Creating a rule to catch each threat doesn’t scale
What if you could use anomaly-based detection to identify threats, reducing the need for custom rules and policy tuning?
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Get the visibility and context you need to defend your cloud environments with autonomous machine learning.
More automation. Less tuning and alerts. It’s time for a threat detection solution that reduces the noise and helps you respond faster.
What if you could use anomaly-based detection to identify threats, reducing the need for custom rules and policy tuning?
What if you could use machine learning and behavioral analysis to reduce false positives and catch new threats?
What if you could automate threat detection so your team could focus on what matters most?
With Lacework, use anomaly-based detection to surface abnormal behavior that signals threats for fewer rules and false positives.
Go beyond threat feeds to uncover signals that indicate compromise to detect both known and unknown threats.
Using automation and machine learning means fewer hours spent on tuning policies and writing rules
A 90% reduction in alerts and false positives means you can focus on the threats that pose the biggest risk to your business.
We use automation and machine learning to detect anomalies that signal malicious activity for cloud accounts and workloads deployed on AWS, Google Cloud, and Azure.
“I have never seen a solution with the capabilities and comprehensiveness of Lacework. Its unique approach automatically learns what’s normal across our infrastructure and detects behavior that deviates from the norm.” Thomas Linck
Spot unknowns sooner and continuously watch for signs of compromise. Take us on a test drive to see for yourself.