Review the governance and risk posture of an AI feature — classify its risk tier by impact (who is affected, reversibility, autonomy, sensitive-data and rights exposure), assign accountable human ownership, require appropriate human oversight (advisory / human-in-the-loop / human-on-the-loop) matched to the tier, check user-facing AI disclosure and consent, data-use and retention posture, a model/feature card documenting intended use and known limits, and a map from applicable obligations (EU AI Act tiers, NIST AI RMF functions) to concrete controls. Composes ai-sdlc-operating-model, agent-governance-audit, and human-approval-boundary rather than restating them. Use for an AI risk assessment, an AI feature's go/no-go governance review, or AI-policy readiness. Do NOT use to audit one change's process compliance (agent-governance-audit), enumerate technical attack threats (ai-threat-modeler), or define the SDLC lifecycle (ai-sdlc-operating-model).
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Review the governance and risk posture of an AI feature — classify its risk tier by impact (who is affected, reversibility, autonomy, sensitive-data and rights exposure), assign accountable human ownership, require appropriate human oversight (advisory / human-in-the-loop / human-on-the-loop) matched to the tier, check user-facing AI disclosure and consent, data-use and retention posture, a model/feature card documenting intended use and known limits, and a map from applicable obligations (EU AI Act tiers, NIST AI RMF functions) to concrete controls. Composes ai-sdlc-operating-model, agent-governance-audit, and human-approval-boundary rather than restating them. Use for an AI risk assessment, an AI feature's go/no-go governance review, or AI-policy readiness. Do NOT use to audit one change's process compliance (agent-governance-audit), enumerate technical attack threats (ai-threat-modeler), or define the SDLC lifecycle (ai-sdlc-operating-model).
AI Governance & Risk Reviewer
Purpose
Review whether an AI feature is governed responsibly: is its risk understood
and tiered, is a named human accountable, is the human-oversight model matched
to the impact, are users told they're interacting with AI where it matters, is
data use and retention appropriate, is there a model/feature card, and do the
applicable regulatory obligations map to concrete controls that actually
exist. The output is a governance verdict — with the gaps that must close
before a high-risk feature ships. This is the organizational/risk layer, above
the technical attack surface (ai-threat-modeler) and distinct from auditing
one change's process compliance (agent-governance-audit); it composes the
governance pack rather than restating it.
Use When
Use when: performing an AI risk assessment, an AI feature's governance
go/no-go, or checking AI-policy readiness before launch.
Use when: deciding the required human-oversight level for an AI feature by
its impact, or assigning accountable ownership.
Use when: mapping regulatory obligations (EU AI Act risk tiers, NIST AI RMF
GOVERN/MAP/MEASURE/MANAGE) to the feature's concrete controls.
Do NOT use when: auditing whether ONE change/PR followed governance process
— agent-governance-audit (evidence-based per-control audit).
Do NOT use when: enumerating technical threats (ai-threat-modeler) or
defining the human+agent SDLC lifecycle (ai-sdlc-operating-model).
Inputs to Inspect
The AI feature's purpose and impact: what decision/action it drives, who is
affected (internal, customers, the public), and the consequence of a wrong
output.
Autonomy and oversight: does it advise, act with a human in the loop, or
act autonomously; where a human can intervene or reverse.
Data posture: what data it uses (personal, sensitive, tenant), where it
goes (provider retention), consent and lawful basis, retention/deletion.
Transparency: is the user told it's AI; are limitations disclosed; is there
a model/feature card (intended use, known failure modes, out-of-scope use).
Accountability: who owns the feature's outcomes; escalation and incident
ownership (incident-response-runbook).
Applicable obligations: regulatory/contractual/policy requirements
(EU AI Act tiering, sector rules, internal AI policy, NIST AI RMF as the
risk method) — and existing governance artifacts from the Phase 1.5 pack.
Workflow
Classify the risk tier. Score impact by: who is affected and how many,
reversibility of a wrong output, degree of autonomy, sensitivity of data,
and effect on people's rights/access/safety. Land on a tier (e.g.
minimal / limited / high / unacceptable) using
references/ai-governance-framework.md.
