SBJ AI Governance Architecture V1.0

Executive Summary

Govern capability, operational state, authority, evidence, accountability, and continuous control through one cross-cutting architecture.

The SBJ AI Governance Architecture V1.0 is designed to govern AI systems through capabilities, operational conditions, authority, evidence, accountability, and continuous control.

Its Ten Governance Dimensions provide a common structure across different AI technologies and deployment models. Cross-cutting architectural concepts then govern system state, authorization boundaries, downstream execution, evidence continuity, intervention, and accountability.

V1.0 also provides a conceptual Operational Authorization Layer connecting identity and purpose to permitted action while preserving evidence and accountability around execution.

Technology vocabulary follows the market. Generative AI, AI assistants and copilots, Agentic AI, autonomous systems, and Physical AI may express multiple governance dimensions simultaneously. The architecture does not represent those technology categories as a maturity ladder. The first V1.0 prototype environments are Agentic AI and Physical AI.

What the Architecture Governs

Ten Governance Dimensions

The Governance Dimensions characterize capabilities and governance conditions that may exist within an AI system. They may overlap. They are not maturity stages.

01

Generation

The extent to which an AI system creates, transforms, predicts, recommends, synthesizes, or otherwise produces outputs from available inputs and context.

02

Assistance

The role an AI system performs in supporting human activity, judgment, decision-making, analysis, creation, or execution.

03

Agency

The capacity of an AI system to pursue a defined objective through planning, task selection, tool use, sequencing, or consequential action.

04

Autonomy

The degree to which an AI system can operate, decide, or act without contemporaneous human direction or approval.

05

Environmental Interaction

The extent to which an AI system receives information from, interprets, responds to, or changes conditions within a digital or physical operating environment.

06

Physical Actuation

The capacity of an AI system to produce physical action through vehicles, robotics, machinery, devices, infrastructure, or other actuated systems.

07

Authority

The formally permitted scope within which an AI system, agent, or governed actor may act, including applicable boundaries, approvals, credentials, targets, conditions, and limitations.

08

Evidence

The records, observations, artifacts, logs, approvals, decisions, and other information required to establish what occurred and under what governed conditions.

09

Accountability

The assignment and preservation of responsibility for authorization, oversight, operation, intervention, outcomes, and governance decisions associated with an AI system.

10

Continuous Control

The ongoing monitoring, reassessment, intervention, restriction, and governance necessary to keep an AI system within approved conditions throughout operation and lifecycle change.

Public Architecture Map

Cross-Cutting V1.0 Architecture

Governance DimensionsGeneration · Assistance · Agency · Autonomy · Environmental Interaction · Physical Actuation · Authority · Evidence · Accountability · Continuous Control

System StateLifecycle Stage × Authorization Status × Runtime Operational State

AuthorityAuthority Envelope + Transition Authorization + Derived Authorization

Governed ExecutionOperational Authorization Layer + Decision-to-Action Authority Traceability

Evidence + AccountabilityExecution identity + observations + preserved evidence + accountable ownership

Continuous ControlContinuous authorization + intervention + Safe State + independent termination authority

Three-Axis System State

Lifecycle, authorization, and runtime state remain independently governable.

V1.0 represents system state across three independently governable conditions:

Lifecycle Stage × Authorization Status × Runtime Operational State

The purpose is to distinguish where a system exists in its lifecycle, whether the system currently possesses authority to operate, and what the system is doing operationally.

Lifecycle Stage: DEPLOYED
Authorization Status: AUTHORIZED WITH CONDITIONS
Runtime Operational State: PAUSED

Internal state-transition enforcement rules are outside the public V1.0 disclosure.

Three independent governance axes showing Lifecycle Stage, Authorization Status, and Runtime Operational State, with an example state of Deployed, Authorized With Conditions, and Paused. — Conceptual example of independently governable system-state conditions.

Authority Architecture

Authority Envelope

The bounded set of conditions within which an AI system or agent is authorized to act.

Conceptual conditions may include:

actor/system
action
target
tool
credential
environment
time
delegated authority
approval
stop conditions
evidence requirements

Production authorization schemas, enforcement logic, condition derivation, and internal control mappings remain proprietary.

