Provably better data

Every security vendor says their data is better. Corelight decided to test that claim directly.

Using real nation-state attack scenarios, including Salt Typhoon-related activity, the same AI model was evaluated against multiple security data sources to measure investigation accuracy, threat visibility, and incident response coverage.

The only variable was the data.

Explore:
➡️ How different security telemetry impacts AI investigations
➡️ Why protocol-aware network evidence changes investigation outcomes
➡️ The relationship between data fidelity and detection coverage
➡️ AI performance during real incident response scenarios
➡️ Where low-context telemetry creates investigative blind spots

The results showed that when AI operated from Corelight network evidence, it identified significantly more threat activity and answered investigation questions with greater accuracy and coverage.

For SOC teams, threat hunters, and defenders evaluating AI-assisted workflows, this discussion focuses on a foundational question:

What happens when AI lacks the evidence needed to investigate effectively?

Corelight transforms raw network traffic into high-fidelity network evidence that helps defenders investigate faster, validate findings, and reduce uncertainty during incident response.

#Cybersecurity #ThreatHunting #AI #NetworkSecurity #NDR