Confidence < 85% |
| Compliance Analysis Agent | 1, 3 | Regulatory Requirements KB; Data Governance KB | DMN-02 | Novel regulatory scenario; cross-border ambiguity |
| Legal Clause Assembly Agent | 5B | Legal Knowledge Graph | Graph traversal rules | Conflicting clauses; novel contract type |
| Contract Redline Agent | 5B | Legal Knowledge Graph; Precedent outcomes | Institutional standards rules | Non-standard deviation > threshold |
| Knowledge Staging Agent | All phases | All knowledge bases (write access) | Validation rules per KB schema | Schema validation failure |
| Monitoring and Alerting Agent | 6 | Performance baselines; drift thresholds | DMN-12, DMN-14 | Alert threshold breach |
AI Agent Swarm Performance Requirements
The AI Agent Swarm SHALL process evidence at: <2 minutes per SOC 2 report (Investigator); <5 minutes per compliance cross-reference cycle (Compliance Agent); <5 minutes per checker validation cycle. Full Tier 1 evidence evaluation SHALL complete within 4 hours of evidence package submission.
Agent disagreements between Investigator and Checker SHALL be escalated immediately to the relevant human role with full context and a structured resolution SLA of 2 business days.
8. Regulatory Requirements
Source: Spec Appendix A
| Regulation | Issuer | Phases | Key Requirements |
|---|---|---|---|
| OCC Bulletin 2023-17 | OCC / Fed / FDIC | 5B; 6 | Five-stage TPRM lifecycle; risk-tiered DD; critical activity designation; sub-contractor oversight; ongoing monitoring; termination planning |
| SR 11-7 MRM | Fed / OCC | 1; 5A; 6 | Model risk inventory; independent validation; documentation; performance monitoring; drift detection; Board-level reporting |
| NIST AI RMF 1.0 | NIST | 1; 5A | GOVERN, MAP, MEASURE, MANAGE functions; bias/fairness; transparency; accountability; trustworthiness |
| SEC Cybersecurity Disclosure Rule | SEC | 2; 5B; 6 | Material incident disclosure; annual cybersecurity risk management; third-party risk as material factor |
| FINRA Rules 3110 / 4511 | FINRA | 6 | Supervision of technology; books and records; 3-year minimum retention |
| SEC Rule 17a-4 | SEC | 6 | WORM-compliant storage; 7-year retention; regulatory access within 24 hours |
| BCBS d577 | BIS | 5B | Concentration risk; supervisory cooperation; termination/BCP planning; sub-contractor chain oversight |
| NIST SP 1800-5 | NIST | 0 | ITAM for financial services; continuous discovery; license compliance; vulnerability integration |
| ISO/IEC 27001:2022 | ISO | 5A; 5B; 6 | ISMS requirements; supplier security; access control; audit logging; cryptographic controls |
| EU AI Act | EU | 1; 5A | High-risk AI registration; fundamental rights assessment; technical documentation; transparency |
| GDPR / CCPA / GLB | EU / CA / US Fed | 1; 5B | Lawful basis; DPA requirements; data subject rights; cross-border transfer; breach notification |
| FCRA / Regulation B | CFPB / Fed | 5A (Tier 1) | Adverse action notices; disparate impact prohibition; explainability for credit-adjacent AI |
| DORA | EU | 5B; 6 | ICT risk management; incident reporting; digital operational resilience testing; third-party oversight |
9. Non-Functional Requirements
9.1 Audit Trail
The system SHALL maintain an immutable, WORM-compliant decision audit log for all decisions (DMN, agent, human). Log entries SHALL be append-only and tamper-evident (SHA-256 input hashing).
Retention: regulated financial decisions 7 years minimum (FINRA Rule 4511; SEC Rule 17a-4); operational decisions 3 years minimum, 7 years if subject to regulatory examination.
