AI risk
The market is taking a more critical look at the risks of autonomous AI agents and chatbots. Reliability, accountability, and control are becoming increasingly important, especially in financial services.
The core
An application may seem technically sound but still pose risks due to incorrect knowledge, unpredictable language use, or excessive authority to perform actions.
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 components provide a concise overview of the content. Use them as a starting point for a conversation between business, IT, customer service, and compliance.
Define who owns content, models, processes, and customer outcomes.
Determine which actions are allowed automatically and where explicit approval is required.
Ensure logging, tests, version control, and explanations of sources used.
Track errors, deviations, complaints, and unexpected behavior after going live.
Practical approach
You don't have to solve everything at once. These three steps make the topic discussable and implementable.
Assess impact, data usage, target audience, and potential damage.
Combine natural language with fixed processes and validated knowledge.
Only expand when results and risks are demonstrably manageable.
What it can achieve
Governance provides teams with a common language for secure innovation and clarifies which AI applications are ready for practice.
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