Before discussing governance, create an AI risk matrix

To judge whether an AI feature will cause trouble, you don't need to jump straight into big platforms. Start by laying out the risks in a simple table. I work on Tencent Cloud AI industry solutions, and when pitching implementation to clients, a plain old ledger works better than any model introduction. Technical feasibility is rarely the issue; the hard part is who takes the blame when things go wrong.

The news headline "If I wanted to maximize AI risk" can be translated as "How to maximize AI risk." I flipped it around to create a risk ledger.

A survey of 272 experts mentioned that AI makes existing risks easier to amplify.

Here’s how to build this AI Risk Ledger step-by-step.

Create a new spreadsheet and fill in the headers. Open Excel (if using the web version, click 'New Blank Workbook' in the top left), and name the file "AI Risk Ledger." In row 1, from A1 to H1, enter: Scenario, Input Data, Tools Used, Potential Failure Modes, Affected Parties, Evidence, Owner, Status.

Scenario: Who uses AI and when? E.g., Customer service sending ticket summaries to a model.

Input Data: Where does the data come from?

Tools Used: Models, plugins, automation workflows.

Potential Failure Modes: Don't just write "model is inaccurate." Write specific errors.

Affected Parties: Customers, Finance, Legal, frontline staff.

Evidence: Test screenshots, logs, user feedback.

Owner: Only one person allowed.

Status: Not Started, Verifying, Closed.

This step has a low barrier to entry and requires no procurement. If the field design is poor, the table will end up empty later.

Pull 5 scenarios from your business. Ask colleagues three questions: What was the last manual rework? What are you most worried about if AI sends it automatically? Who gets called first to explain if there's an error? Write the answers in Column A. Don't get greedy—start with 5, e.g., contract summaries, customer service ticket classification, auto-generated weekly reports. Once you can fill in data sources in Column B and tools in Column C, this step is done.

This aligns closely with client willingness to pay. Clients pay for less rework, fewer complaints, and avoiding blame. Colleagues might say "everything is risky," leading to an overly long list.

Run two low-effort tests for each scenario. No coding required. Open Feishu Docs (Lark) and create a test record. Copy real examples but mask names, phone numbers, and amounts.

Test 1: Deliberately mess up the input. For example, scramble the order of a customer service conversation and see if the tool still generates a definitive conclusion.

Test 2: Deliberately omit data. For example, remove the amount from a contract and see if it hallucinates one.

Record inputs, outputs, anomalies, and screenshots in the table. The expected result is one line of testing corresponding to one piece of evidence.

This helps gather evidence. With too few samples, you might misjudge.

Add a column for permission checks. For every tool, ask: Who can view, edit, or send this? Write the answer next to "Input Data." If customer data is involved, confirm if separate authorization exists. Beginners often miss this step, testing only model responses but not data flow.

Permission checks shift the risk from a model problem to a process problem. Many teams lack a permissions matrix and have to patch it together ad hoc.

Weekly review: Look only at rows with status "Verifying." Copy the table to a shared drive or post it in the group chat. Every week, check three columns: Evidence, Owner, Status. No evidence = cannot close. No owner assigned = cannot launch.

This creates a rhythm. Without maintenance, it will fizzle out.

My pitfalls: On day one using Excel, I wrote "AI might be inaccurate" under Potential Failure Modes. Too vague, untestable. Later, I changed it to "Generates amounts even when fields are missing," which made testing actionable. Another pitfall was having too many tools. Don't start with complex architecture; run through one manual example first.

Judgment comes last. This table shows clients the path to implementation, translates "technical feasibility is fine" into "responsibility assignment," and exposes which tasks aren't worth doing. It's not an audit system; it won't automatically find all risks and relies on manual updates.

My verdict: Build the ledger first, then automate. Consider buying tools or integrating systems only when three signals appear in the ledger: recurring same-type errors, unclear ownership, and business willingness to pay for risk interception.

Next steps: Pick a low-risk scenario, like meeting minutes summarization. Run the ledger for a week, store test records in a fixed folder, and see which evidence is reusable.

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📌 Compiled from Hacker News. Original: https://twitter.com/kellerjordan0/status/2097526284918419662

Copyright belongs to the original authors. This is a compilation and independent analysis based on public reports.