9.2 SLA Enforcement
| KPI / SLA | Target |
|---|---|
| Intake-to-routing completion | ≤ 2 business days |
| Completeness rate at first submission | ≥ 85% |
| Duplicate/reuse detection rate | ≥ 30% resolved via Registry before portfolio entry |
| Go/No-Go decision cycle | ≤ 5 business days |
| Buy pathway: RFP-to-vendor-selection (Tier 3-4) | ≤ 30 days |
| Buy pathway: RFP-to-vendor-selection (Tier 1-2) | ≤ 60 days |
| TPRM due diligence completion | 100% tiered and DD-completed before contract |
| AI Gov checklist completion (Tier 1/2) | 100% with approved checklist before production |
| Vendor monitoring SLA adherence | ≥ 95% on schedule per DMN-14 |
| Regulatory audit readiness | Full log retrievable within 24 hours |
| Shadow IT detection-to-triage | ≤ 5 business days |
| Fast-track cycle time | ≤ 5 business days |
9.3 Scalability
The system SHALL support institutional-scale operation with 10,000+ software assets in the Software Registry without performance degradation.
The Software Registry query interface SHALL return semantic search results within 5 seconds for any intake session.
9.4 Deterministic Output
Running the same DMN decision table with the same inputs SHALL produce identical outputs regardless of when the evaluation occurs (assuming the same DMN table version).
Every AI-generated artifact (PRD, story, clause selection) SHALL be clearly distinguished from human-validated content. AI-generated content SHALL require explicit human approval before governing any downstream action.
10. Architecture
Source: Spec Sections 6, 16-18
10.1 Technology Stack
| Layer | Technology | Constraint |
|---|---|---|
| Process Engine | Camunda Platform 7 | Use camunda: namespace; candidateGroups; historyTimeToLive |
| Process Modeling | BPMN 2.0 | All phases as sub-processes in master process |
| Decision Engine | DMN 1.3 | 15 externalized Business Rules Tasks; independently versioned |
| Audit Storage | WORM-compliant store | Append-only; 7-year retention; examiner access within 24 hours |
| Backlog Integration | Jira / Azure DevOps | Bidirectional sync; Epic and Story push on PRD approval |
| Source Control | GitHub Enterprise / Bitbucket / Azure DevOps | SBOM generation; NVD linkage; feature branch automation |
10.2 Knowledge Base Architecture
| Knowledge Base | Content | Update Mechanism | Retention / SLA |
|---|---|---|---|
| Software Registry | All software assets: purchased, built, contracted, in-flight, retired | Automated nightly reconciliation; manual corrections | 24hr production; 48hr contracts |
| Regulatory Requirements KB | All applicable regulations, clauses, citations, and control requirements | Agent monitors feeds; Compliance validates | 60-day update SLA on material changes |
| Data Governance KB | Data classification rules, residency requirements, retention obligations | Structured updates via validation workflow | Annual review |
| Legal Knowledge Graph | Legal clauses (versioned), contract types, regulatory mappings, precedent outcomes | Agent ingests modifications; Legal Counsel approves | Annual full review |
| AI Governance KB | Model cards, validation evidence, bias testing results, drift thresholds | Knowledge Staging Agent captures; AI/MRM Governance validates | Per-model lifecycle |
| Decision Audit Log | All decision events with full provenance | Automated append-only write | WORM; 7-year; Internal Audit read access |
| Vendor Intelligence KB | Vendor risk assessments, due diligence results, monitoring outcomes, incident history | Agent captures monitoring data; TPRM validates | Per-vendor lifecycle |
11. Data Model
Source: Spec Sections 7-15, 20
11.1 Core Entities
| Entity | Key Attributes | Source |
|---|---|---|
| Software Asset | asset_id (UUID), asset_name, asset_type, vendor_id, business_unit, data_classification, environment, operational_status, license_entitlements, seat_utilization, renewal_date, cmdb_ci_id, sbom_reference, reuse_recommendation_flag | Phase 0 registry feeds |
| Intake Request | request_id (UUID), requestor_role, pathway_assigned, composite_score, risk_tier, ai_risk_tier, registry_match_score, reuse_decision, prd_document_id, jira_epic_id, git_branch_reference, sla_clock_start, current_phase, status | Phase 1 intake bot |
