GraphWeaver: Billion-Scale Cybersecurity Incident Correlation
Bibliographic record
Abstract
In the dynamic landscape of large enterprise cybersecurity, accurately and efficiently correlating billions of security alerts into comprehensive incidents is a substantial challenge. Traditional correlation techniques often struggle with maintenance, scaling, and adapting to emerging threats and novel sources of telemetry. We introduce GraphWeaver, an industry-scale framework that shifts the traditional incident correlation process to a data-optimized, geo-distributed graph based approach. GraphWeaver introduces a suite of innovations tailored to handle the complexities of correlating billions of shared evidence alerts across hundreds of thousands of enterprises. Key among these innovations are a geo-distributed database and PySpark analytics engine for large-scale data processing, a minimum spanning tree algorithm to optimize correlation storage, integration of security domain knowledge and threat intelligence, and a human-in-the-loop feedback system to continuously refine key correlation processes and parameters. GraphWeaver is integrated into the Microsoft Defender XDR product and deployed worldwide, handling billions of correlations with a 99% accuracy rate, as confirmed by customer feedback and extensive investigations by security experts. This integration has not only maintained high correlation accuracy but reduces traditional correlation storage requirements by 7.4x. We provide an in-depth overview of the key design and operational features of GraphWeaver, setting a precedent as the first cybersecurity company to openly discuss these critical capabilities at this level of depth.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".