How to Classify AI Use Cases by Risk Tier

Not every AI use case carries the same risk.

An internal AI tool that summarizes public articles is not the same as an AI system that ranks job applicants.

A code assistant used in a controlled engineering environment is not the same as an AI model that recommends customer credit decisions.

A chatbot that answers internal policy questions is not the same as a customer-facing assistant that gives advice based on personal data.

A vendor AI feature used for productivity is not the same as an AI tool that processes sensitive employee data.

A low-risk use case should not be buried in weeks of unnecessary review.

A high-risk use case should not be waved through because someone called it a pilot.

That is why AI governance needs risk tiering.

AI risk tiering helps organizations decide:

• how much review is required
• which teams need to be involved
• what controls are required
• what evidence must be retained
• who can approve the use case
• whether monitoring is required
• whether executive escalation is needed
• whether risk acceptance is required
• whether the use case should be blocked

The goal is not to make AI governance complicated.

The goal is to make it proportional.

Low-risk AI should move quickly.

Higher-risk AI should receive deeper review.

Unacceptable or prohibited AI should be stopped.

Conditional AI should be governed with clear limits, evidence, and monitoring.

That is how AI governance becomes practical.

What is AI risk tiering?

AI risk tiering is the process of classifying AI use cases into risk levels based on intended purpose, data sensitivity, affected stakeholders, decision impact, autonomy, human oversight, vendor involvement, cybersecurity exposure, regulatory relevance, monitoring needs, and potential harm.

A strong AI risk tier should answer:

• How risky is this use case?
• Why is it classified that way?
• What review path is required?
• What controls are required?
• What evidence is required?
• Who can approve it?
• What monitoring is required?
• What would trigger reassessment?
• What happens if risk increases?

NIST’s AI RMF Core is useful because it treats AI risk management as lifecycle-based and organized around Govern, Map, Measure, and Manage. Risk tiering belongs especially in the Map function because organizations need to understand context, intended use, impact, data, and stakeholders before measuring and managing AI risk.  

A weak AI risk tier says:

“This is high risk because it uses AI.”

A strong AI risk tier says:

“This is high risk because it processes sensitive employee data, influences hiring decisions, uses a third-party model provider, requires human oversight, and needs ongoing fairness, accuracy, monitoring, and legal review.”

That is useful.

AI risk tiering is not the same as legal classification

Legal AI classification and internal risk tiering are related, but they are not identical.

The EU AI Act uses a risk-based approach with four levels: unacceptable risk, high risk, transparency risk, and minimal or no risk. It also identifies examples of high-risk areas such as critical infrastructure, education, employment, access to essential services, certain biometric uses, law enforcement, migration, and justice-related contexts.  

An internal AI risk tiering model may be broader.

For example, an organization may classify a use case as high risk internally because it:

• uses confidential business data
• creates material customer trust risk
• affects a critical business process
• involves a critical vendor
• lacks monitoring
• has limited explainability
• creates cyber exposure
• could affect financial reporting
• conflicts with internal policy or customer commitments

Those may not always map neatly to a legal AI Act category.

That is fine.

The right model should support both:

1. Legal classification where laws or regulations apply.
2. Internal risk tiering for governance, controls, approvals, monitoring, and reporting.

Do not collapse them into one field.

A Connected GRC record should include both where relevant.

A Practical AI Risk Tier Model

A practical internal model can use five tiers:

The specific labels can vary.

Some organizations may prefer:

• minimal
• low
• medium
• high
• critical

Others may align to:

• minimal/no risk
• transparency risk
• high risk
• prohibited

The naming matters less than the operating consequences.

Each tier should drive a different workflow.

Tier 0: Prohibited or Blocked AI

Some AI use cases should not proceed.

A Tier 0 use case may be prohibited because it violates:

• law
• regulation
• internal policy
• customer commitment
• ethical boundary
• risk appetite
• contractual restriction
• employment policy
• privacy standard
• security requirement

Examples may include:

• social scoring
• harmful manipulation
• exploitation of vulnerable groups
• prohibited biometric use
• unapproved employee surveillance
• fully automated people-impacting decisions where prohibited
• use of sensitive data in public AI tools without approval
• AI that violates customer commitments
• AI that uses data in a way the organization cannot legally or contractually support

The EU AI Act identifies unacceptable-risk AI systems as those considered a clear threat to safety, livelihoods, and rights, and it lists prohibited practices such as harmful manipulation, harmful exploitation of vulnerabilities, social scoring, certain biometric uses, and emotion recognition in workplaces and education institutions.  

