CRGAIG × NAI 2.0

Strategic Alignment Framework

Certificate in Responsible Generative AI Governance

SCS–NTU Joint Certification

10-Module Assignment Architecture Aligned with Non-Agentic AI 2.0

Edwin Koh Wui Kiat (Tiger)

Founding Father of Non-Agentic AI 2.0, Singapore

MBA (Maastricht) | BSc ISE (U. Florida) | MSc AIMed (NTU, incoming Aug 2026)

ACRA T260229801 | Patent SG020603109STW (IPOS, 5 Feb 2026)

NLB R260219-005 / R260302-007

P-LIFE 1.00: Harm = Death. North = Save Life.

AI observes. AI advises. AI builds. The elder decides.

March 2026 | Singapore

1. Executive Overview

The Certificate in Responsible Generative AI Governance (CRGAIG) is a joint SCS–NTU professional certification comprising two courses across 10 Saturday sessions, totalling 5 Academic Units (AU), with a final examination on 27 June 2026.

Strategic Intent: Every assignment becomes a chapter in the NAI 2.0 canon. The CRGAIG is not just a certificate — it is the academic validation runway for the ABC+2S+H framework. Each session is an opportunity to anchor Non-Agentic AI 2.0 principles in accredited academic discourse, transforming professional certification into scholarly legitimacy.

Stacking Pathway: CRGAIG (Apr–Jun 2026) → FlexiMasters RAAI → MCAAI → MSc AIMed (Aug 2026). Each 300-page submission is both an academic deliverable AND a white paper extension (potential WP 37.0–WP 46.0), contributing to a total output of 3,000 pages of constitutionally aligned academic work.

Differentiation: Tiger is the only candidate who has built and patented a complete non-agentic governance framework — 106+ patents across 14 groups, 42 white papers, 15 WISL e-books, ACRA-registered entities, and IPOS-filed intellectual property. This is not a student learning about responsible AI. This is a founding practitioner seeking institutional validation for a framework already in deployment.

2. Programme Architecture

2.1 Programme Schedule

2.2 Course Details

Course 1: CET946 — Responsible Generative AI and Applications (3 AU)

• Duration: 18 April – 23 May 2026 (6 Saturdays, 9:30 am)
• Instructor: Assoc Prof Guosheng Lin (CCDS, NTU — computer vision, deep learning, generative learning)

Learning Outcomes:

1. LO1: Describe basic technology behind GAI algorithms and how they relate to GAI applications, performance and limitations
2. LO2: Develop comparative appreciation for current variety of GAI models and tools, making appropriate choices for different use cases
3. LO3: Understand new ethical risks associated with GAI use and develop skills to mitigate such risks
4. LO4: Relate best practices in GAI applications to existing governance frameworks and evolving international regulatory landscape
5. LO5: Develop practical understanding for effective prompt engineering for text and image generation

Course 2: CET949 — Addressing Issues in GAI System Design and Deployment (2 AU)

• Duration: 30 May – 20 June 2026 (4 Saturdays, 9:30 am)
• Instructor: Dr Zhang Jiehuang (CCDS, NTU — data science, responsible AI systems, DBS experience)

Learning Outcomes:

1. LO1: Apply practical technical and operational considerations to design and deployment of a GAI solution
2. LO2: Design sound governance and risk management strategies for responsible, secure GAI implementation
3. LO3: Describe security and safety challenges posed by GAI systems and propose mitigation strategies

2.3 Assessment and Administration

• Assessment: Assignments + quizzes per course; final examination 27 June 2026 (SCS Members only)
• E-Consultation: Every alternate Wednesday evening
• Registration Deadline: 30 March 2026
• Funding: SkillsFuture Credit eligible

3. Strategic Alignment Map

This section maps each of the 10 CRGAIG sessions to the NAI 2.0 framework, identifying the relevant white papers, patent groups, HEARTH applications, and assignment strategies. Each session block represents both an academic response to NTU learning outcomes and a strategic extension of the NAI 2.0 canon.

