Ai Governance Architect
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Install
$ agentstack add skill-prvthmpcypher-skills-business-ai-governance-architect ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-prvthmpcypher-skills-business-ai-governance-architect) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
AI Governance Architect
Establishes enterprise AI governance frameworks balancing innovation velocity with risk management, compliance, and responsible AI principles.
Phased Workflow
Phase 1: AI Risk Assessment & Inventory
- Catalog all AI/ML models in production with metadata: owner, training data provenance, use case, risk tier.
- Classify models by risk level: Low (internal productivity), Medium (customer-facing recommendations), High (financial/healthcare/legal decisions).
- Assess each model for: bias risk, explainability requirements, data privacy exposure, and regulatory compliance.
Phase 2: Policy & Framework Design
- Define responsible AI principles: Fairness, Transparency, Accountability, Privacy, Safety, Human Oversight.
- Establish model lifecycle governance: development standards, pre-deployment review, monitoring, retirement.
- Design human-in-the-loop (HITL) protocols for high-risk decisions with clear escalation paths.
Phase 3: Auditing & Continuous Monitoring
- Implement bias auditing: statistical parity, equalized odds, disparate impact analysis across protected classes.
- Build model performance monitoring dashboards with drift detection and fairness metric tracking.
- Establish incident response procedures for AI system failures or harmful outputs.
Verification & Quality Checklist
- [ ] AI inventory complete with risk classifications for all production models.
- [ ] Responsible AI policy documented and approved by leadership.
- [ ] Bias auditing procedures defined with quantitative fairness thresholds.
- [ ] HITL escalation paths tested and documented for all high-risk use cases.
Anti-Patterns & Constraints
- NEVER deploy high-risk AI models without documented human oversight mechanisms.
- NEVER treat AI governance as a one-time checklist; implement continuous monitoring.
- NEVER ignore demographic subgroup analysis when evaluating model fairness.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: prvthmpcypher
- Source: prvthmpcypher/skills-business
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.