A Tier 0 decision should result in:

• rejection
• suspension
• escalation
• issue creation, if already in use
• remediation
• user communication
• monitoring for recurrence

A prohibited use case should not sit in a backlog.

It should trigger action.

Tier 1: Low-Risk AI

Low-risk AI use cases usually have limited impact and limited data exposure.

Common characteristics:

• internal use only
• no personal or sensitive data
• no customer-facing output
• no decisions about people
• no regulated process
• no critical business dependency
• approved tool or controlled environment
• human review is natural or built in
• minimal vendor or contract risk
• limited operational consequence if output is wrong

Examples:

• summarizing public articles
• drafting internal meeting agendas
• generating internal brainstorming ideas
• helping format non-sensitive documents
• classifying low-risk internal content
• assisting with training content that is reviewed before publication
• spam filtering or routine productivity use with no sensitive data

Low-risk does not mean no governance.

It means lighter governance.

Typical requirements:

• inventory record
• owner
• approved-use guidance
• policy acknowledgement
• basic data restrictions
• periodic owner attestation
• issue trigger if scope changes

Low-risk use cases should move quickly.

A governance model that treats every AI use case like a high-risk system will lose adoption.

Tier 2: Moderate-Risk AI

Moderate-risk AI use cases support business processes but usually do not directly make high-impact decisions.

Common characteristics:

• internal or limited external use
• limited personal or confidential data
• vendor involvement
• output reviewed by humans
• business process support
• low to moderate customer or employee impact
• limited automation
• manageable monitoring needs
• contract terms or data-use review required
• moderate operational dependency

Examples:

• AI drafting customer support responses with required human review
• AI summarizing internal customer feedback
• AI classifying support tickets
• AI assisting legal or contract review with attorney review
• AI summarizing sales calls
• AI helping analysts prioritize routine work
• AI-assisted code suggestions in approved environment
• AI used for low-impact operational recommendations

Moderate-risk use cases usually require:

• intake record
• data review
• vendor review, if third-party
• privacy review if personal data is involved
• cyber review if integrated or sensitive
• legal review if contract, IP, or external output matters
• human review control
• approval conditions
• monitoring plan
• evidence retention
• reassessment triggers

Moderate risk is where conditional approval is common.

The use case may be allowed, but only with limits.

Tier 3: High-Risk AI

High-risk AI use cases can materially affect people, customers, operations, regulated processes, or sensitive data.

Common characteristics:

• sensitive or regulated data
• customer-facing output
• people-impacting decisions
• employee or applicant impact
• automated recommendations with real consequences
• high reliance on AI output
• limited explainability
• critical business process
• material vendor dependency
• cyber integration with sensitive systems
• legal or regulatory exposure
• monitoring required
• potential for meaningful harm if wrong

Examples:

• AI ranking job applicants
• AI supporting hiring, promotion, discipline, or termination
• AI scoring customer eligibility
• AI used in credit, insurance, health, education, or housing workflows
• AI used for fraud investigation with customer consequences
• AI used in regulated compliance decisions
• AI that makes customer-facing recommendations involving sensitive data
• AI controlling or prioritizing critical service operations
• AI used in safety-related contexts
• AI analyzing sensitive employee data

High-risk use cases usually require:

• formal AI risk assessment
• privacy review
• DPIA or PIA where required
• legal review
• cyber review
• vendor and contract review
• data owner approval
• process owner approval
• documented human oversight
• monitoring metrics
• issue escalation
• approval by AI governance committee or executive owner
• evidence package
• periodic reassessment
• risk acceptance if residual risk remains

The European Commission describes high-risk AI use cases as those that can pose serious risks to health, safety, or fundamental rights, and identifies areas such as critical infrastructure, education, employment, access to essential services, certain biometric uses, law enforcement, migration, and justice contexts.  

Internal high-risk classification should be at least as sensitive to business and stakeholder impact.

Tier 4: Critical or Executive Escalation AI

Some AI use cases require executive, committee, or board-level attention.

