
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?
Delivering the most comprehensive and continuous view into threats across multicloud environments.
Solving cloud security and compliance challenges with unmatched visibility.
Lacework is trusted by the most innovative companies across the globe.
We are helping our partners build successful and profitable cloud security practices to help meet the adoption of cloud.
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We are changing the future of cloud security with automation and data so our customers can innovate with speed and safety.
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 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.
"Lacework sees everything. We are aware of any change in behavior, and we can immediately assess a change to see if it’s legit or a threat."
Spot unknowns sooner and continuously watch for signs of compromise. Take us on a test drive to see for yourself.