| Decision Event (Audit Log) | decision_id (UUID, immutable), request_id, process_phase, decision_type, rule_id_or_model_version, knowledge_base_version, input_hash (SHA-256), output, confidence_score, human_reviewer_role, override_rationale (min 50 chars), timestamp_utc (ISO 8601), session_id | All phases — WORM append-only |
| Vendor | vendor_id (UUID), vendor_name, risk_tier (1-4), critical_activity_flag, data_sensitivity_level, concentration_risk_flag, financial_stability_rating, monitoring_cadence_sla, contract_renewal_date, trust_center_url, sbom_reference | Phase 5B onboarding |
| Risk Assessment Evaluation (RAE) | rae_id (UUID), vendor_id, request_id, assessment_date, security_score, financial_score, operational_score, composite_rae_score, risk_tier_recommendation, critical_findings_count, approval_status | Phase 5B due diligence |
| AI Model | model_id (UUID), model_name, model_type, model_version, vendor_id, risk_tier (1-3), deployment_date, next_validation_date, model_card_document_id, drift_monitoring_active_flag, bias_testing_last_date, executive_sponsor_signoff_flag, sr_11_7_tier | Phase 5A / Phase 1 classification |
12. Integration Requirements
| Integration | System(s) | Key Capabilities Required | Phase |
|---|---|---|---|
| Project Management | Jira / Azure DevOps | Bidirectional Epic/Story sync; PRD document link; Git branch creation; status synchronization | 1, 5A |
| Source Control | GitHub Enterprise / Bitbucket / Azure DevOps | Feature branch creation from Epic ID; SBOM generation trigger; NVD linkage | 0, 1, 5A |
| Trust Centers | Vanta, TrustCloud, Drata | API ingest of SOC 2 reports, pen tests, financial statements, insurance certificates, compliance attestations at intake trigger | 1, 5B |
| CMDB | Institution CMDB | Pull all CI records into Software Registry; push asset registrations within 24 hours; trigger intake for shadow IT | 0, all |
| ITSM | Institution ITSM / Service Catalog | Redirect exact-match intakes (>90%) to self-service fulfillment; create fast-track service requests | 0, 2 |
| SAM | SAM Tool | License entitlements, seat utilization, renewal dates, contract values, compliance status | 0 |
| SSO / Spend Analytics | Identity Provider, Expense Management | Shadow IT detection: SSO usage cross-referenced with spend; automated intake trigger | 0 |
| Security Monitoring | BitSight, SecurityScorecard | Real-time external attack surface scoring; automated alerts on posture degradation | 5B, 6 |
| GRC / IRM Platform | ProcessUnity / OneTrust / Aravo | Vendor inventory and risk tier record of record; BPMN engine drives workflow steps | 5B, 6 |
13. Success Metrics
Source: Spec Sections 25-27
13.1 Cycle Time Compression Waterfall
13.2 Automation Targets by Phase
| Phase | Automation % Target | Key Automated Activities |
|---|---|---|
| Phase 1 — Intake | 75% | Intake bot elicitation; registry lookup; risk classification; PRD generation; Jira push |
| Phase 2 — Routing | 85%+ | DMN-01 pathway assignment; composite scoring; SLA clock start |
| Phase 3 — PM Review | 50% | DMN-02 completeness gate; DMN-03 duplicate detection; structured loop-backs |
| Phase 4 — Portfolio | 40% | DMN-04/DMN-05 pre-computation; package assembly |
| Phase 5A — Build | 55% | Git branching; sprint scheduling; compliance checkpoints; observability activation |
| Phase 5B — Buy | 60% | Risk tiering; Trust Center ingestion; agent swarm; monitoring triggers |
| Phase 6 — Observability | 65% | Drift monitoring; bias detection; SLA compliance; exam package assembly |
| Average across all phases | ~60% | — |
14. Implementation Roadmap
Source: Spec Section 26
Governance Infrastructure
- Establish Software Registry: integrate CMDB, SAM, Vendor Contract Repository; begin nightly reconciliation
- Deploy standardized intake with mandatory field enforcement; establish Portfolio Governance Council cadence
- Implement TPRM Vendor Register; assign risk tiers to all existing vendors; initiate Tier 1 DD gap remediation
- Document and ratify all 15 DMN decision tables (manual application at Horizon 1)
