How often should we run an AI risk assessment?

At minimum: when adopting a new AI tool, when a tool's scope changes significantly, and once a year as a general review. For fast-moving tools, quarterly is better.

What are the biggest AI risks for small teams?

Data leakage (employees sharing sensitive data with LLMs), shadow AI (unapproved tools), vendor lock-in, and hallucination-driven mistakes in customer-facing work.

Do we need a dedicated risk officer?

No. A single owner — often an ops lead, CTO, or founder — can run a lightweight risk process for most small teams. The key is documenting decisions, not creating bureaucracy.

What's the difference between AI risk and regular software risk?

AI systems are non-deterministic — the same input can produce different outputs. This makes testing harder and means you need ongoing monitoring, not just pre-launch QA.

Risk Assessment

AI Risk Assessment for Small Teams

Most small teams skip risk assessment because it sounds like enterprise compliance overhead. It doesn't have to be. Here's a practical, lightweight approach to identifying and managing AI risks before they become incidents.

Start here

Pillar guide

How to Do an AI Risk Assessment (Small Team Guide)

A step-by-step guide to running your first AI risk assessment — without a risk team. Covers use-case mapping, likelihood scoring, and key controls.

9 min read

Frequently Asked Questions

How often should we run an AI risk assessment?: At minimum: when adopting a new AI tool, when a tool's scope changes significantly, and once a year as a general review. For fast-moving tools, quarterly is better.
What are the biggest AI risks for small teams?: Data leakage (employees sharing sensitive data with LLMs), shadow AI (unapproved tools), vendor lock-in, and hallucination-driven mistakes in customer-facing work.
Do we need a dedicated risk officer?: No. A single owner — often an ops lead, CTO, or founder — can run a lightweight risk process for most small teams. The key is documenting decisions, not creating bureaucracy.
What's the difference between AI risk and regular software risk?: AI systems are non-deterministic — the same input can produce different outputs. This makes testing harder and means you need ongoing monitoring, not just pre-launch QA.

Risk Assessment

AI Risk Assessment for Small Teams

Start here

Pillar guide

How to Do an AI Risk Assessment (Small Team Guide)

A step-by-step guide to running your first AI risk assessment — without a risk team. Covers use-case mapping, likelihood scoring, and key controls.

9 min read

Frequently Asked Questions

How often should we run an AI risk assessment?: At minimum: when adopting a new AI tool, when a tool's scope changes significantly, and once a year as a general review. For fast-moving tools, quarterly is better.
What are the biggest AI risks for small teams?: Data leakage (employees sharing sensitive data with LLMs), shadow AI (unapproved tools), vendor lock-in, and hallucination-driven mistakes in customer-facing work.
Do we need a dedicated risk officer?: No. A single owner — often an ops lead, CTO, or founder — can run a lightweight risk process for most small teams. The key is documenting decisions, not creating bureaucracy.
What's the difference between AI risk and regular software risk?: AI systems are non-deterministic — the same input can produce different outputs. This makes testing harder and means you need ongoing monitoring, not just pre-launch QA.

Frequently Asked Questions

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