With the proliferation of LLMs across industries and regulation lagging, we thought we’d be share the risks we’ve identified over the years we’ve worked in data services and GenAI. And as part of our commitment to always give you information you can use, we also offer some of the ways these risks can be mitigated.
In a recent white paper we called out all the different risks and solutions in some detail. But before we get to that in this blog, we’d like to point out that the biggest risk, when any of these issues come up, is to your reputation and the trust others have in your company’s processes and ethics.
The nine risks we discuss in our white paper are:
- Regulatory and Compliance Risk
- Model Risk
- Data Risk
- Operational Risk
- Security Risk
- Ethical and Reputational Risk
- Strategic Risk
- Legal Risk
- Human Factors Risk
Rather than rehash all of them in a blog, we’ll highlight a few of the nine we list in the white paper here. These are some of the ones we think can lead to the most serious consequences for your business if not addressed quickly and correctly.
Regulatory and Compliance Risk
There are a number of data protection rules active in different countries where your LLM might operate. Among those are MiFID II, GDPR, or SEC/FINRA rules. To avoid disclosing sensitive data DATAmundi offers PII Redaction & Sensitive-Term Filtering to prevent exposure of client-specific or personal data. We can also help create a Golden Dataset that enforces compliance boundaries during LLM output generation.
Model Risk
LLMs are prone to hallucinations providing confident yet incorrect answers, inconsistent outputs, lack of explainability, and model drift over time. These issues can lead to a lack of trust or even diminished usability of your LLM.
DATAmundi has solutions to mitigate those issues. We deploy Dual Annotation & Disagreement Workflow, where both AI and human annotators review outputs, flag inconsistencies, and surface low-confidence responses early. In addition, we offer Retrieval Fine-Tuning Services which improve RAG systems by enhancing document recall with relevance annotations, and custom benchmarking to detect model drift.
Operational Risk
LLM-based system’s failure during critical business processes, such as trading or client reporting, can have far-reaching consequences. Avoid overreliance on third-party APIs at critical performance times to steer clear of regulatory and compliance issues.
At DATAmundi who have some ideas on how to reduce that type of operational fragility. We can stress test LLM systems under simulated loads, latency conditions, and edge cases using custom built Golden Test Sets. Our annotation teams are very well trained and can create scenarios that mimic real-world failures, which can help businesses validate LLMs before production rollout.
Strategic Risk
We’ve seen a fair number of LLM pilot projects not scale due to a misalignment with business goals or unclear governance. We’ve also seen some companies that might over-promise ROI based on unrealistic expectations of model performance.
How do you avoid that risk? DATAmundi helps enable strategic decisions with benchmarking datasets and human-in-the-loop evaluations giving leaders clarity on model capabilities and limitations.
These are a few of the most serious risks and mitigations we have identified and collected in a recent white paper posted on our website. The AI landscape is changing rapidly, and it can be a challenge to keep up, which is why we are committed to always sharing relevant information with you. As time goes on, and other issues come up, we will update the white paper and post a fresh blog about it.
If you find any of these issues are slowing your AI progress, contact us, we can help.
