Data Edge

In 1988, NTT DATA was born from a bold vision: to harness the transformative potential of information technology and create new value for both clients and society at large. As a spinoff from NTT, Japan's telecommunications giant, the company was founded with a commitment to innovation and societal impact that would shape its trajectory for decades to come. From those early days, NTT DATA recognized that technology could be more than just a tool - it could be the foundation for solving complex business challenges and driving meaningful change across industries. Today, NTT DATA stands as a premier global IT services provider, serving 75% of the Fortune Global 100 and generating $30 billion in annual revenue. With 190,000 innovative colleagues across more than 70 countries, the company has evolved into a trusted partner for digital transformation, offering comprehensive services from consulting to connectivity. Backed by $3 billion in annual R&D investment from NTT Group, the organization continues to pioneer breakthroughs in AI, cloud, cybersecurity, and the revolutionary IOWN (Innovative Optical and Wireless Network) initiative. Yet the mission remains unchanged: to use technology as a force for good, creating sustainable solutions that benefit businesses, society, and the planet.

We are a trusted global innovator of IT and business services, delivering practical and scalable technology solutions that help clients navigate their digital journey. Part of the NTT Group and headquartered in Tokyo, we serve approximately 75% of the Fortune Global 100, combining deep industry expertise with cutting-edge capabilities in consulting, applications, managed services, and digital transformation. Our team of more than 190,000 professionals operates across over 70 countries, united by a commitment to creating technology for good. At NTT DATA, we believe in respecting every voice, thinking big and being bold, delivering the outcomes we promise, and winning together as one global team. We invest heavily in innovation - backed by $3 billion in annual R&D from NTT Group - and we're on track to become a Global Top 5 IT services provider. From regenerating rainforests with AI to helping organizations harness the power of cloud, cybersecurity, and generative AI, we work every day to shape a better, more sustainable future for our clients, employees, and society.

Datacom is Australasia's largest home-grown technology company, delivering comprehensive IT solutions and services that help organizations across New Zealand, Australia, and the wider region innovate and grow. With over 6,500 employees operating in 24 locations globally, Datacom brings together deep technical expertise and strong customer relationships to solve complex business challenges. The company's capabilities span cloud services, infrastructure management, cybersecurity, data centre operations, and enterprise consulting, serving government agencies, major enterprises, and growing businesses alike. Founded in 1965 as Computer Bureau Ltd in Christchurch, Datacom has evolved through six decades of technological change to become a trusted technology partner for organizations undergoing digital transformation. The company maintains a strong commitment to developing local technology talent and supporting the communities where it operates. Datacom's Cybersecurity Defence Operations Centres provide managed security services, while its partnerships with leading technology vendors like Microsoft, Cisco, CrowdStrike, and Zscaler ensure customers receive world-class solutions tailored to their specific needs.

In an increasingly volatile world where crises unfold in real-time across physical, digital, and cyber domains, organizations face a critical gap: the time between when an event occurs and when they can respond. Dataminr exists to close this gap completely, delivering the earliest actionable intelligence on breaking events, emerging threats, and unexpected risks. The company's AI platform processes more than one million unique public data sources, synthesizing terabytes of data spanning text, images, video, audio, and sensor signals across 150+ languages and 220+ countries and territories. Dataminr's pioneering ReGenAI technology combines Predictive AI and Generative AI to automatically regenerate real-time briefs as events unfold, while its new Intel Agents autonomously generate critical context without prompts. Trusted by more than 100 U.S. government agencies, 20+ international governments, two-thirds of the Fortune 50, and half of the Fortune 100, Dataminr protects people, assets, and operations. From the battlefield to the boardroom, the company enables faster, more informed decisions when seconds matter most, transforming how organizations respond to fast-emerging risks.

Datavant operates the largest health data network in the United States, connecting more than 80,000 hospitals and clinics, 100% of US payers, and 350+ real-world data organizations globally. Founded in 2017, the platform enables privacy-preserving data linkage across healthcare organizations - letting hospitals, pharma companies, researchers, and payers share clinical information without exposing patient identities. Following a 2021 merger with Ciox Health, the company added record retrieval, data transformation, and AI-powered analytics to its core linking technology, building out full health data logistics capabilities. The threat model is non-trivial: healthcare data flows across fragmented systems with inconsistent security postures, creating exposure points at every handoff. Datavant's infrastructure handles de-identification, tokenization, and controlled linkage at scale, running on AWS with Kubernetes orchestration. The stack includes Snowflake for warehousing, Kafka and Kinesis for streaming pipelines, and Spark/Databricks for transformation workloads. Monitoring runs through DataDog, Grafana, and CloudWatch. Backend services are built in Python, Go, and Java; frontend tooling uses React and Next.js with GraphQL APIs. Security engineering here means designing cryptographic protocols for data minimization, building access controls that span hundreds of institutional boundaries, and maintaining audit trails across a network processing sensitive health records at enterprise scale. The operational surface includes microservices handling real-time data ingestion, batch ETL pipelines built with Airflow and Glue, and customer-facing analytics tools feeding Power BI, Tableau, and Looker dashboards. This is infrastructure-level work where misconfiguration or weak isolation directly impacts patient privacy across the entire US healthcare system.

Databricks operates a unified Data Intelligence Platform that processes and analyzes data for over 15,000 organizations globally, including more than 60% of the Fortune 500. The platform combines data engineering, ETL pipelines, ML model training, and generative AI infrastructure - running on AWS, Azure, and GCP. Founded in 2013 by the engineers who created Apache Spark at UC Berkeley, the company built its architecture around lakehouse design, which merges data warehouse and data lake capabilities into a single system that handles both structured analytics and unstructured ML workloads. The security surface spans distributed data processing at enterprise scale, multi-cloud deployments, and data governance across heterogeneous environments. Unity Catalog provides centralized access control and audit logging for lakehouse assets, while the platform must secure data pipelines that move between cloud storage, compute clusters, and external integrations. Databricks maintains three major open-source projects - Delta Lake for transactional storage, MLflow for ML lifecycle management, and Unity Catalog for governance - each introducing distinct attack vectors and compliance requirements that security teams need to monitor across production deployments. The threat model includes data exfiltration risks during ETL operations, privilege escalation in shared compute environments, and supply chain vulnerabilities in open-source dependencies. Teams work with Python, Scala, and Java codebases, securing Spark clusters that process sensitive data alongside ML models that require both training data protection and inference endpoint hardening. Security engineering at this scale means defending distributed systems where data moves constantly between storage layers, compute resources, and API endpoints across multiple cloud providers.