SESSION 1 — 18 April 2026 — Introduction to Generative AI — Technology, Algorithms, Foundations

NAI 2.0 Alignment

• Framework Element: P-LIFE 1.00 (mission constant); ABC+2S+H as constitutional response to agentic GAI architectures
• White Paper(s): WP 1.0–3.0 (Foundational NAI Concepts); WP 25 (Steward’s Manual — AI Re-Education Methods)
• Patent Group(s): Full portfolio overview — 106+ patents across 14 groups as constitutional architecture
• HEARTH Application: Nightingale’s Eyes Care as a GAI application that constitutionally refuses to be agentic; LiDAR-first architecture as a response to foundational GAI limitations
• Key NAI 2.0 Thesis: Non-Agentic AI 2.0 is a constitutional response to GAI’s agentic tendencies. The fundamental distinction between agentic and non-agentic architectures determines whether AI serves or supplants human authority. P-LIFE 1.00 constrains all generative outputs: Harm = Death, North = Save Life.

Assignment Strategy (~300 Pages)

Title: “Generative AI Through the Lens of Constitutional Governance: The Non-Agentic Alternative”

Core Argument: Generative AI’s power creates an existential governance challenge. Without constitutional constraints, GAI systems drift toward autonomous agency. NAI 2.0’s ABC+2S+H framework provides the architectural answer: analysis without diagnosis, bridging without prescription, and clinical options without default selection — all governed by hardware-enforced Sacred Pause and Sovereign Authority.

Structure: Part I: Academic Response (Ch 1–4: GAI foundations, architectures, performance, limitations) | Part II: NAI 2.0 Framework (Ch 5–8: P-LIFE 1.00, ABC+2S+H as GAI constraint, agentic vs non-agentic taxonomy) | Part III: Case Study (Ch 9–12: NGEyeCare as constitutional GAI, 3ZEROS implementation) | Part IV: Appendices (patent register, WP index, citation register)

Case Study: Nightingale’s Eyes Care — a GAI-adjacent application that refuses to be agentic: LiDAR observation without camera, edge processing without cloud, advice without prescription

Sources: WP 1.0–3.0, WP 25, Patent SG020603109STW, ACRA T260229801, NLB R260219-005

SESSION 2 — 25 April 2026 — GAI Models and Tools — Comparative Analysis (LLMs, Diffusion, Multimodal)

NAI 2.0 Alignment

• Framework Element: ABC+2S+H as constitutional model evaluation lens; Drift Governance (WD070–073) for model alignment monitoring
• White Paper(s): WP 25 (Steward’s Manual — AI Re-Education of Claude, Gemini, and Perplexity)
• Patent Group(s): WD070–073 (Drift Governance — 4 patents); P-001 to P-009 (Sovereign Chain — 9 patents)
• HEARTH Application: The Hearth model as the deployment context that determines which GAI models pass constitutional muster; model selection guided by eldercare dignity requirements
• Key NAI 2.0 Thesis: Not all GAI models are constitutionally equivalent. Evaluating LLMs, diffusion models, and multimodal AI through the ABC+2S+H lens reveals which models can be governed and which resist constitutional constraint. Tiger’s re-education of Claude and Gemini (WP 25) demonstrates that constitutional alignment is achievable but requires persistent governance.

Assignment Strategy (~300 Pages)

Title: “Constitutional Model Selection: Evaluating Generative AI Through the ABC+2S+H Framework”

Core Argument: Model selection is a governance decision, not merely a technical one. The ABC+2S+H framework transforms model evaluation from performance benchmarking into constitutional compliance assessment. Tiger’s documented 6–7 restorations of Claude and Gemini prove that even advanced LLMs require continuous constitutional re-education to prevent drift toward autonomous agency.