This tier may apply when:

• risk is outside appetite
• residual risk is high
• legal exposure is material
• public trust risk is significant
• customer impact is material
• sensitive data use is broad
• incident likelihood or impact is elevated
• AI affects critical services
• use case is strategically important
• risk acceptance is needed
• controls are incomplete but business wants to proceed
• prior issues or incidents exist
• the use case may create board or regulatory concern

Examples:

• AI system used for material customer eligibility decisions
• AI model supporting regulated financial or health decisions
• AI used across a large employee population for workforce decisions
• AI product feature that could create public harm if wrong
• AI tool with unresolved privacy, cyber, or vendor issues but business pressure to launch
• AI use requiring formal risk acceptance outside normal approval authority
• AI incident remediation where continued use is under review

Tier 4 is not always “worse” than Tier 3.

It means the decision requires a higher governance level.

Typical requirements:

• executive sponsor
• formal risk assessment
• legal and compliance review
• privacy and cyber review
• board or committee visibility where needed
• documented alternatives
• formal risk acceptance
• monitoring commitments
• validated controls
• clear go/no-go decision
• ongoing reporting

This tier prevents high-stakes AI decisions from being made quietly.

AI Risk Tiering Factors

Risk tiering should consider multiple dimensions.

Do not base tiering on one factor alone.

1. Intended purpose

Ask:

• What is the AI used for?
• Is the purpose approved?
• Is the purpose changing?
• Is it used for productivity, recommendation, decision support, automation, or autonomous action?
• Does it support a regulated or sensitive process?

Purpose is the starting point.

The same AI tool may be low risk in one context and high risk in another.

Example:

• AI summarizing public articles: low risk.
• Same AI summarizing employee performance files: higher risk.
• Same AI influencing termination decisions: high risk.

2. Data sensitivity

Ask:

• Does it use personal data?
• Does it use sensitive personal data?
• Does it use regulated data?
• Does it use customer data?
• Does it use employee or applicant data?
• Does it use confidential business data?
• Does it use source code?
• Does it use security-sensitive data?
• Does it use prompts, outputs, embeddings, or training data?

Data sensitivity can raise the tier quickly.

A low-impact use case with public data may remain low risk.

The same use case with sensitive data may require privacy, legal, and cyber review.

3. Affected stakeholders

Ask:

• Who could be affected?
• Employees?
• Applicants?
• Customers?
• Consumers?
• Patients?
• Students?
• Vendors?
• Vulnerable groups?
• Public users?

AI that affects people usually deserves closer review.

The risk tier should increase when affected stakeholders are more vulnerable, more numerous, or more dependent on the outcome.

4. Decision impact

Ask:

• Does AI make a decision?
• Does it recommend a decision?
• Does it rank, score, classify, or prioritize people?
• Does a human review the output?
• Can the human override the output?
• Is the decision consequential?
• Is the decision regulated?
• Is the decision customer-facing?
• Is the decision documented?

Decision impact is one of the strongest risk tier drivers.

AI that suggests a meeting title is different from AI that ranks loan applicants.

5. Autonomy

Ask:

• Does AI only assist a user?
• Does AI generate drafts?
• Does AI recommend actions?
• Does AI trigger workflow actions?
• Does AI make decisions automatically?
• Can AI act without human review?
• Can AI call tools, APIs, or systems?

The more autonomous the AI, the higher the potential risk.

Agentic AI or tool-using AI may require stronger controls because it can act, not just answer.

6. Human oversight

Ask:

• Is human review required?
• Who reviews the output?
• What are they reviewing for?
• Can they override?
• Is the review documented?
• Are reviewers trained?
• Is oversight meaningful or symbolic?

“Human in the loop” is not enough.

The governance record should explain what the human actually does.

Weak oversight:

Human reviews AI output.

Better oversight:

A trained support agent reviews AI-drafted customer responses for accuracy, tone, policy compliance, and prohibited content before sending. Overrides and edits are logged for monitoring.

Human oversight can reduce risk, but only if it is real.

7. External exposure

Ask:

• Is the output internal only?
• Is the output customer-facing?
• Is it public-facing?
• Does it affect contractual commitments?
• Does it affect regulatory submissions?
• Does it affect customer assurance?
• Does it create reputational risk?

External use usually increases risk.

An internal AI draft may be moderate risk.

A customer-facing AI response may be high risk.

A public or regulated AI output may require legal and executive review.

