Legacy tools missed internal threats. Now, AI isolates compromised devices in seconds, freeing analysts and ensuring uninterrupted care.
A nonprofit community hospital serving patients across a 200-mile radius through an acute care facility and 16 associated clinics.
The transition to electronic health records, cloud services, and connected medical devices rapidly expanded the organization's attack surface. IT...
“Cybersecurity has moved beyond the IT department – it’s now a frontline patient safety issue. Our mission at Artesia is to make sure technology never becomes the weak link in delivering care.”
Community hospital providing emergency, primary, and specialty healthcare services.
Self-learning AI platform for cybersecurity threat detection and autonomous response.
Artesia General Hospital's Threat detection and response is part of this use case:
Related implementations across industries and use cases
Analysts dug through disconnected portals to triage alerts. Now, AI autonomously classifies threats and drafts reports in minutes.
Surging imaging volumes created severe cognitive burden. Now, AI summarizes prior reports and drafts impressions directly in the workflow.
Analysts lost hours manually parsing firewall spreadsheets. Now, AI automatically triages alerts, escalating just 1% for human review.
Analysts dug through disconnected portals to triage alerts. Now, AI autonomously classifies threats and drafts reports in minutes.
Analysts lost hours manually parsing firewall spreadsheets. Now, AI automatically triages alerts, escalating just 1% for human review.
Burnout above 40% as documentation forced a choice: patient or chart. Dragon Copilot drafts notes in 20 seconds; clinicians stay present.
Integration projects stretched over months, waiting on scarce specialists at each stage. Axon now guides teams through every step in place.
Each incident sent engineers hunting for context from scratch, inconsistently across teams. Edwin AI now delivers it upfront.
Flying blind across AI pipelines—no view of cost or accuracy. Observability cut compute costs 87.5% and root cause from 30 hours to minutes.
Legacy tools missed internal threats. Now, AI isolates compromised devices in seconds, freeing analysts and ensuring uninterrupted care.
A nonprofit community hospital serving patients across a 200-mile radius through an acute care facility and 16 associated clinics.
The transition to electronic health records, cloud services, and connected medical devices rapidly expanded the organization's attack surface. IT...
“Cybersecurity has moved beyond the IT department – it’s now a frontline patient safety issue. Our mission at Artesia is to make sure technology never becomes the weak link in delivering care.”
Community hospital providing emergency, primary, and specialty healthcare services.
Self-learning AI platform for cybersecurity threat detection and autonomous response.
Artesia General Hospital's Threat detection and response is part of this use case:
Related implementations across industries and use cases
Analysts dug through disconnected portals to triage alerts. Now, AI autonomously classifies threats and drafts reports in minutes.
Surging imaging volumes created severe cognitive burden. Now, AI summarizes prior reports and drafts impressions directly in the workflow.
Analysts lost hours manually parsing firewall spreadsheets. Now, AI automatically triages alerts, escalating just 1% for human review.
Analysts dug through disconnected portals to triage alerts. Now, AI autonomously classifies threats and drafts reports in minutes.
Analysts lost hours manually parsing firewall spreadsheets. Now, AI automatically triages alerts, escalating just 1% for human review.
Burnout above 40% as documentation forced a choice: patient or chart. Dragon Copilot drafts notes in 20 seconds; clinicians stay present.
Integration projects stretched over months, waiting on scarce specialists at each stage. Axon now guides teams through every step in place.
Each incident sent engineers hunting for context from scratch, inconsistently across teams. Edwin AI now delivers it upfront.
Flying blind across AI pipelines—no view of cost or accuracy. Observability cut compute costs 87.5% and root cause from 30 hours to minutes.