How to Create Actionable Intelligence with AI and Machine Learning | Grey Noise Blog

Summary: A GreyNoise blog post drawn from a talk with Centripetal traces ML in security from late-1990s naive Bayes spam filtering to malware detection, outlier detection, and rule generation, and notes that almost all anti-malware processors in VirusTotal have some ML component. The post argues ML is necessary because GreyNoise sees over 2 million unique HTTP requests a day on one protocol, Verizon's DBIR recorded 16k incidents and 5k data breaches last year, and roughly 1,000 CISA Known Exploited Vulnerabilities are in circulation.
Key Takeaway: The post's core warning is about error tolerance: false positives drive alert fatigue until real malicious activity is ignored, and false negatives let attacks run unseen, so an ML detector that cannot show its work and its confidence score leaves analysts unable to tell which alerts to trust.

Read the original at GreyNoise Blog ↗