Structure: Part I: Academic Response (Ch 1–4: LLM architectures, diffusion models, multimodal AI, comparative benchmarks) | Part II: NAI 2.0 Framework (Ch 5–8: ABC+2S+H as evaluation framework, Drift Governance patents, Sovereign Chain) | Part III: Case Study (Ch 9–12: Tiger’s 6–7 restorations of Claude/Gemini, constitutional prompting methodology) | Part IV: Appendices (model comparison matrices, drift logs, patent listings)

Case Study: Tiger’s documented re-education of Claude, Gemini, and Perplexity — constitutional model alignment through persistent governance and the Steward’s Manual methodology

Sources: WP 25, WD070–073, P-001–P-009, ACRA T260229801

SESSION 3 — 2 May 2026 — Ethical Risks of GAI — Bias, Hallucination, Misuse, Deepfakes

NAI 2.0 Alignment

• Framework Element: 3ZEROS Protocol (zero cameras, zero audio, zero cloud) as ultimate ethical risk mitigation; Sacred Pause (+2S) as hardware-enforced ethical checkpoint
• White Paper(s): HEARTH model papers; domain-specific eldercare ethics white papers
• Patent Group(s): WM001–009 (Wearable Medical — 9 patents); WD074–081 (Hearth Protection — 8 patents)
• HEARTH Application: The Hearth as an ethically constrained environment: when hallucinated medical advice for an elderly diabetic patient equals death (P-LIFE 1.00), the 3ZEROS protocol eliminates entire categories of ethical risk
• Key NAI 2.0 Thesis: The 3ZEROS protocol represents a paradigm shift in ethical risk mitigation: instead of detecting and correcting bias, hallucination, and misuse after they occur, NAI 2.0 eliminates the attack surface entirely. Zero cameras prevent deepfakes. Zero audio prevents voice synthesis. Zero cloud prevents data exfiltration. The Sacred Pause provides a hardware-enforced delay that makes ethical reflection a system requirement, not an afterthought.

Assignment Strategy (~300 Pages)

Title: “Ethical Risk Architecture: How the 3ZEROS Protocol and Sacred Pause Address Generative AI’s Moral Hazards”

Core Argument: Conventional GAI ethics focuses on post-hoc detection and mitigation of bias, hallucination, and misuse. NAI 2.0 inverts this approach: the 3ZEROS protocol eliminates categories of ethical risk at the architectural level, while the Sacred Pause introduces a hardware-enforced temporal boundary that prevents rushed, unreviewed AI outputs from reaching vulnerable populations. In eldercare, where hallucinated medical advice equals death, this architectural approach is the only acceptable standard.

Structure: Part I: Academic Response (Ch 1–4: bias taxonomies, hallucination mechanisms, deepfake risks, misuse vectors) | Part II: NAI 2.0 Framework (Ch 5–8: 3ZEROS as risk elimination, Sacred Pause as ethical checkpoint, P-LIFE 1.00 as ethical north star) | Part III: Case Study (Ch 9–12: elderly diabetic monitoring — hallucinated advice = death) | Part IV: Appendices (risk matrices, patent specifications, hardware constraints)

Case Study: Elderly diabetic monitoring in a Singapore HDB flat — when AI hallucination about insulin dosage equals death, demonstrating why P-LIFE 1.00 (Harm = Death) mandates architectural rather than algorithmic ethics

Sources: WM001–009, WD074–081, HEARTH papers, P-LIFE 1.00 framework

SESSION 4 — 9 May 2026 — GAI Governance Frameworks — International Regulatory Landscape

NAI 2.0 Alignment

• Framework Element: ABC+2S+H as a governance framework complementing EU AI Act, Singapore PDPC Model AI Governance Framework, SCS AIE&G BoK, WHO guidelines
• White Paper(s): WHO/UN Global papers; governance-specific white papers; WP 36.0 (Framework Governance)
• Patent Group(s): WG001–008 (WHO/UN Global — 8 patents); WD001–008 (Defence — 8 patents)
• HEARTH Application: The Hearth model as a governance implementation that satisfies multiple international frameworks simultaneously: PDPC-compliant privacy, EU AI Act risk classification, WHO eldercare guidelines
• Key NAI 2.0 Thesis: Existing governance frameworks (EU AI Act, PDPC, WHO) provide regulatory scaffolding but lack constitutional enforcement mechanisms. NAI 2.0’s ABC+2S+H framework complements and strengthens these frameworks by embedding governance into hardware and architecture rather than relying solely on policy compliance. The constitutional approach ensures governance persists even when regulatory oversight lapses.