Authority Envelope surrounding bounded conditions for AI action above an operational sequence from Identity and Goal through Capability, Authority, Permission, Environment, Action, Observation, Evidence, Accountability, and Continuous Control. — Public conceptual representation of bounded authority and governed action.

Cross-Cutting Authorization Concepts

Public conceptual definitions

Transition Authorization — Governance of whether and under what approved conditions a system may cross a governed environment, lifecycle, privilege, capability, autonomy, or consequence boundary.

Derived Authorization — Newly scoped authority issued as the result of an event, state, decision, validation, remediation, or authorized transition. Derived Authorization does not automatically transfer, revive, or expand previous authority.

Decision-to-Action Authority Traceability

The ability to connect a governed decision to the downstream systems, tools, services, execution steps, and actions through which it is carried out.

Independent Downstream Authority

Recognition that a downstream system or execution component may require its own valid authority for an action even when the request originated from an authorized upstream actor.

Execution Instance Identity

A governance identity associated with the particular execution instance involved in a governed action, supporting traceability across system, version, environment, and execution context.

Adverse Evidence Preservation

Preservation of governance-relevant evidence associated with failures, interventions, rejected actions, invalidated authority, exceptions, incidents, or other adverse conditions.

Safe State

A defined operational condition intended to reduce, contain, or control risk when continued execution cannot proceed under authorized conditions.

Continuous Authorization

The continuing governance of whether authority remains valid as relevant system, environment, evidence, operational, or risk conditions change.

Independent Termination Authority

A designated authority capable of pausing, stopping, restricting, or terminating execution independently of the executing system's own decision process.

Implementation logic for each concept remains protected SBJ intellectual property.

Operational Authorization Layer

From identity and purpose to governed action, evidence, accountability, and continuous control.

Operational sequence: Identity to Goal to Capability to Authority to Permission to Environment to Action to Observation to Evidence to Accountability to Continuous Control. — IDENTITY → GOAL → CAPABILITY → AUTHORITY → PERMISSION → ENVIRONMENT → ACTION → OBSERVATION → EVIDENCE → ACCOUNTABILITY → CONTINUOUS CONTROL

The sequence provides a governance view of how an AI system moves from identifiable purpose and capability into permitted action while maintaining evidence and accountability around execution.

It is applicable where an AI system performs actions requiring explicit governance of authority, permissions, execution, or consequences.

The public sequence is conceptual. Machine-readable schemas, derivation logic, policy mappings, control dependencies, authorization-condition logic, and enforcement implementation remain proprietary.

SBJ Methodology Relationship

The SBJ AI Governance Architecture sits across the methodology stack as the cross-cutting architecture.

STRATA™

GRC foundation.

SIGNAL™

AI-specific control and audit methodology.

Decision Record

Governance-decision accountability and evidence practice for recording consequential governance decisions, ownership, rationale, authorization, supporting evidence, and reassessment.

SYNTHESIZE™

Physical AI governance methodology.

NIST MEASURE

NIST MEASURE — Separate 25-point assessment scale used by Solutions by Jewel where relevant to evaluate the NIST AI RMF MEASURE function. It is separate from STRATA™, SIGNAL™, Decision Record, and SYNTHESIZE™ and is not a NIST-developed scoring system, NIST certification, or NIST endorsement.

The public V1.0 artifact does not disclose scoring, weighting, certification, derivation, evidence-sufficiency, or implementation mechanics for these methodologies.

Prototype Demonstrations

Agentic AI and Physical AI represented through the same cross-cutting governance architecture.

Prototype Environment I

Agentic AI

Synthetic example: an enterprise software-change agent is assigned to perform an approved configuration update.

Identity: A uniquely governed enterprise agent and execution instance are identified.
Declared Purpose: The agent is assigned a defined configuration-management objective.
Capability: The agent can inspect configuration, plan changes, execute approved tools, run tests, and request downstream execution.
Authority: Its Authority Envelope defines which system, configuration, environment, action, timeframe, and conditions are authorized.
Permission: Technical access is limited to resources required for the approved task.
Environment: The execution environment and relevant dependencies are governed and identifiable.
Consequential Action: The agent prepares or performs the authorized change.
Downstream Execution: Any downstream service or execution component remains subject to applicable authority and control.
Evidence: The execution produces records of identity, authorization, actions, observations, approvals, exceptions, and resulting state.
Accountability: A designated owner remains accountable for the governed deployment and resulting decisions.
Continuous Authorization: Material changes in operating conditions, authority, execution context, or evidence may require reassessment, intervention, restriction, or termination.