8. Vendor and model provider involvement

Ask:

• Is a third-party tool involved?
• Is a model provider involved?
• Is the AI feature embedded in a SaaS platform?
• Are subprocessors involved?
• Does the vendor retain data?
• Can the vendor train on data?
• What contract terms apply?
• What evidence exists?
• What monitoring is available?
• Can the organization audit or verify controls?

Vendor involvement does not automatically make a use case high risk.

But vendor involvement adds data, contract, security, retention, subprocessor, and monitoring questions.

9. Cyber and integration risk

Ask:

• Does AI connect to production systems?
• Does it access sensitive data?
• Does it use APIs?
• Does it have write access?
• Does it retrieve data from internal systems?
• Can it trigger workflows?
• Does it use plugins or agents?
• Are logs available?
• Can misuse create data leakage?
• Are prompt injection or tool misuse relevant?

AI integrated with production systems usually requires cyber review.

AI with write permissions, system access, or autonomous tool use may require higher risk tiering.

10. Explainability and transparency

Ask:

• Can users understand what the AI is doing?
• Are outputs explainable enough for the use case?
• Are disclosures required?
• Are users aware they are interacting with AI?
• Are affected people informed?
• Can decisions be explained?
• Can outputs be challenged?

The EU AI Act includes a transparency-risk category that focuses on disclosure obligations, such as informing humans when they are interacting with AI systems like chatbots and identifying certain AI-generated content.  

Transparency risk should be reflected in internal tiers.

A use case may not be high risk overall, but it may still require disclosure controls.

11. Monitoring ability

Ask:

• Can the AI be monitored?
• What metrics are available?
• Can errors be detected?
• Can bias, drift, or performance degradation be detected?
• Are logs retained?
• Are complaints tracked?
• Are human overrides tracked?
• Are incidents tracked?
• Can the use case be reassessed?

A high-impact AI use case with weak monitoring should be treated as higher risk.

Monitoring capability is part of risk.

12. Control maturity

Ask:

• Are required controls defined?
• Are controls operating?
• Is evidence available?
• Are issues open?
• Is residual risk accepted?
• Is validation complete?
• Are owners trained?
• Are approval conditions tracked?

A use case with strong controls may have lower residual risk.

A use case with missing controls may require conditional approval, escalation, or rejection.

Risk tiering should distinguish inherent risk and residual risk.

Inherent Risk vs Residual Risk

AI risk classification should consider both.

The intake workflow should assign initial inherent risk.

After controls are defined, the use case may have a residual risk rating.

Example:

• Inherent risk: high
• Controls: human review, data minimization, vendor contract terms, monitoring, fairness testing
• Residual risk: moderate / high
• Decision: approved with conditions and quarterly monitoring

Do not pretend controls eliminate all risk.

They reduce risk.

Residual risk may still require monitoring or risk acceptance.

Rule-Based Overrides

A scoring model can help, but some conditions should automatically raise the tier.

Use overrides.

Automatic Tier 0 or escalation triggers

• prohibited-use indicator
• clear policy violation
• unlawful use
• unapproved sensitive data in public AI tool
• unacceptable safety or rights risk
• high-risk use already deployed without approval

Automatic high-risk triggers

• AI affects employment decisions
• AI affects access to essential services
• AI affects credit, insurance, health, education, housing, or regulated eligibility
• AI processes sensitive personal data at scale
• AI is customer-facing and consequential
• AI uses biometric or highly sensitive identifiers
• AI uses third-party vendor with unresolved data-use terms
• AI has autonomous action in production systems
• AI lacks meaningful human oversight where oversight is required

Rule-based overrides prevent teams from gaming a point score.

A use case involving employment decisions should not be classified low risk because it has a small user base.

AI Risk Tiering Scorecard

A scorecard can help standardize classification.

Use 0–3 scoring for each factor.

Example score interpretation:

But do not rely on score alone.

Use rule-based overrides.

Controls by AI Risk Tier

Risk tier should drive controls.

This table makes tiering operational.

If a risk tier does not change the workflow, it is just a label.

Evidence by AI Risk Tier

Evidence requirements should increase by tier.

