**The Secret to Smarter AI in Security? It’s Not the Model—It’s the Data**
As AI becomes deeply embedded in Security Operations Centers (SOCs), a critical question looms over every strategy session: *Does SOC performance depend more on the large language model (LLM) deployed or on the underlying quality of the security data it consumes?* While the debate rages on between model vendors and academic theorists, a growing body of research points to a clear answer—data quality is the true bottleneck and the biggest lever for success.
### The Data Argument Wins
Recent studies highlight that “trustworthy AI applications require high-quality training and test data along many quality dimensions, such as accuracy, completeness, and consistency.” Complementing this, findings from the Provably Better Data research project reveal that high-fidelity network evidence can improve security outcomes by **2–4x** across core investigation metrics. For CISOs, this means:
– **Reduced Mean Time to Respond (MTTR)** with concrete telemetry
– **Controlled token expenditure**, helping manage AI costs
– **Higher ROI on security investments**, with demonstrable team efficacy
### Putting the Question to the Test
To measure what truly drives AI performance in security, researchers established a controlled benchmark using four distinct network telemetry sources:
– **Corelight enriched logs**
– **Open source nDPI firewall logs**
– **Snort 3 intrusion detection alerts**
– **NetFlow connection telemetry**
Each dataset was processed under identical conditions using the same LLMs and prompts. Models were scored on accuracy in Capture the Flag (CTF) scenarios and evidence-backed incident report generation.
#### Key Results
| Data Source | CTF Accuracy | Evidence Coverage (Incident Response) |
|————-|————-|—————————————-|
| Corelight | 95.2% | 90.3% |
| Firewall | 58.3% | 61.3% |
| Snort 3 | 39.4% | 21.2% |
| NetFlow | 25.8% | 30.9% |
In CTF challenges, Corelight-supported models achieved a score of **4,178.3 points**, while NetFlow lagged at **970.0**—a **fourfold difference**. Investigation times also varied widely: Corelight enabled full completion in **14.7 minutes**, compared to over **26 minutes** for lower-quality sources.
### Why Data Quality Matters More Than Models
Even the most advanced frontier models are bounded by the evidence available. Without protocol-level context, AI agents cannot infer what was never recorded. Hallucinations remained zero across grounded data sources, while lower-fidelity logs like Snort introduced risks of fabricated evidence. In short: **rich, normalized telemetry turns AI from a helpful assistant into a reliable investigator**.
### Recommendations for Security Leaders
– **Prioritize evidence quality over model upgrades**—no amount of prompt engineering can fix poor data
– **Invest in protocol-aware telemetry** that provides structured, complete network context
– **Use measurable outcomes**—like MTTR, coverage rates, and cost per investigation—to justify infrastructure decisions
For SOC architects and CISOs, the message is clear: the future of AI-driven security isn’t about chasing the latest model—it’s about building on a foundation of high-quality, actionable data.
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### FAQ
**Q: Which LLM performed best in the study?**
A: The study found minimal performance differences between models when high-quality data was used. Anthropic Claude Opus 4.6, Google Gemini Pro 3.1, and older models all achieved strong results with Corelight logs, indicating that data quality matters more than model choice.
**Q: What is “Provably Better Data”?**
A: It refers to research evaluating how data fidelity, structure, and normalization directly impact AI outcomes in cybersecurity. The project used standardized benchmarks like CTF scenarios and incident response analysis to isolate data as the primary variable.
**Q: Can AI hallucinations be reduced in SOC workflows?**
A: Yes. Models grounded in high-quality, protocol-aware telemetry (like Corelight) recorded zero hallucinations. Hallucinations were more common in low-fidelity sources like Snort alerts, where context is limited.
**Q: How can security leaders justify investments in enriched telemetry?**
A: By highlighting measurable ROI: faster investigations, lower MTTR, reduced analyst burnout, and demonstrable improvements in security efficacy. The research shows 2–4x gains in outcomes simply from improving data quality.
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### Conclusion
The debate between model sophistication and data quality is settled by the evidence. AI in SOC environments is only as strong as the telemetry it consumes. Organizations that prioritize high-fidelity, structured, and normalized network data will see exponential gains in detection speed, investigation accuracy, and operational efficiency. For security leaders, the path forward is clear: invest in better data first—and let AI amplify its power.
