Chaos Engineering Validates Healthcare Microsegmentation Resilience

Chaos Engineering Validates Healthcare Microsegmentation Resilience

Wednesday, March 11, 2026 2:45 PM to 3:15 PM · 30 min. (US/Pacific)
Level 5 | Palazzo D
Education Sessions
Cybersecurity

Information

Endorsed by:

 
This case study explores how a major healthcare system leverages chaos engineering principles to strengthen cyber resilience while maintaining critical operational control during simulated attacks. By implementing microsegmentation, the organization created a security architecture, enabling security and IT teams to maintain visibility and access to all users, workloads and devices during simulated attacks, while simultaneously blocking simulated threat actors and restricting non-essential staff. Within 24 hours of deployment, teams created and simulated their first security policies, providing immediate visibility into their network of more than 60,000 devices including critical medical IoT systems. This innovative approach allows pressure-testing systems against cyber threats while maintaining operational continuity, revealing security gaps proactively addressed without disrupting patient care. The session details practical methodologies, metrics and lessons learned that healthcare organizations can apply to enhance disaster preparedness while accelerating Zero Trust implementations.

Topic
Disaster Preparedness
Target Audience
CIO/CTO/CTIO/Senior ITCISO/CSOIT Professional
Level
Intermediate
Format
Case Study
CEU Type
ACPECAHIMSCMECNECPDHTSCPHIMSGIAC CPEIAPP
Contact Hours
0.50
Learning Objective #1
Analyze how chaos engineering principles validate microsegmentation effectiveness by intentionally creating controlled network disruptions to test security resilience, enabling healthcare organizations to identify vulnerabilities and validate policies without risking patient care or operational continuity
Learning Objective #2
Design a phased microsegmentation deployment strategy that achieves 97% attack surface reduction while maintaining clinical operations, including stakeholder alignment, policy simulation techniques, and metrics for tracking implementation velocity across multiple healthcare facilities
Learning Objective #3
Evaluate methods for overcoming common microsegmentation barriers including clinical resistance, legacy infrastructure constraints, and resource limitations through visual policy modeling, cross-functional collaboration frameworks, and automated classification approaches that reduce manual effort by 90%
Session #
135

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