Repository files navigation

Regulated AI Starter Repository

Use this templateLicense: MIT

A GitHub template for organizing AI governance, evidence, release decisions, risk records, incident preparation, and model documentation in high-accountability environments.

The repository provides a starting structure, not a control system. Folder names, completed documents, true/false fields, or a passing CI job do not establish safety, compliance, fairness, production readiness, or release approval.

What changed from a typical starter kit

The release configuration is built around decision propositions and evidence rather than universal thresholds and sector checkboxes.

It deliberately avoids claims such as:

  • every classification system should meet the same accuracy or F1 target;
  • a single disparate-impact ratio demonstrates fairness;
  • “bias evaluation complete” proves an acceptable outcome;
  • a legal or security review can be represented meaningfully by one boolean;
  • healthcare, finance, insurance, or government systems share one fixed gate list;
  • passing unit tests and documenting rollback make a system ready to release.

The right evidence depends on the actual use, people, data, authority, consequences, obligations, and operating environment.

Start here

ArtifactUse it for
docs/how-to-use-this-template.mdadopting the repository without copying generic controls blindly
release/release-checklist.yamldefining a scoped release decision, gates, evidence, conditions, and change triggers
examples/sample-release-checklist.yamlfictional evidence-based conditional-pilot example
docs/release-config-validation.mdvalidator scope and decision-coherence rules
risk/risk-assessment-template.mdsystem-specific risk and evidence review
governance/model-inventory.mdadapting inventory fields for deployed systems and ownership
incident/incident-response-playbook.mddefining incident authority and response
model-cards/model-card-template.mdrecording system and model information where a model card is useful

Repository structure

regulated-ai/
├── docs/ adoption and validation guidance
├── examples/ fictional public-safe artifacts
├── governance/ policy, roles, inventory, and practitioner mappings
├── risk/ taxonomy, register, and assessment template
├── release/ evidence-based release decision artifacts
├── incident/ incident, escalation, and response templates
├── model-cards/ model-card starter structure
├── tools/ narrow structural and semantic validators
├── tests/ validator tests
└── .github/workflows/ CI checks for the starter artifacts

Adoption sequence

1. Define scope and authoritative sources

Before filling templates, record:

  • systems and lifecycle stages covered;
  • intended and prohibited uses;
  • user and affected populations;
  • data, model, tool, permission, and environment boundaries;
  • actual internal policies and decision authorities;
  • official legal, regulatory, standards, and contractual sources that apply;
  • information that must remain private.

Remove irrelevant directories rather than preserving them for appearance.

2. Assign decision and control ownership

Distinguish:

  • product or use-case owner;
  • technical, data, model, tool, and platform owners;
  • control owners;
  • independent reviewers;
  • release and residual-risk decision owner;
  • incident and remediation owner;
  • correction, appeal, or redress owner where relevant.

A named person without authority or resources is not effective accountability.

3. Replace the release propositions

Edit release/release-checklist.yaml. Each gate should be a question whose answer changes the scoped decision.

For example:

metadata:
project: "Fictional Catalog Assistant"version: "0.3.0-pilot"environment: "bounded-internal-pilot"decision_scope: "60 trained users; public records; read and draft tools only"decision_owner: "Fictional Pilot Sponsor"evidence_cutoff: "2026-09-30"decision:
outcome: "release_with_conditions"blockers: []required_actions:
- "Complete the confirmatory legacy-record sample before expansion."conditions:
- "Keep source-system tools read-only through the pilot."evidence_gaps:
- "Rare legacy records remain under-sampled."residual_risks:
- "Staff may over-trust fluent draft rationales."gates:
- id: "AUTH-001"question: "Are identity, authorization, confirmation, and action boundaries enforced for the reviewed scope?"hard_gate: truestatus: "pass"evidence:
- "evidence/fictional-permission-test.json"owner: "Fictional Platform Owner"limitation: "Internal pilot configuration only."

The example is intentionally fictional. Replace the questions and evidence model with organization-specific requirements.

4. Validate structure and decision coherence

python -m pip install pyyaml
python -m unittest discover -s tests -v
python tools/validate_release_config.py \
release/release-checklist.yaml \
--mode template
python tools/validate_release_config.py \
examples/sample-release-checklist.yaml \
--mode ready

The validator checks structure and a small set of rules:

  • no release with blockers or unresolved hard gates;
  • no unconditional release with conditions or required actions;
  • conditional release has a condition or required action;
  • pass / not-applicable gates cite evidence;
  • not-applicable gates include scoped rationale;
  • deferred decisions identify evidence gaps;
  • identifiers are unique and required fields are populated.

It does not verify the evidence, gate selection, risk acceptance, or legal and operational sufficiency.

5. Connect the artifacts

The repository becomes useful when records reference each other:

inventory and context
↓
risk and impact assessment
↓
evaluation and control evidence
↓
release decision and conditions
↓
monitoring, incident, correction, and review
↓
change, renewal, rollback, or retirement

Avoid duplicating evidence into several documents without provenance. Link the authoritative record and state its version and freshness.

6. Define change triggers

A decision should be revisited after material changes to:

  • model, provider, prompt, routing, retrieval, or policy;
  • data, population, language, geography, or use case;
  • tools, permissions, identities, and external action authority;
  • infrastructure, region, or supplier;
  • evaluator, threshold, rubric, or test set;
  • applicable policy or obligation;
  • incidents and newly discovered failure classes;
  • conditions, exceptions, or owner authority.

Relationship to NIST AI RMF

The directory structure and templates may support internal work related to Govern, Map, Measure, and Manage. They do not implement those functions by existing.

FunctionPossible repository supportEvidence still required outside the template
Governownership, policy, inventory, decisionsactual authority, operating process, competence, culture
Mapcontext and risk templatesvalidated system and affected-population analysis
Measureevidence and gate recordsvalid evaluations, control tests, uncertainty, outcomes
Managedecision, incident, rollback, and follow-througheffective treatment, response capability, accepted residual risk

Use official NIST sources as authoritative and verify practitioner mappings.

Publication safety

A public copy must not contain real:

  • customer, patient, employee, applicant, citizen, or user data;
  • confidential vendor, pricing, contract, architecture, roadmap, prompt, or model result;
  • internal decision rights, approval chains, risk assessments, incidents, logs, endpoints, credentials, or tool manifests;
  • sensitive regulatory or legal advice;
  • examples derived from internal work by changing only names.

Use invented organizations, .example or .test contact domains, synthetic data, and scenarios clearly separate from employment history.

Quality standard

A useful customization should add:

  • a decision-relevant proposition;
  • traceable evidence and limitations;
  • an accountable owner with authority;
  • explicit condition, exception, or residual-risk semantics;
  • an enforceable monitoring or stop trigger;
  • a tested incident, containment, rollback, correction, or retirement path;
  • an organization-specific source or requirement.

Avoid adding a document or checkbox merely because it is common in governance repositories.

Maturity and scope

This is a starter template with working structural and decision-coherence validation. It is not a governance platform, formal control library, compliance assessment, legal opinion, safety case, or release authority.

Related repositories

RepositoryDistinct role
governance-playbookenterprise governance operating model
release-governancerelease evidence and decision semantics
release-checklistpackaged configuration validator
nist-rmf-guidepractitioner navigation and evidence planning
agent-evalevaluation validity and reporting

Maintained by Sima Bagheri.

About

A starter repository for documenting, reviewing, and releasing regulated AI systems.

