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A top-tier security firm needed a cleaner, faster way to identify compromised systems across a large distributed environment. Traditional monitoring was reactive, slow, and produced excessive false positives.
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Design BEACON, a machine learning platform capable of detecting compromise in near real-time using network behavior analysis at global scale with minimal analyst overhead.
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- Assumed compromised systems alter network footprint in measurable ways
- Analyzed first and second derivatives of network traffic
- Detected predictable malicious behaviors (C2, scanning, probing)
- Built lightweight telemetry agents feeding a real-time detection engine
- Tuned ML models for high precision
- Integrated analytics with SOC workflows and automated response
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- 97% faster detection
- Identified threats missed by traditional monitoring
- Near real-time detection achieved
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- 85% reduction in analyst workload
- Up to 17× reduction in remediation cost
- Improved SOC efficiency
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Enabled operationalization of machine learning for real-time security detection at scale, improving automation and cyber resilience.
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