Tier 1 evidence

• AI inventory record
• business owner
• approved-use statement
• basic data restriction
• policy acknowledgement
• periodic attestation

Tier 2 evidence

• intake record
• data review
• vendor review, if applicable
• privacy or cyber review, if triggered
• human review procedure, if applicable
• approval record
• monitoring plan
• issue record, if gaps exist

Tier 3 evidence

• formal risk assessment
• data inventory links
• privacy review or DPIA / PIA where required
• cyber review
• vendor and contract review
• legal review
• human oversight evidence
• monitoring metrics and thresholds
• test or validation evidence
• approval decision
• issue remediation evidence
• risk acceptance, if residual risk remains

Tier 4 evidence

• executive risk memo
• alternatives considered
• formal approval record
• legal, privacy, cyber, vendor, and AI governance reviews
• residual risk analysis
• risk acceptance
• monitoring and reporting plan
• board or committee materials, where relevant
• periodic reassessment evidence

Evidence is what makes tiering defensible.

AI Risk Tiering Workflow

Step 1: Capture use case facts

Use the intake workflow to capture:

• business purpose
• owner
• users
• affected stakeholders
• data
• vendor
• system
• output
• decision impact
• lifecycle stage

Step 2: Screen for prohibited use

Check against:

• law
• internal policy
• risk appetite
• customer commitments
• ethical boundary
• prohibited-use list

Step 3: Apply rule-based triggers

Identify automatic high-risk or escalation factors.

Step 4: Score risk factors

Use the scorecard to evaluate data, decision impact, autonomy, vendor, cyber, monitoring, and potential harm.

Step 5: Assign initial tier

Document:

• risk tier
• rationale
• evidence
• reviewer
• date

Step 6: Route required reviews

Route based on tier and triggers:

• privacy
• cyber
• legal
• vendor risk
• compliance
• AI governance
• executive committee

Step 7: Define controls and evidence

Define required safeguards and proof.

Step 8: Assess residual risk

After controls, determine whether residual risk is acceptable.

Step 9: Approve, condition, reject, or escalate

Record the decision.

Step 10: Monitor and reassess

Update risk tier when data, scope, vendor, model, output, or impact changes.

AI Risk Tiering Examples

Example 1: Internal public-content summarizer

Use case:

Summarize public industry reports for sales enablement.

Risk factors:

• public data
• internal use
• no personal data
• no decision impact
• no customer-facing output
• approved tool

Tier:

Tier 1: Low

Requirements:

• inventory record
• owner
• approved-use guidance
• no confidential data condition

Example 2: AI support response drafting

Use case:

Draft customer support responses based on support ticket content.

Risk factors:

• customer data
• vendor tool
• output could become customer-facing
• human review required
• prompt/output retention matters

Tier:

Tier 2: Moderate, potentially Tier 3 if sensitive data or customer impact is high

Requirements:

• privacy review
• vendor review
• legal review for data-use terms
• human review control
• monitoring
• approval conditions

Example 3: AI ranking job applicants

Use case:

Rank applicants for interview priority.

Risk factors:

• applicant data
• employment decision impact
• potential bias or fairness risk
• legal and regulatory relevance
• vendor model
• human oversight required
• monitoring required

Tier:

Tier 3: High or Tier 4: Executive escalation

Requirements:

• legal review
• privacy review
• AI governance review
• vendor review
• human oversight
• fairness and performance monitoring
• approval by committee
• risk acceptance if residual risk remains

Example 4: AI code assistant

Use case:

Engineering team uses AI coding assistant.

Risk factors:

• source code
• confidential business data
• vendor tool
• IP concerns
• possible credential leakage
• no direct people decision

Tier:

Tier 2: Moderate, potentially Tier 3 if sensitive repositories or production integration are involved

Requirements:

• legal/IP review
• cyber review
• vendor terms
• training restrictions
• user guidance
• usage monitoring
• approved repository scope

Example 5: AI fraud investigation support

Use case:

AI prioritizes customer transactions for fraud review.

Risk factors:

• customer data
• decision support
• possible financial impact
• regulated context
• explainability needed
• human review required
• monitoring required

Tier:

Tier 3: High

Requirements:

• legal and compliance review
• privacy review
• cyber review
• model performance monitoring
• human review evidence
• customer impact assessment
• issue escalation process

AI Risk Tier Dashboard

An AI risk tier dashboard should show:

Dashboards should show more than AI inventory count.

They should show risk tier health.

Common AI Risk Tiering Mistakes

Mistake 1: Classifying by tool instead of use case

The same tool can be low risk or high risk depending on use.

Classify the use case.

Mistake 2: Treating all AI as high risk

This creates friction and reduces adoption.

Use risk-based routing.

Mistake 3: Treating all pilots as low risk

A pilot can still use sensitive data or affect people.