Topics

Resources

Code of conduct

Contributing

Security policy

Stars

1 star

Watchers

0 watching

Forks

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
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Repository files navigation

Regulated AI Starter Repository

Use this templateLicense: MIT

A GitHub template for organizing AI governance, evidence, release decisions, risk records, incident preparation, and model documentation in high-accountability environments.

The repository provides a starting structure, not a control system. Folder names, completed documents, true/false fields, or a passing CI job do not establish safety, compliance, fairness, production readiness, or release approval.

What changed from a typical starter kit

The release configuration is built around decision propositions and evidence rather than universal thresholds and sector checkboxes.

It deliberately avoids claims such as:

  • every classification system should meet the same accuracy or F1 target;
  • a single disparate-impact ratio demonstrates fairness;
  • “bias evaluation complete” proves an acceptable outcome;
  • a legal or security review can be represented meaningfully by one boolean;
  • healthcare, finance, insurance, or government systems share one fixed gate list;
  • passing unit tests and documenting rollback make a system ready to release.

The right evidence depends on the actual use, people, data, authority, consequences, obligations, and operating environment.

Start here

ArtifactUse it for
docs/how-to-use-this-template.mdadopting the repository without copying generic controls blindly
release/release-checklist.yamldefining a scoped release decision, gates, evidence, conditions, and change triggers
examples/sample-release-checklist.yamlfictional evidence-based conditional-pilot example
docs/release-config-validation.mdvalidator scope and decision-coherence rules
risk/risk-assessment-template.mdsystem-specific risk and evidence review
governance/model-inventory.mdadapting inventory fields for deployed systems and ownership
incident/incident-response-playbook.mddefining incident authority and response
model-cards/model-card-template.mdrecording system and model information where a model card is useful

Repository structure

regulated-ai/
├── docs/ adoption and validation guidance
├── examples/ fictional public-safe artifacts
├── governance/ policy, roles, inventory, and practitioner mappings
├── risk/ taxonomy, register, and assessment template
├── release/ evidence-based release decision artifacts
├── incident/ incident, escalation, and response templates
├── model-cards/ model-card starter structure
├── tools/ narrow structural and semantic validators
├── tests/ validator tests
└── .github/workflows/ CI checks for the starter artifacts

Adoption sequence

1. Define scope and authoritative sources

Before filling templates, record:

  • systems and lifecycle stages covered;
  • intended and prohibited uses;
  • user and affected populations;
  • data, model, tool, permission, and environment boundaries;
  • actual internal policies and decision authorities;
  • official legal, regulatory, standards, and contractual sources that apply;
  • information that must remain private.

Remove irrelevant directories rather than preserving them for appearance.

2. Assign decision and control ownership

Distinguish:

  • product or use-case owner;
  • technical, data, model, tool, and platform owners;
  • control owners;
  • independent reviewers;
  • release and residual-risk decision owner;
  • incident and remediation owner;
  • correction, appeal, or redress owner where relevant.

A named person without authority or resources is not effective accountability.

3. Replace the release propositions

Edit release/release-checklist.yaml. Each gate should be a question whose answer changes the scoped decision.

For example:

metadata:
project: "Fictional Catalog Assistant"version: "0.3.0-pilot"environment: "bounded-internal-pilot"decision_scope: "60 trained users; public records; read and draft tools only"decision_owner: "Fictional Pilot Sponsor"evidence_cutoff: "2026-09-30"decision:
outcome: "release_with_conditions"blockers: []required_actions:
- "Complete the confirmatory legacy-record sample before expansion."conditions:
- "Keep source-system tools read-only through the pilot."evidence_gaps:
- "Rare legacy records remain under-sampled."residual_risks:
- "Staff may over-trust fluent draft rationales."gates:
- id: "AUTH-001"question: "Are identity, authorization, confirmation, and action boundaries enforced for the reviewed scope?"hard_gate: truestatus: "pass"evidence:
- "evidence/fictional-permission-test.json"owner: "Fictional Platform Owner"limitation: "Internal pilot configuration only."

The example is intentionally fictional. Replace the questions and evidence model with organization-specific requirements.

4. Validate structure and decision coherence

python -m pip install pyyaml
python -m unittest discover -s tests -v
python tools/validate_release_config.py \
release/release-checklist.yaml \
--mode template
python tools/validate_release_config.py \
examples/sample-release-checklist.yaml \
--mode ready

The validator checks structure and a small set of rules:

  • no release with blockers or unresolved hard gates;
  • no unconditional release with conditions or required actions;
  • conditional release has a condition or required action;
  • pass / not-applicable gates cite evidence;
  • not-applicable gates include scoped rationale;
  • deferred decisions identify evidence gaps;
  • identifiers are unique and required fields are populated.

It does not verify the evidence, gate selection, risk acceptance, or legal and operational sufficiency.

5. Connect the artifacts

The repository becomes useful when records reference each other:

inventory and context
↓
risk and impact assessment
↓
evaluation and control evidence
↓
release decision and conditions
↓
monitoring, incident, correction, and review
↓
change, renewal, rollback, or retirement

Avoid duplicating evidence into several documents without provenance. Link the authoritative record and state its version and freshness.

6. Define change triggers

A decision should be revisited after material changes to:

  • model, provider, prompt, routing, retrieval, or policy;
  • data, population, language, geography, or use case;
  • tools, permissions, identities, and external action authority;
  • infrastructure, region, or supplier;
  • evaluator, threshold, rubric, or test set;
  • applicable policy or obligation;
  • incidents and newly discovered failure classes;
  • conditions, exceptions, or owner authority.

Relationship to NIST AI RMF

The directory structure and templates may support internal work related to Govern, Map, Measure, and Manage. They do not implement those functions by existing.

FunctionPossible repository supportEvidence still required outside the template
Governownership, policy, inventory, decisionsactual authority, operating process, competence, culture
Mapcontext and risk templatesvalidated system and affected-population analysis
Measureevidence and gate recordsvalid evaluations, control tests, uncertainty, outcomes
Managedecision, incident, rollback, and follow-througheffective treatment, response capability, accepted residual risk

Use official NIST sources as authoritative and verify practitioner mappings.

Publication safety

A public copy must not contain real:

  • customer, patient, employee, applicant, citizen, or user data;
  • confidential vendor, pricing, contract, architecture, roadmap, prompt, or model result;
  • internal decision rights, approval chains, risk assessments, incidents, logs, endpoints, credentials, or tool manifests;
  • sensitive regulatory or legal advice;
  • examples derived from internal work by changing only names.

Use invented organizations, .example or .test contact domains, synthetic data, and scenarios clearly separate from employment history.

Quality standard

A useful customization should add:

  • a decision-relevant proposition;
  • traceable evidence and limitations;
  • an accountable owner with authority;
  • explicit condition, exception, or residual-risk semantics;
  • an enforceable monitoring or stop trigger;
  • a tested incident, containment, rollback, correction, or retirement path;
  • an organization-specific source or requirement.

Avoid adding a document or checkbox merely because it is common in governance repositories.

Maturity and scope

This is a starter template with working structural and decision-coherence validation. It is not a governance platform, formal control library, compliance assessment, legal opinion, safety case, or release authority.

Related repositories

RepositoryDistinct role
governance-playbookenterprise governance operating model
release-governancerelease evidence and decision semantics
release-checklistpackaged configuration validator
nist-rmf-guidepractitioner navigation and evidence planning
agent-evalevaluation validity and reporting

Maintained by Sima Bagheri.

About

A starter repository for documenting, reviewing, and releasing regulated AI systems.