- Establish decision audit log; define retention standards and access controls
- Define AI Governance Checklist; establish Model Risk Inventory; assess all current AI deployments
- Map all knowledge bases; begin structured knowledge capture
Process Digitization
- Deploy conversational AI intake bot with completeness enforcement and Software Registry integration
- Implement DMN-01 through DMN-05 and DMN-15 in Camunda Platform 7; automate routing and scoring
- Automate PRD generation; deploy Jira/ADO integration; establish Git branch automation
- Implement Legal Knowledge Graph Phase 1: RFP clause library for top 5 acquisition scenarios
- Deploy immutable decision audit log in WORM-compliant storage; automated retention enforcement
- Implement DMN-06 through DMN-08 for Buy pathway automation; automate TPRM monitoring triggers
- Deploy Knowledge Staging Agent for Phases 1-4; replace email handoffs with structured knowledge flows
- Implement SLA monitoring, queue visibility dashboards, and basic escalation timers
Full Framework Realization
- Full Legal Knowledge Graph deployment: complete clause library; AI-assisted redline review
- Shadow IT continuous detection: SSO and expense management integration for real-time identification
- AI model performance observability: full drift monitoring, bias detection, hallucination monitoring dashboards
- Automated regulatory reporting package: pre-built exam packages; 24-hour retrieval SLA
- Predictive portfolio management: demand forecasting; proactive vendor renewal management
- Fourth-party risk monitoring: automated monitoring of critical vendor sub-contractors
- Full Knowledge Staging Agent deployment; zero email-based handoffs in governed workflow
- Bottleneck-driven automation: automated identification and implementation of automation opportunities
15. Risks and Mitigations
15.1 Change Management Risks
| Risk | Likelihood | Impact | Mitigation |
|---|---|---|---|
| Stakeholder resistance to formalized DMN governance replacing informal decision-making | High | High | Joint executive sponsorship from Technology and Risk leadership required before framework activation; structured training for Product Owners, Procurement Leads, and TPRM managers |
| Requestors bypassing formal intake to use informal channels | High | High | Council refuses to review any request not through formal intake; measure adoption rate vs. informal channels; executive mandate required |
| Product Owner capacity insufficient to manage increased governance throughput | Medium | High | Portfolio Governance Council cadence established and resourced before Horizon 1 launch; capacity planning tracked from Day 1 |
15.2 Technology Adoption Risks
| Risk | Likelihood | Impact | Mitigation |
|---|---|---|---|
| CMDB data quality insufficient to seed reliable Software Registry | High | High | Registry seeding audit in Horizon 1; Software Asset Manager reconciles CMDB vs. vendor invoices before nightly reconciliation automation activated |
| AI agent outputs non-deterministic or inconsistent | Medium | High | Agents bound to specific versioned knowledge bases; ≥85% confidence threshold required; human escalation mandatory below threshold |
| Trust Center APIs unavailable or returning incomplete data | Medium | Medium | Fallback to manual evidence request workflow maintained; Trust Center connectivity monitored; SLA breach alerts when evidence collection delayed |
15.3 Regulatory Change Risks
| Risk | Likelihood | Impact | Mitigation |
|---|---|---|---|
| Material regulatory guidance change requiring DMN table updates | Medium | High | 60-day update SLA on material changes; Compliance Governance owns regulatory KB with change monitoring; quarterly DMN table review |
| EU AI Act high-risk AI classification changes affecting intake routing | Medium | Medium | AI risk classification DMN-09 reviewed in each quarterly DMN audit; Compliance Governance tracks EU AI Act implementation guidance |
| OCC examination finding governance gaps requiring emergency process changes | Low | Critical | Three-level gap analysis (framework, lifecycle, evidence coverage) performed quarterly; Internal Audit independent assurance program active from Horizon 1 |