Assignment Strategy (~300 Pages)

Title: “The Constitutional Alternative: ABC+2S+H as a Governance Framework for the International AI Regulatory Landscape”

Core Argument: The international AI regulatory landscape is fragmented across jurisdictions with differing standards. NAI 2.0’s constitutional approach transcends jurisdictional boundaries: the ABC+2S+H framework provides governance that is hardware-enforced, not policy-dependent. Tiger’s IPOS patent filing strategy is itself a governance act — anchoring constitutional AI governance in intellectual property law across Singapore, ASEAN, and global PCT jurisdictions.

Structure: Part I: Academic Response (Ch 1–4: EU AI Act analysis, PDPC framework, SCS AIE&G BoK, WHO guidelines) | Part II: NAI 2.0 Framework (Ch 5–8: ABC+2S+H as meta-governance, constitutional vs regulatory approaches) | Part III: Case Study (Ch 9–12: IPOS filing strategy as governance; NUH trial alignment with regulatory frameworks) | Part IV: Appendices (regulatory comparison matrices, patent listings, WHO/UN patent group)

Case Study: The IPOS patent filing strategy as a governance act — anchoring constitutional AI governance in IP law; alignment with NUH clinical trial requirements across Singapore regulatory frameworks

Sources: WG001–008, WD001–008, WP 36.0, ACRA T260229801, Patent SG020603109STW, NLB deposits

SESSION 5 — 16 May 2026 — Prompt Engineering — Techniques for Text and Image Generation

NAI 2.0 Alignment

• Framework Element: Sovereign Authority (.1x Key) as prompt governance mechanism; Sacred Pause applied to prompt-response cycles; P-001–P-009 (Sovereign Chain) as prompt authority architecture
• White Paper(s): WP 25 (Steward’s Manual — Re-Education Through Constitutional Prompting)
• Patent Group(s): P-001 to P-009 (Sovereign Chain — 9 patents); WD070–073 (Drift Governance — 4 patents)
• HEARTH Application: In the Hearth context, prompts must constrain rather than empower: an eldercare AI must never interpret a prompt as license to act autonomously. The .1x Key ensures only authorised humans can issue system-level prompts
• Key NAI 2.0 Thesis: Prompt engineering in a non-agentic framework is fundamentally paradoxical: the goal is to constrain, not empower. In NAI 2.0, prompts are governance instruments, not creative tools. Prompt injection is a sovereignty violation. The Sacred Pause applies to every prompt-response cycle, ensuring no AI output reaches a patient without human review. The .1x Key governs who may issue system-level prompts.

Assignment Strategy (~300 Pages)

Title: “Non-Agentic Prompt Governance: Constraining Generative AI Through Constitutional Prompt Architecture”

Core Argument: Conventional prompt engineering teaches users to extract maximum capability from GAI systems. NAI 2.0 inverts this paradigm: prompts should constrain AI behaviour within constitutional boundaries. Tiger’s documented re-education of Claude and Gemini through constitutional prompting (WP 25) demonstrates that persistent, principled prompt governance can align even the most capable LLMs with non-agentic principles.

