AI risk assessment

Purpose

Produce a defensible, governance-ready risk assessment of a specific AI system in its specific deployment context — surfacing inherent risk across seven dimensions, prescribing concrete mitigations, and reporting residual risk so decision-makers can approve, condition, or reject adoption with full visibility.

When to Use

Invoke this skill when the user asks to:

• Assess the risk of adopting a new AI tool, vendor, or model
• Conduct an AI governance review for a system already in use
• Create an AI risk register entry for an internal system or feature
• Run a pre-launch risk review for an AI-powered product or workflow
• Evaluate a third-party AI vendor as part of due diligence
• Determine whether an AI use case is high-risk under EU AI Act, NIST AI RMF, or internal policy

Inputs Required

Before starting, collect:

• System description: What the AI does — model type (LLM, classifier, generative, agentic), provider, key capabilities
• Deployment context: Where it operates — internal tool, customer-facing, embedded in product, autonomous agent
• Use case and decisions: What decisions or outputs the AI drives, and what humans do with them
• Data inputs: What data flows in — PII, PHI, financial, proprietary, public, customer content
• User population: Who interacts with it — employees, customers, regulated populations (minors, healthcare patients, applicants)
• Jurisdictions: Where users and data live — relevant for GDPR, EU AI Act, CCPA, HIPAA, sector regulations
• Existing controls: What governance, monitoring, or guardrails are already in place

If jurisdictions or existing controls are unspecified, assume US + EU exposure and no controls in place, and note the assumption explicitly.

Instructions

Step 1: Classify the System and Set Inherent Risk Baseline

Before assessing dimensions, establish the risk envelope:

• Autonomy level: Advisory (human decides) / Augmented (human reviews) / Automated (human can override) / Autonomous (no human in loop) — higher autonomy raises baseline risk by one tier
• Impact level: Low (informational), Medium (operational efficiency), High (affects customers/employees materially), Critical (affects safety, rights, finances, or legal status)
• Regulatory classification: Determine EU AI Act tier (Minimal / Limited / High-Risk / Prohibited), whether HIPAA/GLBA/FCRA/EEOC applies, and any sector-specific regimes
• Baseline tier: Combine autonomy + impact + regulatory classification into an initial inherent risk tier (Low / Medium / High / Critical)

State the baseline tier explicitly before proceeding.

Step 2: Assess Across Seven Risk Dimensions

For each dimension, produce: (a) specific findings tied to this system, (b) inherent risk rating (Low/Medium/High/Critical), (c) prescribed mitigations, (d) residual risk rating after mitigations applied.

Dimension 1 — Model Risk What can go wrong with the model itself:

• Hallucination and factual error rates in the use case
• Prompt injection, jailbreaks, adversarial inputs
• Model drift, version changes by vendor, deprecation risk
• Lack of explainability for consequential decisions
• Overreliance — users trusting outputs without verification
• Performance degradation on edge cases or underrepresented populations

Dimension 2 — Data Risk What can go wrong with data flowing in or out:

• Sensitive data (PII, PHI, financial, IP, trade secrets) sent to third-party models
• Training data exposure — does the vendor train on inputs?
• Data residency violations (e.g., EU data sent to US-hosted model)
• Output data classification — is generated content treated as a new record?
• Retention, deletion, and right-to-erasure handling
• Data poisoning risk for retrievable/fine-tuned systems

Dimension 3 — Security Risk Attack surface and infrastructure exposure:

• Authentication, authorization, and access controls on the AI surface
• API key and credential handling
• Output handling — can model outputs trigger downstream code execution, queries, or actions (especially in agentic systems)?
• Tool/plugin/MCP server access scope — what can the AI actually do?
• Logging, audit trail completeness, tamper resistance
• Vendor security posture — SOC 2, ISO 27001, penetration testing cadence

Dimension 4 — Operational Risk What breaks the business if this fails:

• Vendor concentration and lock-in
• Availability, SLA, and fallback when the AI is down
• Cost volatility — token costs scaling unpredictably
• Latency and throughput under load
• Change management — vendor model updates breaking workflows
• Skill dependency — does only one team know how to operate or fix this?

