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compliance-readiness

AI Compliance Readiness Assessment — evaluate how prepared an organization is for AI governance regulations (EU AI Act, NIST AI RMF, HHS mandates, state bar AI rules). Scores readiness across 8 dimensions and generates an action plan. Use when assessing AI compliance gaps, preparing for audits, or building a governance roadmap.

1kalin0 source installs☆ 0 Repository stars

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Skill instructions

AI Compliance Readiness Assessment

Evaluate organizational readiness for AI governance regulations and generate an actionable compliance roadmap.

When to Use

  • Assessing AI compliance posture before an audit
  • Preparing for EU AI Act (Aug 2026), HHS AI mandates, NIST AI RMF
  • Building a governance roadmap for AI deployments
  • Evaluating risk exposure from current AI usage

How to Use

When asked to assess AI compliance readiness, gather these inputs:

Required Inputs
  1. Industry (legal, healthcare, financial-services, insurance, construction, manufacturing, government, other)
  2. Company size (employees or revenue range)
  3. AI systems in use (list: chatbots, document review, fraud detection, hiring tools, customer service, analytics, other)
  4. Jurisdictions (US-only, EU-exposed, both, global)
Optional Inputs
  • Current governance framework (if any)
  • Upcoming audit dates
  • Existing compliance certifications (SOC2, ISO 27001, HIPAA, etc.)
  • Number of AI vendors/tools in use

Assessment Framework

Score each dimension 1-5 (1=no controls, 5=mature):

8 Dimensions
  1. Risk Classification — Have you categorized AI systems by risk level per EU AI Act / NIST?
  2. Documentation — Technical docs, model cards, data lineage for each AI system?
  3. Human Oversight — Defined human-in-the-loop processes for high-risk decisions?
  4. Bias & Fairness — Regular bias audits, fairness metrics, disparate impact testing?
  5. Data Governance — Training data provenance, consent, retention, and deletion policies?
  6. Incident Response — AI-specific incident playbook, reporting procedures, rollback plans?
  7. Vendor Management — AI vendor risk assessments, contractual AI governance requirements?
  8. Audit Trail — Logging, explainability, decision traceability for AI-assisted outputs?
Scoring
  • 35-40: Compliance-ready — minor gaps to address
  • 25-34: Partially prepared — significant work needed in specific areas
  • **...

Source information

Source
github
Repository
openclaw/skills
Path
skills/1kalin/compliance-readiness
Version
—
Commit
aefebffd6447b67f7a0b65561b5c3dfbdfc0cfa3
Classification source
rules
Status
active
Visibility
public

These signals come from indexed source analysis and may lag behind changes. Check the current source files.

Extracted content

SKILL.md
# AI Compliance Readiness Assessment

Evaluate organizational readiness for AI governance regulations and generate an actionable compliance roadmap.

## When to Use
- Assessing AI compliance posture before an audit
- Preparing for EU AI Act (Aug 2026), HHS AI mandates, NIST AI RMF
- Building a governance roadmap for AI deployments
- Evaluating risk exposure from current AI usage

## How to Use

When asked to assess AI compliance readiness, gather these inputs:

### Required Inputs
1. **Industry** (legal, healthcare, financial-services, insurance, construction, manufacturing, government, other)
2. **Company size** (employees or revenue range)
3. **AI systems in use** (list: chatbots, document review, fraud detection, hiring tools, customer service, analytics, other)
4. **Jurisdictions** (US-only, EU-exposed, both, global)

### Optional Inputs
- Current governance framework (if any)
- Upcoming audit dates
- Existing compliance certifications (SOC2, ISO 27001, HIPAA, etc.)
- Number of AI vendors/tools in use

## Assessment Framework

Score each dimension 1-5 (1=no controls, 5=mature):

### 8 Dimensions
1. **Risk Classification** — Have you categorized AI systems by risk level per EU AI Act / NIST?
2. **Documentation** — Technical docs, model cards, data lineage for each AI system?
3. **Human Oversight** — Defined human-in-the-loop processes for high-risk decisions?
4. **Bias & Fairness** — Regular bias audits, fairness metrics, disparate impact testing?
5. **Data Governance** — Training data provenance, consent, retention, and deletion policies?
6. **Incident Response** — AI-specific incident playbook, reporting procedures, rollback plans?
7. **Vendor Management** — AI vendor risk assessments, contractual AI governance requirements?
8. **Audit Trail** — Logging, explainability, decision traceability for AI-assisted outputs?

### Scoring
- **35-40**: Compliance-ready — minor gaps to address
- **25-34**: Partially prepared — significant work needed in specific areas
- **...

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