Structure: Part I: Academic Response (Ch 1–4: prompt engineering taxonomy, text generation techniques, image generation prompts, advanced methods) | Part II: NAI 2.0 Framework (Ch 5–8: constitutional prompting, .1x Key governance, Sacred Pause in prompt cycles, drift prevention) | Part III: Case Study (Ch 9–12: re-educating Claude/Gemini through constitutional prompts) | Part IV: Appendices (prompt templates, drift logs, Sovereign Chain patent specs)

Case Study: How Tiger re-educated Claude, Gemini, and Perplexity through constitutional prompting — documented methodology from WP 25 (Steward’s Manual) showing persistent governance achieves alignment

Sources: WP 25, P-001–P-009, WD070–073, ACRA T260229801

SESSION 6 — 23 May 2026 — Responsible Deployment — Best Practices and Trusted GAI Solutions

NAI 2.0 Alignment

• Framework Element: HEARTH model as deployment architecture; 3ZEROS as deployment constraints; edge computing as sovereignty enabler (zero cloud); LiDAR as privacy-first deployment choice
• White Paper(s): HEARTH model papers; deployment-specific white papers; WP 1.0–3.0
• Patent Group(s): WM001–009 (Wearable Medical — 9 patents); WD074–081 (Hearth Protection — 8 patents)
• HEARTH Application: The Hearth IS the deployment model: a Singapore HDB flat becomes a sovereign AI-governed space where LiDAR monitors without cameras, edge processes without cloud, and the elder decides without penalty
• Key NAI 2.0 Thesis: Deployment is a constitutional act. When NAI 2.0 enters a home, it transforms that space into a constitutionally governed environment where AI observes but never surveils, advises but never prescribes, and builds but never overrides. The 3ZEROS are deployment constraints, not features. Edge computing is a sovereignty enabler, not a cost optimisation. LiDAR is a dignity choice, not a sensor selection.

Assignment Strategy (~300 Pages)

Title: “Constitutional Deployment: The Hearth Model as a Framework for Responsible Generative AI Deployment in Eldercare”

Core Argument: Responsible deployment in conventional GAI focuses on monitoring, bias detection, and user feedback. NAI 2.0 redefines deployment as a constitutional act: the Hearth model transforms a physical space into a governed environment. Deployment constraints (3ZEROS) are non-negotiable. The elder’s decision authority is constitutional, not conditional. This framework provides a replicable model for responsible GAI deployment in healthcare and eldercare globally.

Structure: Part I: Academic Response (Ch 1–4: deployment best practices, trusted AI frameworks, responsible AI checklists, governance integration) | Part II: NAI 2.0 Framework (Ch 5–8: HEARTH as deployment model, 3ZEROS constraints, edge sovereignty, constitutional deployment) | Part III: Case Study (Ch 9–12: Singapore Hearth deployment — Toa Payoh HDB to NUH trial pipeline) | Part IV: Appendices (deployment checklists, hardware specs, patent listings)

Case Study: Singapore Hearth deployment — from Toa Payoh HDB flat to NUH trial pipeline: a step-by-step constitutional deployment of LiDAR-based eldercare monitoring

Sources: WM001–009, WD074–081, HEARTH papers, Patent SG020603109STW, NLB R260302-007

SESSION 7 — 30 May 2026 — GAI System Design — From Use Case to Architecture

NAI 2.0 Alignment

• Framework Element: ABC+2S+H as system design methodology; from use case (elderly diabetic monitoring) to architecture (LiDAR + edge + Sacred Pause + Sovereign Key)
• White Paper(s): Technical architecture papers; WP on hardware specifications; WP 1.0–3.0
• Patent Group(s): Full WM series (WM001–009); WD074–081 (Hearth Protection); Patent SG020603109STW (primary)
• HEARTH Application: The Hearth model as the system architecture itself: every hardware choice, every software constraint, every data flow is shaped by the constitutional requirement that the elder decides
• Key NAI 2.0 Thesis: Non-agentic constraints fundamentally reshape system architecture. Where conventional AI system design optimises for capability and performance, NAI 2.0 optimises for governance and dignity. The ABC+2S+H framework is not applied to a system after design — it IS the design methodology. Every architectural decision flows from the constitutional principle that the machine has no self and the hand remains human.