Topics

Resources

Code of conduct

Contributing

Security policy

Stars

1 star

Watchers

0 watching

Forks

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix"); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Repository files navigation

Regulated AI Starter Repository

Use this templateLicense: MIT

A GitHub template for organizing AI governance, evidence, release decisions, risk records, incident preparation, and model documentation in high-accountability environments.

The repository provides a starting structure, not a control system. Folder names, completed documents, true/false fields, or a passing CI job do not establish safety, compliance, fairness, production readiness, or release approval.

What changed from a typical starter kit

The release configuration is built around decision propositions and evidence rather than universal thresholds and sector checkboxes.

It deliberately avoids claims such as:

  • every classification system should meet the same accuracy or F1 target;
  • a single disparate-impact ratio demonstrates fairness;
  • “bias evaluation complete” proves an acceptable outcome;
  • a legal or security review can be represented meaningfully by one boolean;
  • healthcare, finance, insurance, or government systems share one fixed gate list;
  • passing unit tests and documenting rollback make a system ready to release.

The right evidence depends on the actual use, people, data, authority, consequences, obligations, and operating environment.

Start here

ArtifactUse it for
docs/how-to-use-this-template.mdadopting the repository without copying generic controls blindly
release/release-checklist.yamldefining a scoped release decision, gates, evidence, conditions, and change triggers
examples/sample-release-checklist.yamlfictional evidence-based conditional-pilot example
docs/release-config-validation.mdvalidator scope and decision-coherence rules
risk/risk-assessment-template.mdsystem-specific risk and evidence review
governance/model-inventory.mdadapting inventory fields for deployed systems and ownership
incident/incident-response-playbook.mddefining incident authority and response
model-cards/model-card-template.mdrecording system and model information where a model card is useful

Repository structure

regulated-ai/
├── docs/ adoption and validation guidance
├── examples/ fictional public-safe artifacts
├── governance/ policy, roles, inventory, and practitioner mappings
├── risk/ taxonomy, register, and assessment template
├── release/ evidence-based release decision artifacts
├── incident/ incident, escalation, and response templates
├── model-cards/ model-card starter structure
├── tools/ narrow structural and semantic validators
├── tests/ validator tests
└── .github/workflows/ CI checks for the starter artifacts

Adoption sequence

1. Define scope and authoritative sources

Before filling templates, record:

  • systems and lifecycle stages covered;
  • intended and prohibited uses;
  • user and affected populations;
  • data, model, tool, permission, and environment boundaries;
  • actual internal policies and decision authorities;
  • official legal, regulatory, standards, and contractual sources that apply;
  • information that must remain private.

Remove irrelevant directories rather than preserving them for appearance.

2. Assign decision and control ownership

Distinguish:

  • product or use-case owner;
  • technical, data, model, tool, and platform owners;
  • control owners;
  • independent reviewers;
  • release and residual-risk decision owner;
  • incident and remediation owner;
  • correction, appeal, or redress owner where relevant.

A named person without authority or resources is not effective accountability.

3. Replace the release propositions

Edit release/release-checklist.yaml. Each gate should be a question whose answer changes the scoped decision.

For example:

metadata:
project: "Fictional Catalog Assistant"version: "0.3.0-pilot"environment: "bounded-internal-pilot"decision_scope: "60 trained users; public records; read and draft tools only"decision_owner: "Fictional Pilot Sponsor"evidence_cutoff: "2026-09-30"decision:
outcome: "release_with_conditions"blockers: []required_actions:
- "Complete the confirmatory legacy-record sample before expansion."conditions:
- "Keep source-system tools read-only through the pilot."evidence_gaps:
- "Rare legacy records remain under-sampled."residual_risks:
- "Staff may over-trust fluent draft rationales."gates:
- id: "AUTH-001"question: "Are identity, authorization, confirmation, and action boundaries enforced for the reviewed scope?"hard_gate: truestatus: "pass"evidence:
- "evidence/fictional-permission-test.json"owner: "Fictional Platform Owner"limitation: "Internal pilot configuration only."

The example is intentionally fictional. Replace the questions and evidence model with organization-specific requirements.

4. Validate structure and decision coherence

python -m pip install pyyaml
python -m unittest discover -s tests -v
python tools/validate_release_config.py \
release/release-checklist.yaml \
--mode template
python tools/validate_release_config.py \
examples/sample-release-checklist.yaml \
--mode ready

The validator checks structure and a small set of rules:

  • no release with blockers or unresolved hard gates;
  • no unconditional release with conditions or required actions;
  • conditional release has a condition or required action;
  • pass / not-applicable gates cite evidence;
  • not-applicable gates include scoped rationale;
  • deferred decisions identify evidence gaps;
  • identifiers are unique and required fields are populated.

It does not verify the evidence, gate selection, risk acceptance, or legal and operational sufficiency.

5. Connect the artifacts

The repository becomes useful when records reference each other:

inventory and context
↓
risk and impact assessment
↓
evaluation and control evidence
↓
release decision and conditions
↓
monitoring, incident, correction, and review
↓
change, renewal, rollback, or retirement

Avoid duplicating evidence into several documents without provenance. Link the authoritative record and state its version and freshness.

6. Define change triggers

A decision should be revisited after material changes to:

  • model, provider, prompt, routing, retrieval, or policy;
  • data, population, language, geography, or use case;
  • tools, permissions, identities, and external action authority;
  • infrastructure, region, or supplier;
  • evaluator, threshold, rubric, or test set;
  • applicable policy or obligation;
  • incidents and newly discovered failure classes;
  • conditions, exceptions, or owner authority.

Relationship to NIST AI RMF

The directory structure and templates may support internal work related to Govern, Map, Measure, and Manage. They do not implement those functions by existing.

FunctionPossible repository supportEvidence still required outside the template
Governownership, policy, inventory, decisionsactual authority, operating process, competence, culture
Mapcontext and risk templatesvalidated system and affected-population analysis
Measureevidence and gate recordsvalid evaluations, control tests, uncertainty, outcomes
Managedecision, incident, rollback, and follow-througheffective treatment, response capability, accepted residual risk

Use official NIST sources as authoritative and verify practitioner mappings.

Publication safety

A public copy must not contain real:

  • customer, patient, employee, applicant, citizen, or user data;
  • confidential vendor, pricing, contract, architecture, roadmap, prompt, or model result;
  • internal decision rights, approval chains, risk assessments, incidents, logs, endpoints, credentials, or tool manifests;
  • sensitive regulatory or legal advice;
  • examples derived from internal work by changing only names.

Use invented organizations, .example or .test contact domains, synthetic data, and scenarios clearly separate from employment history.

Quality standard

A useful customization should add:

  • a decision-relevant proposition;
  • traceable evidence and limitations;
  • an accountable owner with authority;
  • explicit condition, exception, or residual-risk semantics;
  • an enforceable monitoring or stop trigger;
  • a tested incident, containment, rollback, correction, or retirement path;
  • an organization-specific source or requirement.

Avoid adding a document or checkbox merely because it is common in governance repositories.

Maturity and scope

This is a starter template with working structural and decision-coherence validation. It is not a governance platform, formal control library, compliance assessment, legal opinion, safety case, or release authority.

Related repositories

RepositoryDistinct role
governance-playbookenterprise governance operating model
release-governancerelease evidence and decision semantics
release-checklistpackaged configuration validator
nist-rmf-guidepractitioner navigation and evidence planning
agent-evalevaluation validity and reporting

Maintained by Sima Bagheri.

About

A starter repository for documenting, reviewing, and releasing regulated AI systems.