No feature purpose/impact available → Stop Conditions.
Match oversight to tier. Higher tiers require stronger human oversight:
advisory-only (human decides) → human-in-the-loop (human approves each
action) → human-on-the-loop (human monitors, can intervene). Verify the
feature's actual oversight matches its tier; a high-impact autonomous
feature with no human gate is a blocking gap (compose
human-approval-boundary for the gate design).
Assign accountability. A named human owner accountable for outcomes,
not a team abstraction. Escalation path defined and tied to
incident-response-runbook.
Check transparency and consent. Users are told they're interacting with
AI where it affects them; limitations are disclosed; consent/lawful basis
for data use exists where required. Absent disclosure on a user-affecting
feature is a gap.
Check data governance. What's sent to the model, provider retention and
training-use terms, PII/sensitive handling, retention/deletion — compose
sensitive-disclosure-guard for the leak surface and
tenant-isolation-reviewer for tenant data. Governance says whether the
use is permitted; those skills check the mechanism.
Require a model/feature card. Intended use, in-scope and out-of-scope
use, known limitations and failure modes, evaluation summary
(ai-evaluation-harness), and the human-oversight model. Missing card on a
material feature is a gap.
Map obligations to controls. For each applicable obligation (EU AI Act
tier duties, NIST AI RMF function, internal policy), name the concrete
control that satisfies it and where it lives — or mark it a gap. Do not
assert regulatory conclusions as legal advice; flag items needing legal
review.
Verdict and gaps. Governance PASS / CONDITIONAL / BLOCK with the
specific gaps that must close, each routed to an owning skill/owner and a
named risk acceptor via human-approval-boundary for anything shipped with
residual risk.
Risk tier assigned from impact, reversibility, autonomy, data
sensitivity, and rights exposure — not from feature size.
Oversight model matched to tier; a high-impact autonomous feature with
no human gate is flagged blocking.
A named human owner is accountable for outcomes; escalation is defined.
User-facing AI disclosure and consent/lawful basis checked for
user-affecting features.
Data-use, provider retention/training, and retention/deletion posture
reviewed; mechanism checks routed to the owning skills.
A model/feature card exists (intended use, limits, eval summary,
oversight) for material features.
Obligations map to concrete controls or explicit gaps; legal-review
items flagged, not asserted as legal conclusions.
Gotchas
Risk tier is about impact, not sophistication: a simple model that auto-
denies loan applications is higher-risk than a complex one that suggests
playlists. Tier by consequence.
"There's a human somewhere" is not oversight — oversight requires the human
to see the decision, have time and information to intervene, and not be
rubber-stamping (see human-agent-trust-reviewer for consent fatigue).
Disclosure gaps hide in "helpful" UX: a support bot that never says it's a
bot, an AI-written email sent as a person. Material AF interaction needs
disclosure.
Provider data terms are part of governance: sending customer data to a model
that trains on it can breach contracts and privacy law even if technically
secure. Check the terms, not just the transport.
This is not legal advice: regulatory tiering (is this "high-risk" under the
EU AI Act?) often needs counsel — map and flag, don't rule.
Governance without evidence is theater: a policy that says "human oversight"
with no gate in the code is a gap — cross-check against
agent-governance-audit for actual compliance.
Stop Conditions
No feature purpose or impact is described — stop; risk tiering needs to know
what the feature does and to whom.
The review surfaces a likely unacceptable-risk use (e.g. prohibited
practice) — escalate to a named human and legal; do not bless it.
The question is really one change's process compliance, the technical threat
model, or the SDLC lifecycle — hand to the owning skill.
A governance gap corresponds to an active harm in production — route to
incident-response-runbook.
Supporting Files
references/ai-governance-framework.md
— the risk-tiering rubric, the oversight-to-tier matrix, model/feature card
template, and the obligation→control mapping scaffold (EU AI Act tiers,
NIST AI RMF GOVERN/MAP/MEASURE/MANAGE).
evals/evals.json — trigger + behavior cases.
evals/trigger-evals.json — discrimination within the AI-platform-ops
cluster and against agent-governance-audit and ai-sdlc-operating-model.