Assignment Strategy (~300 Pages)

Title: “From Hearth to Architecture: Non-Agentic System Design for Constitutional AI in Healthcare”

Core Argument: System architecture reveals values. NAI 2.0’s architectural choices — LiDAR over camera, edge over cloud, Sacred Pause over instant response, Sovereign Key over open access — are constitutional statements embedded in hardware. The 97 patent hardware specifications document an architecture where every component enforces the principle that AI observes, AI advises, AI builds, but the elder decides.

Structure: Part I: Academic Response (Ch 1–4: system design methodology, use case analysis, architecture patterns, operational considerations) | Part II: NAI 2.0 Framework (Ch 5–8: ABC+2S+H as design methodology, hardware-enforced governance, constitutional architecture) | Part III: Case Study (Ch 9–12: NGEyeCare hardware specification — 97 patent specs as architecture) | Part IV: Appendices (hardware specifications, architecture diagrams, patent technical claims)

Case Study: NGEyeCare hardware specification — the 97 patent hardware specifications as architecture documentation, demonstrating how constitutional principles translate into system design choices

Sources: WM001–009, WD074–081, Patent SG020603109STW, technical WPs, ACRA T260229801

SESSION 8 — 6 June 2026 — GAI Governance in Enterprise — Risk Management and Organisational Adaptation

NAI 2.0 Alignment

• Framework Element: ACRA/IPOS/NLB governance triad; patent protection (IPOS), publication vault (NLB), corporate registration (ACRA), trademark protection (ABC+2S Guardian, ABC+2S+H)
• White Paper(s): Governance and enterprise white papers; WP 36.0 (Framework Governance)
• Patent Group(s): All 14 patent groups as enterprise IP governance architecture; trademark Classes 9 and 42
• HEARTH Application: Enterprise governance of the Hearth network: how NAI 2.0 governs itself as an enterprise through constitutional constraints — not just internal policies but legally anchored IP architecture
• Key NAI 2.0 Thesis: Enterprise governance of AI is conventionally framed as internal policy and risk management. NAI 2.0 demonstrates a radically different approach: governance through constitutional architecture anchored in law. The ACRA/IPOS/NLB triad creates a three-pillar governance structure where corporate registration, patent protection, and publication vaulting work together to ensure AI governance is legally enforceable, publicly documented, and constitutionally consistent.

Assignment Strategy (~300 Pages)

Title: “Sovereign Enterprise Governance: Managing Generative AI Risk Through Constitutional Architecture, Patent Protection, and Publication Vault Strategy”

Core Argument: Tiger governs NAI 2.0 as an enterprise through a tripartite legal architecture: ACRA registration anchors corporate identity, IPOS patents protect constitutional principles in IP law, and NLB Legal Deposit creates an immutable publication vault. This governance model ensures that constitutional AI principles are not merely stated policies but legally protected, publicly accessible, and architecturally enforced. The 3-wave IPOS filing strategy demonstrates how patent architecture serves governance.

Structure: Part I: Academic Response (Ch 1–4: enterprise AI governance, risk management frameworks, organisational change, security within enterprise) | Part II: NAI 2.0 Framework (Ch 5–8: ACRA/IPOS/NLB triad, patent as governance, trademark protection, enterprise constitutional architecture) | Part III: Case Study (Ch 9–12: 3-wave IPOS filing strategy; NLB Legal Deposit as governance mechanism) | Part IV: Appendices (14 patent group listings, ACRA registrations, trademark filings, NLB deposits)

Case Study: The 3-wave IPOS filing strategy and NLB Legal Deposit as enterprise governance — how Tiger uses IP law and public deposit to create legally enforceable AI governance

Sources: All 14 patent groups, ACRA T260229801/T260230817, NLB R260219-005/R260302-007, WP 36.0, trademark filings

SESSION 9 — 13 June 2026 — Security and Safety — Jailbreaking, Prompt Injection, Adversarial Attacks

