Quick answer
An AI risk assessment template should include the AI use case, owner, model or vendor source, data used, decision role, customer impact, risk tier, controls, validation evidence, monitoring cadence, open issues, and approval decision.
Guide vs. template
This guide explains what belongs in the template. The paid template gives you the editable working files so you are not rebuilding from a blank page.
Paid template includes
- ✓ AI Use Case Inventory tab with auto-tiering formula (consumer impact + decisioning role + PII + regulatory touchpoint)
- ✓ 44-question pre-deployment risk assessment scorecard across 11 risk domains
- ✓ 31-question third-party AI vendor due diligence questionnaire
- ✓ 8 pre-filled worked examples: Fraud Detection, Customer Chatbot, Credit Underwriting, AML Monitoring, Marketing GenAI, Shadow AI ChatGPT, BaaS KYC AI, Crypto Sanctions AI
What is this template for?
An AI risk assessment template is the working file risk and compliance teams use to inventory AI use cases, assign risk tiers, document model/vendor controls, capture bias and data-quality review, and produce evidence for bank partners, auditors, or regulators. The useful version is not a generic questionnaire — it connects each AI use case to a risk rating, owner, control evidence, and go/no-go decision.
Who needs this
- ✓ A bank partner, auditor, customer, or regulator asked how your team governs AI use.
- ✓ Your company uses vendor AI, embedded AI features, LLMs, underwriting models, fraud tools, or employee productivity AI and nobody owns the inventory.
- ✓ You need a repeatable pre-deployment review before product or engineering teams ship AI-enabled workflows.
- ✓ You need to explain which AI systems are high risk, why, and what controls are in place.
Required template fields
If you only build one section first, start with these fields. They give buyers, auditors, and reviewers a concrete checklist of what belongs in the template.
Want the working version? Download the editable template instead of rebuilding these fields from scratch.
Buy $59 →| Field | Why it matters | Example |
|---|---|---|
| AI use case / system name | Creates the inventory anchor. | Customer support chatbot; credit policy exception model; vendor fraud-screening API |
| Business owner and technical owner | Prevents orphaned AI systems. | Head of Operations; VP Engineering; Model Risk Manager |
| Decision role | Separates advisory AI from automated decisioning. | Human-assist only; recommends action; auto-approves/denies |
| Data used | Flags privacy, bias, confidentiality, and training-data concerns. | Customer PII, transaction history, adverse action reasons, call transcripts |
| Consumer/customer impact | Drives risk tiering and escalation. | No customer impact; customer communication; eligibility/pricing/credit decision |
| Vendor / model source | Tells you whether third-party due diligence is required. | OpenAI API, internal model, SaaS vendor model, rules engine with ML component |
| Risk tier and rationale | Makes the decision defensible. | High — affects credit eligibility and uses protected-class proxy variables |
| Control evidence | Turns policy into proof. | Validation sign-off, prompt testing log, vendor SOC 2, bias test output, monitoring dashboard |
Example AI risk assessment row
Use case
Customer support chatbot summarizes account questions and drafts responses for agent review.
Risk tier
Medium — customer-facing communication, but human review required before response.
Required evidence
Prompt testing log, hallucination test results, escalation procedure, vendor AI questionnaire, monitoring owner.
Implementation roadmap
Build the AI inventory first
Owner: Risk or compliance lead with engineering/product input
Output: List of all AI-enabled systems, including vendor tools and employee-facing LLM use
Assign risk tiers using objective triggers
Owner: Model risk, compliance, or product risk owner
Output: Tiering formula based on decision role, customer impact, data sensitivity, and regulatory touchpoints
Run pre-deployment assessment for high and medium risk systems
Owner: System owner + compliance reviewer
Output: Completed scorecard, required controls, and launch conditions
Collect vendor evidence where AI is third-party supplied
Owner: Third-party risk management, procurement, or compliance
Output: Vendor AI questionnaire, SOC report, model documentation, incident and monitoring commitments
Create ongoing monitoring cadence
Owner: System owner with second-line challenge
Output: Quarterly or semiannual review schedule, drift/bias checks, exception log
Ready to use it?
Download the AI Risk Assessment Template & Guide
Use the guide to understand the structure, or buy the editable template to move faster.
FAQ
What should be included in an AI risk assessment template? ⌄
At minimum: AI use case, owner, model/vendor source, data used, decision role, customer impact, risk tier, controls, validation evidence, vendor evidence, monitoring cadence, open issues, and approval decision.
Is an AI risk assessment the same as model validation? ⌄
No. Model validation tests whether a model performs appropriately for its intended use. AI risk assessment is broader: it covers ownership, data, privacy, bias, vendor risk, customer impact, governance, and monitoring.
Do vendor AI tools need risk assessment? ⌄
Yes. If a vendor provides AI that affects customers, operations, compliance, or decisioning, the company still needs to understand the use case, data, controls, monitoring, and failure modes.