The risks worth checking first
AI risk falls into a few buckets: data risk (sensitive information going somewhere it shouldn't), security risk (new attack surfaces and access), and reputational risk (wrong, biased, or embarrassing output reaching customers). An assessment maps which apply to your specific deployment.
The point isn't to scare you out of using AI. It's to deploy with eyes open, putting controls where the real exposure is rather than treating every use case as equally dangerous.
- Data: what sensitive info flows into AI tools, and where it goes
- Vendor terms: whether your data trains their models or is retained
- Access: who and what can reach the AI and its data
- Output risk: harm from wrong, biased, or fabricated results
- New attack surface: prompt injection and data leakage paths
- Regulatory exposure for health, finance, or personal data
Turning findings into controls
An assessment is only useful if it leads to action. The output should be a prioritized list of risks with practical mitigations: tightening what data can enter tools, choosing vendors with better terms, adding human review where output is consequential, and limiting access.
Match the control to the stakes. A low-risk internal summarizer needs light guardrails; a system touching customer data or money needs strict ones. Over-controlling everything just pushes staff toward unapproved workarounds.
- A prioritized risk list, most serious first
- Data-handling rules on what may enter which tools
- Vendor selection weighted on data terms and retention
- Human review gates on consequential output
- Least-privilege access to AI systems and data
- Controls sized to the stakes, not one-size-fits-all
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Frequently asked questions
Do AI vendors train their models on the data we send them?
It depends entirely on the vendor and plan, and the defaults vary. Some retain and train on inputs unless you opt out; business tiers often don't. Reading the actual terms for each tool you use is exactly the kind of thing an assessment checks.
What's the single biggest risk for most companies?
Staff pasting sensitive data into unapproved tools without realizing where it goes. It's common, easy to prevent with clear rules and approved tools, and one of the first things a risk assessment addresses.
How thorough does an assessment need to be?
Proportional to your exposure. A company handling regulated data needs more depth than one using AI to draft marketing copy. A free consultation can help scope an assessment that fits your actual risk without overdoing it.