AI governance
AI agents and chatbots can independently suggest answers or actions. While this accelerates service delivery, it also amplifies the consequences of an incorrect answer or a wrongly executed task.
The Core
In regulated sectors, it must be clear who is responsible, what data is used, and which decisions the technology is or is not permitted to make. Without these agreements, assessing the risk remains difficult.
A good approach doesn't start with an isolated tool. First, it must be clear what problem the customer is experiencing, what outcome the organization wants to achieve, and which processes, data, and people are involved.
Important to know
These elements provide a concise overview of the content. Use them as a starting point for a conversation between business, IT, customer service, and compliance.
A generative model can react differently to small variations in a query.
An error can originate in data, model, integration, prompt, process rule, or human management.
Organizations must be able to explain how an answer or action was derived.
Customers and employees need a clear path when AI is not sufficiently certain.
Practical approach
You don't have to solve everything at once. These three steps make the topic discussable and actionable.
Determine impact, sensitivity, data usage, and potential harm per use case.
Use validated knowledge, process rules, logging, tests, and explicit handovers.
Assign responsibilities for content, technology, risk, monitoring, and improvement.
What it can deliver
Good governance doesn't hinder innovation. Instead, it clarifies which applications can safely scale and where additional protection is needed.
Discuss a secure AI routeNext step
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