Topics

Resources

Code of conduct

Contributing

Security policy

Stars

1 star

Watchers

0 watching

Forks

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms"); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
Skip to content

Repository files navigation

Regulated AI Starter Repository

Use this templateLicense: MIT

A GitHub template for organizing AI governance, evidence, release decisions, risk records, incident preparation, and model documentation in high-accountability environments.

The repository provides a starting structure, not a control system. Folder names, completed documents, true/false fields, or a passing CI job do not establish safety, compliance, fairness, production readiness, or release approval.

What changed from a typical starter kit

The release configuration is built around decision propositions and evidence rather than universal thresholds and sector checkboxes.

It deliberately avoids claims such as:

  • every classification system should meet the same accuracy or F1 target;
  • a single disparate-impact ratio demonstrates fairness;
  • “bias evaluation complete” proves an acceptable outcome;
  • a legal or security review can be represented meaningfully by one boolean;
  • healthcare, finance, insurance, or government systems share one fixed gate list;
  • passing unit tests and documenting rollback make a system ready to release.

The right evidence depends on the actual use, people, data, authority, consequences, obligations, and operating environment.

Start here

ArtifactUse it for
docs/how-to-use-this-template.mdadopting the repository without copying generic controls blindly
release/release-checklist.yamldefining a scoped release decision, gates, evidence, conditions, and change triggers
examples/sample-release-checklist.yamlfictional evidence-based conditional-pilot example
docs/release-config-validation.mdvalidator scope and decision-coherence rules
risk/risk-assessment-template.mdsystem-specific risk and evidence review
governance/model-inventory.mdadapting inventory fields for deployed systems and ownership
incident/incident-response-playbook.mddefining incident authority and response
model-cards/model-card-template.mdrecording system and model information where a model card is useful

Repository structure

regulated-ai/
├── docs/ adoption and validation guidance
├── examples/ fictional public-safe artifacts
├── governance/ policy, roles, inventory, and practitioner mappings
├── risk/ taxonomy, register, and assessment template
├── release/ evidence-based release decision artifacts
├── incident/ incident, escalation, and response templates
├── model-cards/ model-card starter structure
├── tools/ narrow structural and semantic validators
├── tests/ validator tests
└── .github/workflows/ CI checks for the starter artifacts

Adoption sequence

1. Define scope and authoritative sources

Before filling templates, record:

  • systems and lifecycle stages covered;
  • intended and prohibited uses;
  • user and affected populations;
  • data, model, tool, permission, and environment boundaries;
  • actual internal policies and decision authorities;
  • official legal, regulatory, standards, and contractual sources that apply;
  • information that must remain private.

Remove irrelevant directories rather than preserving them for appearance.

2. Assign decision and control ownership

Distinguish:

  • product or use-case owner;
  • technical, data, model, tool, and platform owners;
  • control owners;
  • independent reviewers;
  • release and residual-risk decision owner;
  • incident and remediation owner;
  • correction, appeal, or redress owner where relevant.

A named person without authority or resources is not effective accountability.

3. Replace the release propositions

Edit release/release-checklist.yaml. Each gate should be a question whose answer changes the scoped decision.

For example:

metadata:
project: "Fictional Catalog Assistant"version: "0.3.0-pilot"environment: "bounded-internal-pilot"decision_scope: "60 trained users; public records; read and draft tools only"decision_owner: "Fictional Pilot Sponsor"evidence_cutoff: "2026-09-30"decision:
outcome: "release_with_conditions"blockers: []required_actions:
- "Complete the confirmatory legacy-record sample before expansion."conditions:
- "Keep source-system tools read-only through the pilot."evidence_gaps:
- "Rare legacy records remain under-sampled."residual_risks:
- "Staff may over-trust fluent draft rationales."gates:
- id: "AUTH-001"question: "Are identity, authorization, confirmation, and action boundaries enforced for the reviewed scope?"hard_gate: truestatus: "pass"evidence:
- "evidence/fictional-permission-test.json"owner: "Fictional Platform Owner"limitation: "Internal pilot configuration only."

The example is intentionally fictional. Replace the questions and evidence model with organization-specific requirements.

4. Validate structure and decision coherence

python -m pip install pyyaml
python -m unittest discover -s tests -v
python tools/validate_release_config.py \
release/release-checklist.yaml \
--mode template
python tools/validate_release_config.py \
examples/sample-release-checklist.yaml \
--mode ready

The validator checks structure and a small set of rules:

  • no release with blockers or unresolved hard gates;
  • no unconditional release with conditions or required actions;
  • conditional release has a condition or required action;
  • pass / not-applicable gates cite evidence;
  • not-applicable gates include scoped rationale;
  • deferred decisions identify evidence gaps;
  • identifiers are unique and required fields are populated.

It does not verify the evidence, gate selection, risk acceptance, or legal and operational sufficiency.

5. Connect the artifacts

The repository becomes useful when records reference each other:

inventory and context
↓
risk and impact assessment
↓
evaluation and control evidence
↓
release decision and conditions
↓
monitoring, incident, correction, and review
↓
change, renewal, rollback, or retirement

Avoid duplicating evidence into several documents without provenance. Link the authoritative record and state its version and freshness.

6. Define change triggers

A decision should be revisited after material changes to:

  • model, provider, prompt, routing, retrieval, or policy;
  • data, population, language, geography, or use case;
  • tools, permissions, identities, and external action authority;
  • infrastructure, region, or supplier;
  • evaluator, threshold, rubric, or test set;
  • applicable policy or obligation;
  • incidents and newly discovered failure classes;
  • conditions, exceptions, or owner authority.

Relationship to NIST AI RMF

The directory structure and templates may support internal work related to Govern, Map, Measure, and Manage. They do not implement those functions by existing.

FunctionPossible repository supportEvidence still required outside the template
Governownership, policy, inventory, decisionsactual authority, operating process, competence, culture
Mapcontext and risk templatesvalidated system and affected-population analysis
Measureevidence and gate recordsvalid evaluations, control tests, uncertainty, outcomes
Managedecision, incident, rollback, and follow-througheffective treatment, response capability, accepted residual risk

Use official NIST sources as authoritative and verify practitioner mappings.

Publication safety

A public copy must not contain real:

  • customer, patient, employee, applicant, citizen, or user data;
  • confidential vendor, pricing, contract, architecture, roadmap, prompt, or model result;
  • internal decision rights, approval chains, risk assessments, incidents, logs, endpoints, credentials, or tool manifests;
  • sensitive regulatory or legal advice;
  • examples derived from internal work by changing only names.

Use invented organizations, .example or .test contact domains, synthetic data, and scenarios clearly separate from employment history.

Quality standard

A useful customization should add:

  • a decision-relevant proposition;
  • traceable evidence and limitations;
  • an accountable owner with authority;
  • explicit condition, exception, or residual-risk semantics;
  • an enforceable monitoring or stop trigger;
  • a tested incident, containment, rollback, correction, or retirement path;
  • an organization-specific source or requirement.

Avoid adding a document or checkbox merely because it is common in governance repositories.

Maturity and scope

This is a starter template with working structural and decision-coherence validation. It is not a governance platform, formal control library, compliance assessment, legal opinion, safety case, or release authority.

Related repositories

RepositoryDistinct role
governance-playbookenterprise governance operating model
release-governancerelease evidence and decision semantics
release-checklistpackaged configuration validator
nist-rmf-guidepractitioner navigation and evidence planning
agent-evalevaluation validity and reporting

Maintained by Sima Bagheri.

About

A starter repository for documenting, reviewing, and releasing regulated AI systems.

