◆ 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're 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.
◆ Audience
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.
◆ Implementation roadmap
How to roll this out.
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
Frequently asked questions.
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.