Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·May 25·5 min read
TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale
TL;DR
Large-scale cloud-native services face significant risks from technical anomalies, often indicated by noisy customer incidents. TingIS is an end-to-end system that utilizes a multi-stage event linking engine, combining efficient indexing techniques with Large Language Models (LLMs) to extract actionable incidents.
✦ Why It Matters
Engineers can leverage TingIS to improve incident detection and response in large-scale systems, minimizing downtime and financial impact.
Key Takeaways
- Utilize TingIS for real-time incident detection in cloud environments.
- Leverage LLMs for improved event merging and analysis.
- Achieve faster incident response with a P90 alert latency of 3.5 minutes.
- Enhance routing accuracy and clustering quality in incident management.
How It Works
TingIS integrates a multi-stage event linking engine that uses efficient indexing and Large Language Models (LLMs) to analyze and merge diverse user incident descriptions. This allows for the extraction of actionable intelligence from noisy data, while a cascaded routing mechanism ensures precise attribution to relevant business lines.
A multi-dimensional noise reduction pipeline further refines the data by incorporating domain knowledge and statistical patterns.