Topics

Resources

Code of conduct

Contributing

Security policy

Stars

1 star

Watchers

0 watching

Forks

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links"); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + '
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Regulated AI Starter Repository

Use this templateLicense: MIT

A GitHub template for organizing AI governance, evidence, release decisions, risk records, incident preparation, and model documentation in high-accountability environments.

The repository provides a starting structure, not a control system. Folder names, completed documents, true/false fields, or a passing CI job do not establish safety, compliance, fairness, production readiness, or release approval.

What changed from a typical starter kit

The release configuration is built around decision propositions and evidence rather than universal thresholds and sector checkboxes.

It deliberately avoids claims such as:

  • every classification system should meet the same accuracy or F1 target;
  • a single disparate-impact ratio demonstrates fairness;
  • “bias evaluation complete” proves an acceptable outcome;
  • a legal or security review can be represented meaningfully by one boolean;
  • healthcare, finance, insurance, or government systems share one fixed gate list;
  • passing unit tests and documenting rollback make a system ready to release.

The right evidence depends on the actual use, people, data, authority, consequences, obligations, and operating environment.

Start here

ArtifactUse it for
docs/how-to-use-this-template.mdadopting the repository without copying generic controls blindly
release/release-checklist.yamldefining a scoped release decision, gates, evidence, conditions, and change triggers
examples/sample-release-checklist.yamlfictional evidence-based conditional-pilot example
docs/release-config-validation.mdvalidator scope and decision-coherence rules
risk/risk-assessment-template.mdsystem-specific risk and evidence review
governance/model-inventory.mdadapting inventory fields for deployed systems and ownership
incident/incident-response-playbook.mddefining incident authority and response
model-cards/model-card-template.mdrecording system and model information where a model card is useful

Repository structure

regulated-ai/
├── docs/ adoption and validation guidance
├── examples/ fictional public-safe artifacts
├── governance/ policy, roles, inventory, and practitioner mappings
├── risk/ taxonomy, register, and assessment template
├── release/ evidence-based release decision artifacts
├── incident/ incident, escalation, and response templates
├── model-cards/ model-card starter structure
├── tools/ narrow structural and semantic validators
├── tests/ validator tests
└── .github/workflows/ CI checks for the starter artifacts

Adoption sequence

1. Define scope and authoritative sources

Before filling templates, record:

  • systems and lifecycle stages covered;
  • intended and prohibited uses;
  • user and affected populations;
  • data, model, tool, permission, and environment boundaries;
  • actual internal policies and decision authorities;
  • official legal, regulatory, standards, and contractual sources that apply;
  • information that must remain private.

Remove irrelevant directories rather than preserving them for appearance.

2. Assign decision and control ownership

Distinguish:

  • product or use-case owner;
  • technical, data, model, tool, and platform owners;
  • control owners;
  • independent reviewers;
  • release and residual-risk decision owner;
  • incident and remediation owner;
  • correction, appeal, or redress owner where relevant.

A named person without authority or resources is not effective accountability.

3. Replace the release propositions

Edit release/release-checklist.yaml. Each gate should be a question whose answer changes the scoped decision.

For example:

metadata:
project: "Fictional Catalog Assistant"version: "0.3.0-pilot"environment: "bounded-internal-pilot"decision_scope: "60 trained users; public records; read and draft tools only"decision_owner: "Fictional Pilot Sponsor"evidence_cutoff: "2026-09-30"decision:
outcome: "release_with_conditions"blockers: []required_actions:
- "Complete the confirmatory legacy-record sample before expansion."conditions:
- "Keep source-system tools read-only through the pilot."evidence_gaps:
- "Rare legacy records remain under-sampled."residual_risks:
- "Staff may over-trust fluent draft rationales."gates:
- id: "AUTH-001"question: "Are identity, authorization, confirmation, and action boundaries enforced for the reviewed scope?"hard_gate: truestatus: "pass"evidence:
- "evidence/fictional-permission-test.json"owner: "Fictional Platform Owner"limitation: "Internal pilot configuration only."

The example is intentionally fictional. Replace the questions and evidence model with organization-specific requirements.

4. Validate structure and decision coherence

python -m pip install pyyaml
python -m unittest discover -s tests -v
python tools/validate_release_config.py \
release/release-checklist.yaml \
--mode template
python tools/validate_release_config.py \
examples/sample-release-checklist.yaml \
--mode ready

The validator checks structure and a small set of rules:

  • no release with blockers or unresolved hard gates;
  • no unconditional release with conditions or required actions;
  • conditional release has a condition or required action;
  • pass / not-applicable gates cite evidence;
  • not-applicable gates include scoped rationale;
  • deferred decisions identify evidence gaps;
  • identifiers are unique and required fields are populated.

It does not verify the evidence, gate selection, risk acceptance, or legal and operational sufficiency.

5. Connect the artifacts

The repository becomes useful when records reference each other:

inventory and context
↓
risk and impact assessment
↓
evaluation and control evidence
↓
release decision and conditions
↓
monitoring, incident, correction, and review
↓
change, renewal, rollback, or retirement

Avoid duplicating evidence into several documents without provenance. Link the authoritative record and state its version and freshness.

6. Define change triggers

A decision should be revisited after material changes to:

  • model, provider, prompt, routing, retrieval, or policy;
  • data, population, language, geography, or use case;
  • tools, permissions, identities, and external action authority;
  • infrastructure, region, or supplier;
  • evaluator, threshold, rubric, or test set;
  • applicable policy or obligation;
  • incidents and newly discovered failure classes;
  • conditions, exceptions, or owner authority.

Relationship to NIST AI RMF

The directory structure and templates may support internal work related to Govern, Map, Measure, and Manage. They do not implement those functions by existing.

FunctionPossible repository supportEvidence still required outside the template
Governownership, policy, inventory, decisionsactual authority, operating process, competence, culture
Mapcontext and risk templatesvalidated system and affected-population analysis
Measureevidence and gate recordsvalid evaluations, control tests, uncertainty, outcomes
Managedecision, incident, rollback, and follow-througheffective treatment, response capability, accepted residual risk

Use official NIST sources as authoritative and verify practitioner mappings.

Publication safety

A public copy must not contain real:

  • customer, patient, employee, applicant, citizen, or user data;
  • confidential vendor, pricing, contract, architecture, roadmap, prompt, or model result;
  • internal decision rights, approval chains, risk assessments, incidents, logs, endpoints, credentials, or tool manifests;
  • sensitive regulatory or legal advice;
  • examples derived from internal work by changing only names.

Use invented organizations, .example or .test contact domains, synthetic data, and scenarios clearly separate from employment history.

Quality standard

A useful customization should add:

  • a decision-relevant proposition;
  • traceable evidence and limitations;
  • an accountable owner with authority;
  • explicit condition, exception, or residual-risk semantics;
  • an enforceable monitoring or stop trigger;
  • a tested incident, containment, rollback, correction, or retirement path;
  • an organization-specific source or requirement.

Avoid adding a document or checkbox merely because it is common in governance repositories.

Maturity and scope

This is a starter template with working structural and decision-coherence validation. It is not a governance platform, formal control library, compliance assessment, legal opinion, safety case, or release authority.

Related repositories

RepositoryDistinct role
governance-playbookenterprise governance operating model
release-governancerelease evidence and decision semantics
release-checklistpackaged configuration validator
nist-rmf-guidepractitioner navigation and evidence planning
agent-evalevaluation validity and reporting

Maintained by Sima Bagheri.

About

A starter repository for documenting, reviewing, and releasing regulated AI systems.