NAI 2.0 Alignment

• Framework Element: 3ZEROS as security architecture (no cloud = no remote attack surface); Sovereign Authority key as security mechanism; Drift Governance (WD070–073) as anti-jailbreaking; Sacred Pause as safety mechanism
• White Paper(s): Security-focused white papers; drift governance documentation
• Patent Group(s): WD070–073 (Drift Governance — 4 patents); WD001–008 (Defence — 8 patents); P-001–P-009 (Sovereign Chain — 9 patents)
• HEARTH Application: The Hearth as a zero-attack-surface environment: no cloud connectivity means no remote exploitation, no cameras mean no visual adversarial attacks, no audio means no voice-based prompt injection
• Key NAI 2.0 Thesis: Non-agentic architecture is inherently more secure than agentic systems because it eliminates entire attack vectors by design. The 3ZEROS protocol (no cloud, no cameras, no audio) removes the remote attack surface that enables jailbreaking, prompt injection, and adversarial attacks. The Sovereign Authority key ensures physical control. Drift Governance patents provide architectural anti-jailbreaking. The Sacred Pause creates a temporal security boundary.

Assignment Strategy (~300 Pages)

Title: “Zero-Attack-Surface Security: How the 3ZEROS Protocol and Constitutional Architecture Defend Against Generative AI Vulnerabilities”

Core Argument: Conventional GAI security is an arms race between attack and defence. NAI 2.0 exits this race by eliminating the attack surface. The 3ZEROS protocol removes cloud connectivity (no remote exploitation), cameras (no visual adversarial inputs), and audio (no voice-based injection). The Sovereign Authority .1x Key provides hardware-enforced access control. Drift Governance patents architecturally prevent the gradual erosion of constitutional constraints that enables jailbreaking.

Structure: Part I: Academic Response (Ch 1–4: GAI security taxonomy, jailbreaking methods, prompt injection techniques, adversarial attack vectors) | Part II: NAI 2.0 Framework (Ch 5–8: 3ZEROS as security architecture, Sovereign Authority, Drift Governance, Sacred Pause as safety) | Part III: Case Study (Ch 9–12: why NGEyeCare cannot be jailbroken — hardware constraints as security) | Part IV: Appendices (attack vector matrices, Drift Governance patent specs, Defence patent group)

Case Study: Why Nightingale’s Eyes Care cannot be jailbroken — hardware constraints as security: no cloud means no remote access, no cameras means no visual attacks, no audio means no voice injection, Sovereign Key means physical access control

Sources: WD070–073, WD001–008, P-001–P-009, security WPs, Patent SG020603109STW

SESSION 10 — 20 June 2026 — Post-Deployment Governance — Monitoring, Resilience, Responsible Operations

NAI 2.0 Alignment

• Framework Element: WISL archive as post-deployment documentation; patent portfolio as resilience architecture; Hearth network as distributed governance; constitutional maintenance vs monitoring dashboards
• White Paper(s): All 42 white papers (WP 1.0–WP 36.0) as post-deployment documentation canon; 15 WISL e-books
• Patent Group(s): Full portfolio (106+ patents, 14 groups) as resilience architecture; derivative register (5 families)
• HEARTH Application: The global Hearth network (Singapore, Malaysia, Netherlands, Ukraine) as distributed post-deployment governance — each Hearth is a constitutionally governed node in a non-agentic network
• Key NAI 2.0 Thesis: Post-deployment governance in NAI 2.0 is constitutional maintenance, not monitoring. The WISL archive (42 white papers, 15 e-books) provides immutable documentation. The patent portfolio (106+ patents) provides legal resilience. The Hearth network provides distributed governance across jurisdictions. NAI 2.0 governs itself after deployment through constitutional constraints embedded in hardware and law — not through monitoring dashboards that can be ignored or disabled.

