The HiveWatch Approach
HiveWatch approaches noise differently than the solutions currently offered on the market.
Analyze not only the systems being used, but the individual customer's data to determine the most accurate and productive path forward.
Machine learning is used to dramatically reduce false alarms and excess noise (up to 98%).
report on your baseline noise, where its coming from, and the best way to solve for it.
Why fix your false alarm problem NOW?
Alarm fatigue happens when alarms keep coming and "The Alarm Who Cried Wolf" gets ignored.
missed incidents as alarms that are legitimate become ignored due to overload.
focus on more critical security initiatives which could ultimately improve the health and efficiency of your security program.
Supercharge your security team
The HiveWatch® AI Operator intelligently detects and responds to security incidents
The AI Operator is able to:
Verify alarms by reviewing camera footage
Escalate high-priority and real alarms to its human supervisors as needed
Reduce missed incidents
“We were getting as many as 305 alarms on just one device a week, and now we’re down to about 25. That’s a monthly noise reduction of 91% freeing up hours of our operator’s time to focus on more strategic initiatives.”
Additional Assets
Reducing Noise the Right Way
Interview with HiveWatch Product Manager about the concept of noise in an operations center and how the HiveWatch® GSOC Operating System solves for it.
Creating a Noise Reduction Strategy for Your Organization
Join us Nov. 9 and walk away with a better understanding of how to create a noise reduction strategy for your organization.
Developing a Strategy for Reducing Noise in the SOC
A step-by-step guide for addressing noise and false alarms across your organization.