Topics

Resources

Code of conduct

Contributing

Security policy

Stars

1 star

Watchers

0 watching

Forks

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default"); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Repository files navigation

Regulated AI Starter Repository

Use this templateLicense: MIT

A GitHub template for organizing AI governance, evidence, release decisions, risk records, incident preparation, and model documentation in high-accountability environments.

The repository provides a starting structure, not a control system. Folder names, completed documents, true/false fields, or a passing CI job do not establish safety, compliance, fairness, production readiness, or release approval.

What changed from a typical starter kit

The release configuration is built around decision propositions and evidence rather than universal thresholds and sector checkboxes.

It deliberately avoids claims such as:

  • every classification system should meet the same accuracy or F1 target;
  • a single disparate-impact ratio demonstrates fairness;
  • “bias evaluation complete” proves an acceptable outcome;
  • a legal or security review can be represented meaningfully by one boolean;
  • healthcare, finance, insurance, or government systems share one fixed gate list;
  • passing unit tests and documenting rollback make a system ready to release.

The right evidence depends on the actual use, people, data, authority, consequences, obligations, and operating environment.

Start here

ArtifactUse it for
docs/how-to-use-this-template.mdadopting the repository without copying generic controls blindly
release/release-checklist.yamldefining a scoped release decision, gates, evidence, conditions, and change triggers
examples/sample-release-checklist.yamlfictional evidence-based conditional-pilot example
docs/release-config-validation.mdvalidator scope and decision-coherence rules
risk/risk-assessment-template.mdsystem-specific risk and evidence review
governance/model-inventory.mdadapting inventory fields for deployed systems and ownership
incident/incident-response-playbook.mddefining incident authority and response
model-cards/model-card-template.mdrecording system and model information where a model card is useful

Repository structure

regulated-ai/
├── docs/ adoption and validation guidance
├── examples/ fictional public-safe artifacts
├── governance/ policy, roles, inventory, and practitioner mappings
├── risk/ taxonomy, register, and assessment template
├── release/ evidence-based release decision artifacts
├── incident/ incident, escalation, and response templates
├── model-cards/ model-card starter structure
├── tools/ narrow structural and semantic validators
├── tests/ validator tests
└── .github/workflows/ CI checks for the starter artifacts

Adoption sequence

1. Define scope and authoritative sources

Before filling templates, record:

  • systems and lifecycle stages covered;
  • intended and prohibited uses;
  • user and affected populations;
  • data, model, tool, permission, and environment boundaries;
  • actual internal policies and decision authorities;
  • official legal, regulatory, standards, and contractual sources that apply;
  • information that must remain private.

Remove irrelevant directories rather than preserving them for appearance.

2. Assign decision and control ownership

Distinguish:

  • product or use-case owner;
  • technical, data, model, tool, and platform owners;
  • control owners;
  • independent reviewers;
  • release and residual-risk decision owner;
  • incident and remediation owner;
  • correction, appeal, or redress owner where relevant.

A named person without authority or resources is not effective accountability.

3. Replace the release propositions

Edit release/release-checklist.yaml. Each gate should be a question whose answer changes the scoped decision.

For example:

metadata:
project: "Fictional Catalog Assistant"version: "0.3.0-pilot"environment: "bounded-internal-pilot"decision_scope: "60 trained users; public records; read and draft tools only"decision_owner: "Fictional Pilot Sponsor"evidence_cutoff: "2026-09-30"decision:
outcome: "release_with_conditions"blockers: []required_actions:
- "Complete the confirmatory legacy-record sample before expansion."conditions:
- "Keep source-system tools read-only through the pilot."evidence_gaps:
- "Rare legacy records remain under-sampled."residual_risks:
- "Staff may over-trust fluent draft rationales."gates:
- id: "AUTH-001"question: "Are identity, authorization, confirmation, and action boundaries enforced for the reviewed scope?"hard_gate: truestatus: "pass"evidence:
- "evidence/fictional-permission-test.json"owner: "Fictional Platform Owner"limitation: "Internal pilot configuration only."

The example is intentionally fictional. Replace the questions and evidence model with organization-specific requirements.

4. Validate structure and decision coherence

python -m pip install pyyaml
python -m unittest discover -s tests -v
python tools/validate_release_config.py \
release/release-checklist.yaml \
--mode template
python tools/validate_release_config.py \
examples/sample-release-checklist.yaml \
--mode ready

The validator checks structure and a small set of rules:

  • no release with blockers or unresolved hard gates;
  • no unconditional release with conditions or required actions;
  • conditional release has a condition or required action;
  • pass / not-applicable gates cite evidence;
  • not-applicable gates include scoped rationale;
  • deferred decisions identify evidence gaps;
  • identifiers are unique and required fields are populated.

It does not verify the evidence, gate selection, risk acceptance, or legal and operational sufficiency.

5. Connect the artifacts

The repository becomes useful when records reference each other:

inventory and context
↓
risk and impact assessment
↓
evaluation and control evidence
↓
release decision and conditions
↓
monitoring, incident, correction, and review
↓
change, renewal, rollback, or retirement

Avoid duplicating evidence into several documents without provenance. Link the authoritative record and state its version and freshness.

6. Define change triggers

A decision should be revisited after material changes to:

  • model, provider, prompt, routing, retrieval, or policy;
  • data, population, language, geography, or use case;
  • tools, permissions, identities, and external action authority;
  • infrastructure, region, or supplier;
  • evaluator, threshold, rubric, or test set;
  • applicable policy or obligation;
  • incidents and newly discovered failure classes;
  • conditions, exceptions, or owner authority.

Relationship to NIST AI RMF

The directory structure and templates may support internal work related to Govern, Map, Measure, and Manage. They do not implement those functions by existing.

FunctionPossible repository supportEvidence still required outside the template
Governownership, policy, inventory, decisionsactual authority, operating process, competence, culture
Mapcontext and risk templatesvalidated system and affected-population analysis
Measureevidence and gate recordsvalid evaluations, control tests, uncertainty, outcomes
Managedecision, incident, rollback, and follow-througheffective treatment, response capability, accepted residual risk

Use official NIST sources as authoritative and verify practitioner mappings.

Publication safety

A public copy must not contain real:

  • customer, patient, employee, applicant, citizen, or user data;
  • confidential vendor, pricing, contract, architecture, roadmap, prompt, or model result;
  • internal decision rights, approval chains, risk assessments, incidents, logs, endpoints, credentials, or tool manifests;
  • sensitive regulatory or legal advice;
  • examples derived from internal work by changing only names.

Use invented organizations, .example or .test contact domains, synthetic data, and scenarios clearly separate from employment history.

Quality standard

A useful customization should add:

  • a decision-relevant proposition;
  • traceable evidence and limitations;
  • an accountable owner with authority;
  • explicit condition, exception, or residual-risk semantics;
  • an enforceable monitoring or stop trigger;
  • a tested incident, containment, rollback, correction, or retirement path;
  • an organization-specific source or requirement.

Avoid adding a document or checkbox merely because it is common in governance repositories.

Maturity and scope

This is a starter template with working structural and decision-coherence validation. It is not a governance platform, formal control library, compliance assessment, legal opinion, safety case, or release authority.

Related repositories

RepositoryDistinct role
governance-playbookenterprise governance operating model
release-governancerelease evidence and decision semantics
release-checklistpackaged configuration validator
nist-rmf-guidepractitioner navigation and evidence planning
agent-evalevaluation validity and reporting

Maintained by Sima Bagheri.

About

A starter repository for documenting, reviewing, and releasing regulated AI systems.