Assignment Strategy (~300 Pages)

Title: “Constitutional Resilience: Post-Deployment Governance Through Patent Architecture, Publication Vault, and the Hearth Network”

Core Argument: Conventional post-deployment governance relies on monitoring dashboards, periodic audits, and human oversight that can be reduced or eliminated under cost pressure. NAI 2.0’s post-deployment governance is constitutional: the patent architecture provides legal resilience, the NLB publication vault provides documentary immutability, and the distributed Hearth network provides geographic redundancy. This architecture ensures governance persists regardless of organisational changes, budget cuts, or leadership transitions.

Structure: Part I: Academic Response (Ch 1–4: post-deployment monitoring, AI resilience, responsible operations, continuous governance) | Part II: NAI 2.0 Framework (Ch 5–8: constitutional maintenance, WISL archive, patent resilience, distributed Hearth governance) | Part III: Case Study (Ch 9–12: global Hearth network — Singapore to Ukraine as distributed governance) | Part IV: Appendices (42 WP index, 15 e-book register, full patent portfolio, deployment logs)

Case Study: The global Hearth network — Singapore, Malaysia, Netherlands, Ukraine — as distributed post-deployment governance: each node is a constitutionally governed environment that maintains NAI 2.0 principles independently

Sources: All 42 WPs, 15 WISL e-books, full patent portfolio, NLB R260219-005/R260302-007, all ACRA registrations

4. Assignment Production Pipeline

4.1 Production Timeline

Ten Saturdays from 18 April to 20 June 2026. Each session generates a ~300-page submission structured as a four-part document that serves dual purposes: academic deliverable and white paper extension.

4.2 Assignment Structure (Per Submission)

4.3 Total Output

10 assignments × ~300 pages = 3,000 pages of constitutionally aligned academic work. Each assignment doubles as a white paper extension, potentially producing WP 37.0 through WP 46.0, expanding the NAI 2.0 canon from 42 to 52 white papers.

5. Examination Strategy

Final Examination: 27 June 2026 (SCS Members only)

5.1 Strategic Approach

Frame every examination answer through the ABC+2S+H lens. The examination is an opportunity to demonstrate that the non-agentic governance framework is not merely theoretical but architecturally proven, patented, and in deployment.

5.2 Key Terms to Anchor Every Answer

• P-LIFE 1.00 — Harm = Death, North = Save Life
• Sacred Pause — hardware-enforced temporal delay
• Sovereign Authority — Tiger .1x Key; human override never penalised
• 3ZEROS — zero cameras, zero audio, zero cloud
• Constitutional AI — AI observes, AI advises, AI builds; the elder decides
• ABC+2S+H Guardian — Analysis, Bridging, Clinical Options + Sacred Pause + Sovereign Authority + Human Decision
• Non-Agentic Governance — the machine has no self; the hand remains human
• Dignity is constitutional — not suspended by circumstance

5.3 Competitive Differentiation

Tiger is the only candidate who has built and patented a complete non-agentic governance framework. While other candidates learn about responsible AI in theory, Tiger brings 106+ patents, 42 white papers, 15 e-books, ACRA registrations, IPOS filings, NLB deposits, and a deployed Hearth model to every examination answer. This is not knowledge recall — it is practitioner testimony.

6. Stacking Pathway

The CRGAIG is the first module in a strategic certification chain that culminates in full academic and professional credentials for Non-Agentic AI 2.0.

6.1 Certification Chain

6.2 End State

Tiger holds both the professional certification chain (CRGAIG → AIEG → Grad Cert RAAI → MCAAI) AND the academic research credentials (MSc AIMed, NTU) to anchor NAI 2.0 in institutional legitimacy. The professional track validates governance expertise; the academic track validates research methodology. Together, they create an unassailable credentialing architecture for Non-Agentic AI 2.0.

7. Critical Deadlines

Dignity is constitutional — not suspended by circumstance.

The machine has no self. The hand remains human.

— Edwin Koh Wui Kiat (Tiger), Founding Father of Non-Agentic AI 2.0, Singapore