David Wolf · Portfolio Use Case
Large-scale connected-device analytics using Forescout Device Cloud, Elastic, Kibana, and security-research workflows to turn millions of device records into report-ready security evidence.
Built and executed Elastic/Kibana-style analytics workflows over Forescout Device Cloud data to support security research, sector-specific report findings, connected-device risk analysis, rapid response investigations, and public market education across healthcare, connected medical devices, financial services, OT, IoT, and the Enterprise of Things. Contributed to a Forescout Device Cloud research program spanning healthcare, connected medical devices, financial services, operational technology, and the Enterprise of Things, using large-scale connected-device telemetry and Elastic/Kibana-style analysis to support public research reports, market education, customer conversations, and executive security narratives.
Client
Forescout
Engagement Type
Full-Time research contribution; exact title and dates should be confirmed from resume/Profile source
Period
2019–2020
Role
Security Research / Device Cloud Analytics / Kibana & Elastic Analyst Contributor
Focus Areas
Device Cloud Analytics, Elastic/Kibana Workflows, Connected Device Intelligence
The Context
Forescout Device Cloud gave researchers a rare view into real connected-device environments. The data spanned sectors, networks, device types, operating systems, protocols, services, and unmanaged assets that ordinary endpoint tools often missed.
The Challenge
The research problem was not simply collecting data. It was extracting defensible security signal from millions of records: which devices existed, where they lived, what they ran, what they exposed, what they were near, and what risk that created.
What I Did
The Outcome
The result was an evidence-generation capability behind multiple Forescout research narratives. The same pattern now underpins David's AI-security work: telemetry, normalization, analysis, control evidence, and clear executive communication.
Analysis
Over Forescout Device Cloud data used in multiple 2019–2020 public research reports
Forescout
Sources describe report-specific Device Cloud datasets including more than 8 million devices, approximately 13 million devices, and more than 11 million customer enterprise devices depending on the report context
Forescout
Sources describe Banking on Security as examining 100 large financial-services deployments with over 8,500 VLANs and nearly 900,000 devices
Forescout
Sources describe Healthcare Under the Microscope as analyzing 75 healthcare deployments with over 1.5 million devices
Forescout
Sources describe related connected medical-device/TCP-IP analysis as using anonymized data from approximately 13 million devices from more than 1,800 global customers
Research
Across multiple Forescout public reports in 2019–2020, including healthcare, connected medical devices, financial services, operational technology, and Enterprise of Things themes
Public
Sources state that Healthcare Under the Microscope analyzed 75 healthcare deployments with over 1.5 million devices from a Device Cloud repository of more than 8 million devices at the time
Public
Sources state that the Enterprise of Things Security Report analyzed data from over 8 million devices across financial services, government, healthcare, manufacturing, and retail
Public
Sources state that Banking on Security examined 100 large financial-services deployments with over 8,500 VLANs and nearly 900,000 devices
Public
Sources state that related connected medical-device / TCP-IP vulnerability analysis used anonymized data from approximately 13 million devices from more than 1,800 global customers
Key Deliverables
Collaboration
Worked in a Forescout research and device-intelligence context where large-scale telemetry, Elastic/Kibana analysis, product-security interpretation, and market-facing security research had to converge into usable findings. Worked in a Forescout research and device-intelligence context where large-scale Device Cloud analysis needed to become credible security insight for practitioners, executives, customers, sales teams, analysts, and market education across multiple sectors.
Client
Forescout
Engagement Type
Full-Time research contribution; exact title and dates should be confirmed from resume/Profile source
Period
2019–2020
Role
Security Research / Device Cloud Analytics / Kibana & Elastic Analyst Contributor
Focus Areas
Device Cloud Analytics, Elastic/Kibana Workflows, Connected Device Intelligence
The Context
Forescout Device Cloud gave researchers a rare view into real connected-device environments. The data spanned sectors, networks, device types, operating systems, protocols, services, and unmanaged assets that ordinary endpoint tools often missed.
The Challenge
The research problem was not simply collecting data. It was extracting defensible security signal from millions of records: which devices existed, where they lived, what they ran, what they exposed, what they were near, and what risk that created.
What I Did
The Outcome
The result was an evidence-generation capability behind multiple Forescout research narratives. The same pattern now underpins David's AI-security work: telemetry, normalization, analysis, control evidence, and clear executive communication.
Analysis
Over Forescout Device Cloud data used in multiple 2019–2020 public research reports
Forescout
Sources describe report-specific Device Cloud datasets including more than 8 million devices, approximately 13 million devices, and more than 11 million customer enterprise devices depending on the report context
Forescout
Sources describe Banking on Security as examining 100 large financial-services deployments with over 8,500 VLANs and nearly 900,000 devices
Forescout
Sources describe Healthcare Under the Microscope as analyzing 75 healthcare deployments with over 1.5 million devices
Forescout
Sources describe related connected medical-device/TCP-IP analysis as using anonymized data from approximately 13 million devices from more than 1,800 global customers
Research
Across multiple Forescout public reports in 2019–2020, including healthcare, connected medical devices, financial services, operational technology, and Enterprise of Things themes
Public
Sources state that Healthcare Under the Microscope analyzed 75 healthcare deployments with over 1.5 million devices from a Device Cloud repository of more than 8 million devices at the time
Public
Sources state that the Enterprise of Things Security Report analyzed data from over 8 million devices across financial services, government, healthcare, manufacturing, and retail
Public
Sources state that Banking on Security examined 100 large financial-services deployments with over 8,500 VLANs and nearly 900,000 devices
Public
Sources state that related connected medical-device / TCP-IP vulnerability analysis used anonymized data from approximately 13 million devices from more than 1,800 global customers
Key Deliverables
Collaboration
Worked in a Forescout research and device-intelligence context where large-scale telemetry, Elastic/Kibana analysis, product-security interpretation, and market-facing security research had to converge into usable findings. Worked in a Forescout research and device-intelligence context where large-scale Device Cloud analysis needed to become credible security insight for practitioners, executives, customers, sales teams, analysts, and market education across multiple sectors.
At a Glance
Focus Areas
Tools & Technologies
Evidence & Artifacts
Public-Safe Caveat
This case study describes the analytics layer behind multiple Forescout Device Cloud research efforts. Public report-level facts are sourced from Forescout materials where available, while the author's specific Elastic/Kibana contribution is based on user-provided context. Exact queries, dashboards, raw datasets, customer names, internal schemas, proprietary scoring logic, private drafts, and non-public analysis details are omitted unless later confirmed and approved for public use. This case study aggregates Forescout Device Cloud research contribution across multiple 2019–2020 reports. Public report-level facts are sourced from Forescout materials where available, while the author's specific Device Cloud and Elastic/Kibana contribution is based on user-provided context. Exact authorship, internal dashboards, queries, raw datasets, proprietary schemas, customer names, unpublished drafts, and private analysis details are omitted unless later confirmed and approved for public use.
David Wolf
AI Security · Product Security · Security Leadership
Based on analyzed public signals, not proof of any individual's or company's internal state.