Topics

Resources

Code of conduct

Contributing

Security policy

Stars

1 star

Watchers

0 watching

Forks

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers"); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
Skip to content

Repository files navigation

Regulated AI Starter Repository

Use this templateLicense: MIT

A GitHub template for organizing AI governance, evidence, release decisions, risk records, incident preparation, and model documentation in high-accountability environments.

The repository provides a starting structure, not a control system. Folder names, completed documents, true/false fields, or a passing CI job do not establish safety, compliance, fairness, production readiness, or release approval.

What changed from a typical starter kit

The release configuration is built around decision propositions and evidence rather than universal thresholds and sector checkboxes.

It deliberately avoids claims such as:

  • every classification system should meet the same accuracy or F1 target;
  • a single disparate-impact ratio demonstrates fairness;
  • “bias evaluation complete” proves an acceptable outcome;
  • a legal or security review can be represented meaningfully by one boolean;
  • healthcare, finance, insurance, or government systems share one fixed gate list;
  • passing unit tests and documenting rollback make a system ready to release.

The right evidence depends on the actual use, people, data, authority, consequences, obligations, and operating environment.

Start here

ArtifactUse it for
docs/how-to-use-this-template.mdadopting the repository without copying generic controls blindly
release/release-checklist.yamldefining a scoped release decision, gates, evidence, conditions, and change triggers
examples/sample-release-checklist.yamlfictional evidence-based conditional-pilot example
docs/release-config-validation.mdvalidator scope and decision-coherence rules
risk/risk-assessment-template.mdsystem-specific risk and evidence review
governance/model-inventory.mdadapting inventory fields for deployed systems and ownership
incident/incident-response-playbook.mddefining incident authority and response
model-cards/model-card-template.mdrecording system and model information where a model card is useful

Repository structure

regulated-ai/
├── docs/ adoption and validation guidance
├── examples/ fictional public-safe artifacts
├── governance/ policy, roles, inventory, and practitioner mappings
├── risk/ taxonomy, register, and assessment template
├── release/ evidence-based release decision artifacts
├── incident/ incident, escalation, and response templates
├── model-cards/ model-card starter structure
├── tools/ narrow structural and semantic validators
├── tests/ validator tests
└── .github/workflows/ CI checks for the starter artifacts

Adoption sequence

1. Define scope and authoritative sources

Before filling templates, record:

  • systems and lifecycle stages covered;
  • intended and prohibited uses;
  • user and affected populations;
  • data, model, tool, permission, and environment boundaries;
  • actual internal policies and decision authorities;
  • official legal, regulatory, standards, and contractual sources that apply;
  • information that must remain private.

Remove irrelevant directories rather than preserving them for appearance.

2. Assign decision and control ownership

Distinguish:

  • product or use-case owner;
  • technical, data, model, tool, and platform owners;
  • control owners;
  • independent reviewers;
  • release and residual-risk decision owner;
  • incident and remediation owner;
  • correction, appeal, or redress owner where relevant.

A named person without authority or resources is not effective accountability.

3. Replace the release propositions

Edit release/release-checklist.yaml. Each gate should be a question whose answer changes the scoped decision.

For example:

metadata:
project: "Fictional Catalog Assistant"version: "0.3.0-pilot"environment: "bounded-internal-pilot"decision_scope: "60 trained users; public records; read and draft tools only"decision_owner: "Fictional Pilot Sponsor"evidence_cutoff: "2026-09-30"decision:
outcome: "release_with_conditions"blockers: []required_actions:
- "Complete the confirmatory legacy-record sample before expansion."conditions:
- "Keep source-system tools read-only through the pilot."evidence_gaps:
- "Rare legacy records remain under-sampled."residual_risks:
- "Staff may over-trust fluent draft rationales."gates:
- id: "AUTH-001"question: "Are identity, authorization, confirmation, and action boundaries enforced for the reviewed scope?"hard_gate: truestatus: "pass"evidence:
- "evidence/fictional-permission-test.json"owner: "Fictional Platform Owner"limitation: "Internal pilot configuration only."

The example is intentionally fictional. Replace the questions and evidence model with organization-specific requirements.

4. Validate structure and decision coherence

python -m pip install pyyaml
python -m unittest discover -s tests -v
python tools/validate_release_config.py \
release/release-checklist.yaml \
--mode template
python tools/validate_release_config.py \
examples/sample-release-checklist.yaml \
--mode ready

The validator checks structure and a small set of rules:

  • no release with blockers or unresolved hard gates;
  • no unconditional release with conditions or required actions;
  • conditional release has a condition or required action;
  • pass / not-applicable gates cite evidence;
  • not-applicable gates include scoped rationale;
  • deferred decisions identify evidence gaps;
  • identifiers are unique and required fields are populated.

It does not verify the evidence, gate selection, risk acceptance, or legal and operational sufficiency.

5. Connect the artifacts

The repository becomes useful when records reference each other:

inventory and context
↓
risk and impact assessment
↓
evaluation and control evidence
↓
release decision and conditions
↓
monitoring, incident, correction, and review
↓
change, renewal, rollback, or retirement

Avoid duplicating evidence into several documents without provenance. Link the authoritative record and state its version and freshness.

6. Define change triggers

A decision should be revisited after material changes to:

  • model, provider, prompt, routing, retrieval, or policy;
  • data, population, language, geography, or use case;
  • tools, permissions, identities, and external action authority;
  • infrastructure, region, or supplier;
  • evaluator, threshold, rubric, or test set;
  • applicable policy or obligation;
  • incidents and newly discovered failure classes;
  • conditions, exceptions, or owner authority.

Relationship to NIST AI RMF

The directory structure and templates may support internal work related to Govern, Map, Measure, and Manage. They do not implement those functions by existing.

FunctionPossible repository supportEvidence still required outside the template
Governownership, policy, inventory, decisionsactual authority, operating process, competence, culture
Mapcontext and risk templatesvalidated system and affected-population analysis
Measureevidence and gate recordsvalid evaluations, control tests, uncertainty, outcomes
Managedecision, incident, rollback, and follow-througheffective treatment, response capability, accepted residual risk

Use official NIST sources as authoritative and verify practitioner mappings.

Publication safety

A public copy must not contain real:

  • customer, patient, employee, applicant, citizen, or user data;
  • confidential vendor, pricing, contract, architecture, roadmap, prompt, or model result;
  • internal decision rights, approval chains, risk assessments, incidents, logs, endpoints, credentials, or tool manifests;
  • sensitive regulatory or legal advice;
  • examples derived from internal work by changing only names.

Use invented organizations, .example or .test contact domains, synthetic data, and scenarios clearly separate from employment history.

Quality standard

A useful customization should add:

  • a decision-relevant proposition;
  • traceable evidence and limitations;
  • an accountable owner with authority;
  • explicit condition, exception, or residual-risk semantics;
  • an enforceable monitoring or stop trigger;
  • a tested incident, containment, rollback, correction, or retirement path;
  • an organization-specific source or requirement.

Avoid adding a document or checkbox merely because it is common in governance repositories.

Maturity and scope

This is a starter template with working structural and decision-coherence validation. It is not a governance platform, formal control library, compliance assessment, legal opinion, safety case, or release authority.

Related repositories

RepositoryDistinct role
governance-playbookenterprise governance operating model
release-governancerelease evidence and decision semantics
release-checklistpackaged configuration validator
nist-rmf-guidepractitioner navigation and evidence planning
agent-evalevaluation validity and reporting

Maintained by Sima Bagheri.

About

A starter repository for documenting, reviewing, and releasing regulated AI systems.