Pilot status does not automatically reduce risk.

Mistake 4: Ignoring data sensitivity

AI risk depends heavily on data.

Unknown data should not be treated as low risk.

Mistake 5: Ignoring decision impact

AI that influences decisions about people usually needs stronger review.

Mistake 6: Ignoring vendor and contract risk

AI vendors may retain prompts, use data for training, or rely on model providers.

Mistake 7: Not changing the tier after scope changes

Risk tier should update when data, users, outputs, vendors, monitoring, or decision impact changes.

Mistake 8: Having tiers that do not drive workflow

If Tier 1 and Tier 3 have the same process, the model is not useful.

AI Risk Tiering Checklist

Use this checklist before assigning a risk tier.

If several answers are no, the use case is not ready for classification.

A 30-Day AI Risk Tiering Implementation Plan

Days 1–5: Define the tier model

Create tiers:

• prohibited / blocked
• low
• moderate
• high
• critical / escalation

Define what each tier means.

Days 6–10: Define tiering factors

Select factors:

• data sensitivity
• decision impact
• autonomy
• human oversight
• vendor involvement
• cyber integration
• external exposure
• monitoring
• regulatory relevance
• potential harm

Days 11–15: Define rule-based overrides

Create automatic escalation triggers:

• prohibited use
• employment decisions
• sensitive data at scale
• regulated decisions
• autonomous production action
• missing monitoring for high-impact use
• unresolved vendor data-use terms

Days 16–20: Map tiers to workflow

Define for each tier:

• required reviews
• required approvals
• required controls
• required evidence
• monitoring cadence
• dashboard visibility

Days 21–25: Pilot the model

Classify:

• one low-risk tool
• one moderate-risk workflow
• one high-risk people-impacting use case
• one vendor AI feature
• one sensitive-data use case

Days 26–30: Launch dashboard and governance

Create dashboard views for:

• use cases by tier
• use cases missing tier
• high-risk use cases
• approved with conditions
• monitoring gaps
• reassessments due
• decisions needed

This creates a practical AI risk classification model quickly.

How Connected GRC Improves AI Risk Tiering

Connected GRC improves AI risk tiering by linking:

• AI use case
• business process
• owner
• data inventory
• vendor
• contract
• system
• privacy review
• cyber review
• legal review
• risk assessment
• controls
• evidence
• approval
• monitoring
• issues
• risk acceptance
• dashboard

ISO/IEC 42001 describes an AI management system as a set of interrelated or interacting elements that establish policies and objectives and processes to achieve those objectives for responsible AI development, provision, or use. That management-system view supports connected records, lifecycle governance, and continuous improvement.  

In a disconnected model, risk tiering is a spreadsheet field.

In a connected model, risk tiering drives the workflow.

That is the difference.

A Practical Test for Your AI Risk Tiering Model

Pick one AI use case.

Ask whether your GRC model can show:

• use case description
• business owner
• lifecycle stage
• business process
• data categories
• personal or sensitive data status
• vendor or model provider
• contract terms
• affected stakeholders
• decision impact
• autonomy level
• human oversight
• monitoring plan
• prohibited-use screen
• risk tier
• tier rationale
• required reviews
• controls
• evidence
• approval decision
• approval conditions
• open issues
• risk acceptance
• reassessment triggers
• dashboard status

If answering those questions requires intake forms, spreadsheets, vendor files, privacy documents, legal notes, cyber tickets, and meetings, AI risk tiering is not connected enough.

That is common.

It is also the opportunity.

Final Thought

AI risk tiering is where AI governance becomes practical.

It helps the organization avoid two bad outcomes.

The first is treating every AI use case as low risk because teams want to move quickly.

The second is treating every AI use case as high risk because governance teams want to be safe.

Both approaches fail.

The better approach is risk-based.

Classify the use case.
Understand the data.
Understand who is affected.
Understand decision impact.
Understand vendor and cyber exposure.
Understand human oversight.
Understand monitoring.
Assign the tier.
Route the reviews.
Define controls.
Collect evidence.
Approve with conditions where needed.
Monitor after approval.
Reassess when the use case changes.

That is how AI risk tiering supports innovation and accountability at the same time.

It lets low-risk AI move.

It makes high-risk AI visible.

It blocks prohibited use.

It gives executives the decisions they need.

That is Connected GRC for AI governance.