FoundationDx uses adaptive AI to identify authentication anomalies, behavioral risks, and operational inefficiencies — before they become incidents. Designed for speed, accuracy, and affordability.
Healthcare organizations generate massive volumes of data across systems like DUO, Microsoft 365, EMRs, and network infrastructure.
FoundationDx combines machine learning with adaptive rules to identify patterns that traditional tools miss. Our platform continuously learns your environment — so detection improves over time.
Deep analysis of DUO & Microsoft 365 authentication events to surface credential-based threats instantly.
User-level behavioral baselines detect deviations from normal patterns before they escalate.
Identify early warning signals of potential compromise or misuse across your entire environment.
Surface clinical and operational inefficiencies hidden in high-volume data streams.
Group complex numeric and categorical data into meaningful risk clusters — revealing hidden population-level patterns invisible to rule-based tools.
Using real-world data, FoundationDx identifies threats and anomalies that are often early indicators of security threats or system misuse.
FoundationDx applies unsupervised machine learning to group users, devices, and events into risk-differentiated clusters — combining numeric metrics and categorical attributes that traditional tools analyze in isolation.
Group users by authentication behavior, role, location, and device — identifying cohorts that share elevated risk characteristics across multiple dimensions.
Cluster EMR access events by time, record type, volume, and department to detect physicians or staff whose access patterns deviate from their peer group.
Segment devices by OS, location, authentication method, and failure rate to surface high-risk endpoint clusters that warrant priority remediation.
Analyze clinical workflow data — visit duration, order volumes, escalation rates — to identify process clusters with systemic inefficiencies.
We are not a broad AI platform. We are purpose-built to uncover anomalies in complex healthcare data environments.
Purpose-built to uncover anomalies in complex healthcare data environments — not a broad AI platform trying to do everything.
Our models use a rolling behavioral baseline (e.g., 60-day learning window) to continuously adjust as user activity evolves.
We combine AI-driven insights with configurable rules, ensuring both flexibility and precision across your unique environment.
We prioritize meaningful alerts — not overwhelming volumes of data that exhaust your security team.
Available as a managed service or on-premise solution, with minimal overhead and rapid time to value.
Designed for organizations with high data volume but limited internal AI resources — we do the heavy lifting.
FoundationDx is a specialist layer that makes existing security investments smarter and more effective in healthcare — not a competitor, but a force multiplier.
FoundationDx is ideal for healthcare organizations that generate high volumes of authentication and operational data and need smarter, faster insight.
We make it easy to get started. No heavy lift. No unnecessary complexity.
Provide a scoped dataset from your environment — we handle the rest.
Our platform surfaces real risks and anomalies hidden in your data.
Receive actionable recommendations you can act on immediately.
No heavy lift. No unnecessary complexity.
FoundationDx solutions are currently deployed in healthcare environments analyzing large-scale datasets, uncovering anomalies that would be difficult and time-consuming to detect manually.
Additional use cases include:
Duo and Cisco Duo are trademarks or registered trademarks of Cisco Systems, Inc. and/or its affiliates in the United States and certain other countries. Microsoft 365 is a trademark or registered trademark of Microsoft Corporation in the United States and/or other countries. FoundationDx is not affiliated with, endorsed by, or sponsored by Cisco Systems, Inc. or Microsoft Corporation. All other trademarks, product names, and company names mentioned herein are the property of their respective owners.