Topics

Resources

Code of conduct

Contributing

Security policy

Stars

1 star

Watchers

0 watching

Forks

Releases

Packages

Contributors

Languages

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Regulated AI Starter Repository

Use this templateLicense: MIT

A GitHub template for organizing AI governance, evidence, release decisions, risk records, incident preparation, and model documentation in high-accountability environments.

The repository provides a starting structure, not a control system. Folder names, completed documents, true/false fields, or a passing CI job do not establish safety, compliance, fairness, production readiness, or release approval.

What changed from a typical starter kit

The release configuration is built around decision propositions and evidence rather than universal thresholds and sector checkboxes.

It deliberately avoids claims such as:

  • every classification system should meet the same accuracy or F1 target;
  • a single disparate-impact ratio demonstrates fairness;
  • “bias evaluation complete” proves an acceptable outcome;
  • a legal or security review can be represented meaningfully by one boolean;
  • healthcare, finance, insurance, or government systems share one fixed gate list;
  • passing unit tests and documenting rollback make a system ready to release.

The right evidence depends on the actual use, people, data, authority, consequences, obligations, and operating environment.

Start here

ArtifactUse it for
docs/how-to-use-this-template.mdadopting the repository without copying generic controls blindly
release/release-checklist.yamldefining a scoped release decision, gates, evidence, conditions, and change triggers
examples/sample-release-checklist.yamlfictional evidence-based conditional-pilot example
docs/release-config-validation.mdvalidator scope and decision-coherence rules
risk/risk-assessment-template.mdsystem-specific risk and evidence review
governance/model-inventory.mdadapting inventory fields for deployed systems and ownership
incident/incident-response-playbook.mddefining incident authority and response
model-cards/model-card-template.mdrecording system and model information where a model card is useful

Repository structure

regulated-ai/
├── docs/ adoption and validation guidance
├── examples/ fictional public-safe artifacts
├── governance/ policy, roles, inventory, and practitioner mappings
├── risk/ taxonomy, register, and assessment template
├── release/ evidence-based release decision artifacts
├── incident/ incident, escalation, and response templates
├── model-cards/ model-card starter structure
├── tools/ narrow structural and semantic validators
├── tests/ validator tests
└── .github/workflows/ CI checks for the starter artifacts

Adoption sequence

1. Define scope and authoritative sources

Before filling templates, record:

  • systems and lifecycle stages covered;
  • intended and prohibited uses;
  • user and affected populations;
  • data, model, tool, permission, and environment boundaries;
  • actual internal policies and decision authorities;
  • official legal, regulatory, standards, and contractual sources that apply;
  • information that must remain private.

Remove irrelevant directories rather than preserving them for appearance.

2. Assign decision and control ownership

Distinguish:

  • product or use-case owner;
  • technical, data, model, tool, and platform owners;
  • control owners;
  • independent reviewers;
  • release and residual-risk decision owner;
  • incident and remediation owner;
  • correction, appeal, or redress owner where relevant.

A named person without authority or resources is not effective accountability.

3. Replace the release propositions

Edit release/release-checklist.yaml. Each gate should be a question whose answer changes the scoped decision.

For example:

metadata:
project: "Fictional Catalog Assistant"version: "0.3.0-pilot"environment: "bounded-internal-pilot"decision_scope: "60 trained users; public records; read and draft tools only"decision_owner: "Fictional Pilot Sponsor"evidence_cutoff: "2026-09-30"decision:
outcome: "release_with_conditions"blockers: []required_actions:
- "Complete the confirmatory legacy-record sample before expansion."conditions:
- "Keep source-system tools read-only through the pilot."evidence_gaps:
- "Rare legacy records remain under-sampled."residual_risks:
- "Staff may over-trust fluent draft rationales."gates:
- id: "AUTH-001"question: "Are identity, authorization, confirmation, and action boundaries enforced for the reviewed scope?"hard_gate: truestatus: "pass"evidence:
- "evidence/fictional-permission-test.json"owner: "Fictional Platform Owner"limitation: "Internal pilot configuration only."

The example is intentionally fictional. Replace the questions and evidence model with organization-specific requirements.

4. Validate structure and decision coherence

python -m pip install pyyaml
python -m unittest discover -s tests -v
python tools/validate_release_config.py \
release/release-checklist.yaml \
--mode template
python tools/validate_release_config.py \
examples/sample-release-checklist.yaml \
--mode ready

The validator checks structure and a small set of rules:

  • no release with blockers or unresolved hard gates;
  • no unconditional release with conditions or required actions;
  • conditional release has a condition or required action;
  • pass / not-applicable gates cite evidence;
  • not-applicable gates include scoped rationale;
  • deferred decisions identify evidence gaps;
  • identifiers are unique and required fields are populated.

It does not verify the evidence, gate selection, risk acceptance, or legal and operational sufficiency.

5. Connect the artifacts

The repository becomes useful when records reference each other:

inventory and context
↓
risk and impact assessment
↓
evaluation and control evidence
↓
release decision and conditions
↓
monitoring, incident, correction, and review
↓
change, renewal, rollback, or retirement

Avoid duplicating evidence into several documents without provenance. Link the authoritative record and state its version and freshness.

6. Define change triggers

A decision should be revisited after material changes to:

  • model, provider, prompt, routing, retrieval, or policy;
  • data, population, language, geography, or use case;
  • tools, permissions, identities, and external action authority;
  • infrastructure, region, or supplier;
  • evaluator, threshold, rubric, or test set;
  • applicable policy or obligation;
  • incidents and newly discovered failure classes;
  • conditions, exceptions, or owner authority.

Relationship to NIST AI RMF

The directory structure and templates may support internal work related to Govern, Map, Measure, and Manage. They do not implement those functions by existing.

FunctionPossible repository supportEvidence still required outside the template
Governownership, policy, inventory, decisionsactual authority, operating process, competence, culture
Mapcontext and risk templatesvalidated system and affected-population analysis
Measureevidence and gate recordsvalid evaluations, control tests, uncertainty, outcomes
Managedecision, incident, rollback, and follow-througheffective treatment, response capability, accepted residual risk

Use official NIST sources as authoritative and verify practitioner mappings.

Publication safety

A public copy must not contain real:

  • customer, patient, employee, applicant, citizen, or user data;
  • confidential vendor, pricing, contract, architecture, roadmap, prompt, or model result;
  • internal decision rights, approval chains, risk assessments, incidents, logs, endpoints, credentials, or tool manifests;
  • sensitive regulatory or legal advice;
  • examples derived from internal work by changing only names.

Use invented organizations, .example or .test contact domains, synthetic data, and scenarios clearly separate from employment history.

Quality standard

A useful customization should add:

  • a decision-relevant proposition;
  • traceable evidence and limitations;
  • an accountable owner with authority;
  • explicit condition, exception, or residual-risk semantics;
  • an enforceable monitoring or stop trigger;
  • a tested incident, containment, rollback, correction, or retirement path;
  • an organization-specific source or requirement.

Avoid adding a document or checkbox merely because it is common in governance repositories.

Maturity and scope

This is a starter template with working structural and decision-coherence validation. It is not a governance platform, formal control library, compliance assessment, legal opinion, safety case, or release authority.

Related repositories

RepositoryDistinct role
governance-playbookenterprise governance operating model
release-governancerelease evidence and decision semantics
release-checklistpackaged configuration validator
nist-rmf-guidepractitioner navigation and evidence planning
agent-evalevaluation validity and reporting

Maintained by Sima Bagheri.

About

A starter repository for documenting, reviewing, and releasing regulated AI systems.

Topics

Resources

Code of conduct

Contributing

Security policy

Stars

1 star

Watchers

0 watching

Forks

Releases

Packages

Contributors

Languages