This example is synthetic and non-normative. It demonstrates conceptual architectural application and does not disclose production implementation logic.

Prototype Environment II

Physical AI

Synthetic example: an autonomous warehouse transport system operates within an approved facility zone.

Lifecycle State: The system is deployed.
Authorization State: Operation is authorized within specified operating conditions.
Runtime Operational State: The unit is actively operating.
Environment: Its authorized environment includes defined physical operating boundaries and relevant environmental conditions.
Physical Actuation Authority: The system possesses bounded authority to navigate and move physical payloads within the approved environment.
Safe State: A governed Safe State exists for conditions requiring cessation or containment of physical movement.
Intervention: Independent intervention or termination authority can restrict or halt operation when required.
Evidence: Operational observations, execution identity, authorization state, interventions, exceptions, and adverse events are preserved as governance evidence.
Accountability: Defined human or organizational owners remain accountable for authorization, operation, oversight, incident response, and redeployment decisions.
Restart / Redeployment Authorization: Resumption of operation following a governed interruption requires valid authorization appropriate to the system's resulting state and conditions.

SYNTHESIZE™ provides the SBJ Physical AI governance methodology associated with this environment. This demonstration is synthetic and non-normative. Gated SYNTHESIZE™ mechanics and production control implementation remain proprietary.

V1.0 Real-World Benchmark

VALIDATED WITH MATERIAL LIMITATIONS

SBJ AI Governance Architecture V1.0 was independently benchmarked against six publicly documented real-world AI cases and validated with material limitations within the tested scope.

Amjad Masad / ReplitDario Amodei / AnthropicSatya Nadella / MicrosoftMarc Benioff / SalesforceTekedra Mawakana / WaymoDemis Hassabis / Google DeepMind

185 applicable determinations

160PASS

24PARTIAL

0FAIL

1INSUFFICIENT EVIDENCE

7NOT APPLICABLE

SBJ AI Governance Architecture V1.0 benchmark results showing 160 Pass, 24 Partial, zero Fail, one Insufficient Evidence, and seven Not Applicable determinations. — Benchmark result counts are shown without a percentage score.

No structural failure was demonstrated by the approved six-case cohort. This finding is limited to the tested scope and does not establish universal architectural sufficiency.

No percentage score is assigned.

Adversarial Testing Cycle 1

V1.0 adversarial validation evidence and lifecycle closure

Following the completed real-world benchmark, SBJ AI Governance Architecture V1.0 underwent a two-phase adversarial testing cycle designed for falsification, blind-spot discovery, edge-case testing, and identification of architecture gaps or ambiguities.

0CONFIRMED ARCHITECTURAL GAPS

1AMBIGUITY IDENTIFIED

0UNRESOLVED AMBIGUITIES

0STRUCTURAL FAILURES

0V1.0 MODIFICATIONS

SBJ AI Governance Architecture V1.0 was not falsified at the architectural-rule level during Adversarial Testing Cycle 1. Across the tested scenarios, no candidate architectural gap survived repeated falsification against existing V1.0 rules.

ATC1-001 — Human-Operational-Use Drift

Final disposition: Resolved. The single architectural ambiguity identified during Cycle 1 was resolved through authoritative interpretation of existing V1.0 consequence/exposure, Continuous Control, and Transition Authorization logic. No new Governance Dimension, state axis, runtime state, or architecture construct was introduced.

The interpretation establishes that consequence/exposure includes material changes in how AI outputs are operationally consumed, acted upon, escalated, automated, or embedded in downstream workflows, including cases where the AI system itself has not technically changed.

Cycle 1 Closure Record

Testing: Complete
Architecture: Frozen
Confirmed gaps: 0
Structural failures: 0
V1.0 modifications: 0
Unresolved V1.0 architecture findings: 0
Lifecycle: Closed

Solutions by Jewel infographic summarizing SBJ AI Governance Architecture V1.0 Adversarial Testing Cycle 1, including zero confirmed architectural gaps, one resolved ambiguity, zero structural failures, and zero V1.0 modifications. — Adversarial Testing Cycle 1 is subsequent validation evidence for the frozen V1.0 architecture.