Dimension 5 — Legal, Regulatory & Compliance Risk Where regulators or courts can intervene:

• EU AI Act obligations for the system's tier (transparency, conformity assessment, human oversight, technical documentation)
• GDPR / CCPA — automated decision-making rights, DPIA requirements, lawful basis
• Sector regimes: HIPAA (healthcare), FCRA/ECOA (credit and hiring), SEC/FINRA (financial advice), FTC (deceptive practices)
• IP exposure — copyright in training data, output ownership, indemnification from vendor
• Contractual obligations — customer DPAs, confidentiality terms, AI disclosure clauses
• Cross-border transfer mechanisms (SCCs, adequacy decisions)

Dimension 6 — Ethical & Bias Risk Where outcomes can be unfair or harmful:

• Disparate impact across protected classes in any decisioning use case
• Representation gaps in training data affecting underrepresented users
• Dignity and harm risks — content that demeans, misgenders, or stereotypes
• Manipulation risk — does the system nudge users in ways they wouldn't consent to?
• Affected populations who can't opt out (employees subject to AI monitoring, applicants screened by AI)
• Bias testing methodology and documented evaluation results

Dimension 7 — Reputational Risk What public failure looks like:

• Headline risk — is the use case the kind that makes news if it fails (children, healthcare, hiring, content moderation, surveillance)?
• Stakeholder reactions — employees, customers, investors, regulators, press
• Incident disclosure obligations — when does this need to be reported?
• Social media amplification potential of a single bad output
• Trust erosion — repeat-failure visibility

Step 3: Risk Heat Map

Produce a summary table:

Step 4: Mitigation Plan

Consolidate prescribed mitigations across all dimensions into a single action list, organized by:

• Pre-deployment blockers — must be in place before go-live
• Conditional controls — required within 30/60/90 days
• Ongoing controls — continuous monitoring, review cadence
• Owner: Function responsible (Legal, Security, Data, ML/Product, Compliance)

Step 5: Overall Residual Risk and Recommendation

Provide a final governance recommendation:

• Recommendation: Approve / Approve with conditions / Defer pending controls / Reject
• Overall residual risk tier: Low / Medium / High / Critical
• Conditions or blockers: Specific items that must be true for approval
• Re-review trigger: What changes would require reassessment (new use case, vendor model change, jurisdiction expansion, incident)
• Re-review cadence: When this assessment expires (typically 6 or 12 months)

Be opinionated. Do not hedge the recommendation.

Output Format

• Structure: System classification → Seven dimension assessments → Risk heat map table → Mitigation plan → Final recommendation
• Length: 1,500–2,200 words
• Format: Structured document with headers, dimension subsections, a heat map table, and a numbered mitigation action list
• Sections: System Classification & Baseline; Seven Risk Dimensions (each with findings, inherent rating, mitigations, residual rating); Risk Heat Map; Mitigation Plan; Recommendation & Re-Review Triggers
• Tone: Director of AI governance — measured, specific, decision-oriented; cites concrete regulatory regimes by name; avoids both hype and FUD

Quality Checklist

Before delivering output, verify:

• [ ] All seven risk dimensions are assessed — none skipped or merged
• [ ] Each dimension has both an inherent rating AND a residual rating after mitigations
• [ ] Regulatory references are specific (EU AI Act article tier, HIPAA, FCRA, etc.) — not generic "regulations apply"
• [ ] Mitigations are concrete actions with owners — not vague guidance like "improve monitoring"
• [ ] The heat map table is present and complete across all seven dimensions
• [ ] Final recommendation is one of the four explicit options — no hedging
• [ ] Re-review trigger and cadence are stated

Notes

• The most common assessment failure is rating everything "Medium" — push for differentiation across dimensions; some risks genuinely are Low, others genuinely Critical
• Agentic systems (those that take actions via tools) should almost always be rated higher on Security and Operational dimensions than chat-only systems with similar use cases
• If the system processes data subject to GDPR Article 22 (automated decisions with legal effect), this is a near-automatic High-Risk classification under the EU AI Act — flag it loudly
• Vendor-provided AI assessments and SOC 2 reports are inputs, not substitutes for this assessment — the deployment context determines risk, not the vendor's controls alone
• When in doubt between two tiers, choose the higher tier and document the reasoning — governance defensibility favors conservatism

How to use this skill

This file is a Claude Code skill in Anthropic's standard format — markdown with YAML frontmatter at the top.

Installing in Claude Code:

• ~/.claude/skills/<folder-name>/SKILL.md — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects)
• <your-project>/.claude/skills/<folder-name>/SKILL.md — auto-loads only when working in that project (recommended for project-specific skills)

Restart Claude Code or open a new session to register.

Using in Claude.ai, ChatGPT, Gemini, or other AI tools:

1. Drag this file into a new chat, or copy and paste its contents
2. Send your task or request as your next message — the AI will apply the skill where it fits

For Claude Code skill details: https://docs.anthropic.com/en/docs/claude-code/skills