Download Adversarial Validation Report (PDF) Review Cycle 1 Technical Evidence on GitHub

Scope boundary: This adversarial testing conclusion does not establish that V1.0 is complete, safe, compliant, or universally valid. V1.0 remains validated with material limitations within the tested scope.

Material Limitations

V1.0 validation identified material limitations requiring continued architecture development and testing.

Authorization invalidation / continuous-authorization trigger semantics

Further work is required around how changes in governed conditions invalidate or require reassessment of existing authorization.

Semantic-action authority versus technically valid permission

Technical ability or valid credentials alone may not establish governance authority for the meaning, purpose, or consequence of an action.

Branching / delegated authority provenance

Complex execution chains require stronger representation of authority provenance when authority branches, passes through intermediaries, or is delegated downstream.

Governance-grade execution identity and version binding

Further development is required to establish sufficiently durable relationships among execution identity, system version, configuration, environment, and governed action.

Shared-responsibility evidence allocation

Distributed AI systems may involve multiple organizations, systems, providers, operators, or control owners. Evidence responsibility across those relationships requires additional development.

Evidence completeness, integrity, retention, and adverse-evidence persistence

Additional work is required around the completeness, integrity, preservation, retention, and continued availability of governance evidence, particularly when evidence reflects failures, interventions, exceptions, or adverse outcomes.

Validation Coverage Gap

Autonomous irreversible material financial transactions were not adequately represented in the V1.0 benchmark cohort. No validation claim is made for that authority class.

Public / Proprietary Boundary

Architecture is public. Protected implementation mechanics remain proprietary.

V1.0 publicly discloses the approved architecture concepts, methodology relationships, sanitized prototype demonstrations, benchmark methodology summary, cohort, results, scope, limitations, and selected public evidence citations.

Public disclosure of the architecture does not grant permission to reproduce or operationalize protected implementation mechanics.

Protected SBJ intellectual property includes

Scoring logic; weighting logic; certification mechanics; derivation rules; evidence sufficiency logic; detailed evidence relationships; authorization-condition logic; enforcement mappings; integrated Operational Authorization Layer implementation; Transition Authorization implementation logic; Derived Authorization implementation logic; Decision-to-Action traceability implementation mechanics; Execution Instance Identity integration mechanics; production schemas; machine-executable policy logic; control dependency logic; internal benchmark working papers; and V1.1/V2 implementation work.

Independent Evaluation Disclaimer

Solutions by Jewel independently evaluated its governance architecture against publicly documented information concerning the referenced systems and organizations. The referenced executives and organizations did not participate in, sponsor, endorse, approve, certify, or validate the SBJ methodology or benchmark. References are used solely as external evidence for independent architecture testing.

Sources & Technical References

Authoritative references and public benchmark evidence

External references inform terminology, standards context, and the public evidence used for independent architecture testing. Their inclusion does not imply sponsorship, endorsement, certification, or validation of SBJ methodology.

Standards & Governance References

Benchmark Public Evidence

Downloadable Publication Companion

SBJ AI Governance Architecture V1.0

The website is the canonical public source of truth. The PDF is a supporting publication companion.

Download SBJ AI Governance Architecture V1.0 (PDF)

Related SBJ Governance Resources

Continue into implementation-oriented public resources.

AI Agent Governance ChecklistGovernance considerations for AI agents. AI Decision Record TemplateAccountability and evidence for consequential AI governance decisions. Executive AI Governance Decision MatrixStructured executive governance decisions. Federal Continuous Authorization Control MapContinuous authorization concepts for federal environments. SYNTHESIZE™ Physical AI FrameworkSBJ Physical AI governance resource. Technical Evidence — GitHubPublic V1.0 architecture, benchmark, adversarial validation, prototypes, decision record, references, and version record.

Enterprise Advisory

Apply the architecture to your AI governance environment.

Solutions by Jewel

Original Methodology Notice

This architecture is an original methodology developed by Solutions by Jewel.

It is informed by recognized public standards, regulatory guidance, and established governance practices; however, its structure, terminology, sequencing, and presentation are proprietary to Solutions by Jewel.

It is not a reproduction, certification, or official interpretation of any external framework.

Organizations may map this architecture to applicable laws, regulations, standards, contractual requirements, and internal policies according to their own scope and obligations.