Upendra Jadon
DataMasque, Solutions Architect
Jersey City, New Jersey, United States
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Upendra Singh Jadon is a senior data privacy and enterprise architecture leader with 15-plus years of experience helping Fortune 500 organizations across financial services, healthcare, and insurance build secure, compliant data ecosystems. He currently serves as senior solutions architect at Datamasque, where he leads enterprise-grade data masking and privacy implementations for Tier-1 insurance carriers and regulated industries.
Previously at TIBCO Software (2018-2024), Upendra served as principal architect for privacy-by-design governance platforms for Fortune 100 clients, supporting $8M+ in pipeline across BFSI verticals. He has delivered 50+ technical workshops, authored architectural standards adopted globally, and contributed reusable data masking frameworks incorporated into enterprise platform strategies.
Upendra’s areas of expertise include data masking and anonymization, master data management (MDM), GDPR/CCPA/HIPAA compliance architecture, cloud data platforms (AWS, Azure, GCP), and AI/ML data enablement. He is a published technical author, frequent speaker, and workshop leader.
Area of Expertise
Topics
Privacy-Preserving AI: Using Synthetic Data to Safely Fine-Tune Enterprise LLMs
Enterprise AI initiatives often stall because teams cannot safely access realistic production data. Security, compliance, and privacy concerns create major blockers for LLM fine-tuning, AI testing, agent development, and API integration workflows.
In this session, we will explore how synthetic and masked data can enable secure AI innovation without exposing sensitive customer information.
Drawing from real-world enterprise implementations across healthcare, insurance, and financial services, this talk covers:
* Common data privacy failures in AI pipelines
* Why traditional anonymization often breaks AI usefulness
* Architectures for privacy-preserving LLM fine-tuning
* Synthetic data strategies for APIs, MCP servers, and AI agents
* Securing AI development workflows while maintaining data realism
* Lessons learned from deploying enterprise-scale masking platforms
Attendees will leave with practical implementation patterns, architectural guidance, and actionable strategies to accelerate AI adoption while remaining compliant with modern data governance requirements.
Privacy-Preserving AI: Using Synthetic Data to Safely Fine-Tune Enterprise LLMs
Enterprise AI initiatives often stall because teams cannot safely access realistic production data. Security, compliance, and privacy concerns create major blockers for LLM fine-tuning, AI testing, agent development, and API integration workflows.
This session explores how synthetic and masked data can enable secure AI innovation without exposing sensitive customer information. Drawing from real-world enterprise implementations across healthcare, insurance, and financial services, the talk will cover practical architectures and implementation strategies for privacy-preserving AI systems.
Topics include:
Common data privacy failures in AI pipelines
Why traditional anonymization often breaks AI usefulness
Architectures for privacy-preserving LLM fine-tuning
Synthetic data strategies for APIs, MCP servers, and AI agents
Secure AI development workflows using production-like data
Lessons learned from enterprise-scale masking deployments
Meetup Panel: The Truth About Agentic AI: What Breaks, What Scales
In this panel, we'll uncover what actually scales in agentic AI -- from hype to production-grade systems, covering topics, including:
What actually breaks when deploying agents
Why most AI systems fail after the demo
Observability, guardrails, and orchestration
The “agent execution gap”
Panelist:
1. Upendra Jadon, Solution Architect @ DataMasque
2. Avichal Chum, Solutions Architect @ Amazon Web Services
3. Rajitha Rupani, Head of AI Adoption @ Percepta
4. Budhaditya Bhattacharya, Director of Product Ecosystem @ Tyk
Moderator: Ari Kamlani, Principal AI Advisor @ Velari Studios (Independent)
Limited seating during Block Party.
Operationalizing Responsible AI: Turning Governance Principles into Production Systems
As AI systems move from experimentation to large-scale deployment, organizations face a growing gap between Responsible AI principles and real-world implementation. This session explores how to operationalize Ethical AI & Governance in practice—covering bias detection and mitigation, model transparency, privacy-preserving design, and compliance alignment with emerging regulations such as the EU AI Act.
Through real-world architectural patterns and deployment lessons from production AI systems, we will examine how to embed governance directly into the ML lifecycle—from data ingestion and training to monitoring and continuous evaluation. Attendees will learn practical strategies for building scalable AI systems that are not only performant, but also auditable, explainable, and aligned with regulatory and organizational standards.
Designing Trustworthy AI Systems: Architecture, Risk, and Real-World Deployment
Trust is becoming a core requirement for modern AI systems, especially in high-impact domains like finance, healthcare, and enterprise decision-making. This session presents a systems-level approach to building trustworthy AI, focusing on risk management, interpretability, governance pipelines, and continuous monitoring.
We will break down how to integrate trust-building mechanisms into AI architecture itself, rather than treating them as post-deployment add-ons. Attendees will gain practical insights into building AI systems that are robust, explainable, and aligned with both user expectations and regulatory requirements.
AI-Driven Data Privacy Engineering: Secure Software by Design in Modern Cloud Environments
As organizations accelerate cloud adoption and AI integration, protecting sensitive data across distributed systems has become a critical software engineering challenge. This session explores how privacy engineering and data masking can be embedded directly into the software development lifecycle to support Secure Software by Design principles.
Drawing from real-world enterprise implementations, the talk will cover practical approaches to dynamic data masking, test data management, anonymization, and secure data provisioning across AWS, containerized platforms, and distributed environments. Attendees will also learn how AI-assisted automation can improve compliance, reduce operational risk, and accelerate secure development workflows.
The session will include implementation lessons, architectural patterns, common pitfalls, and strategies for balancing security, usability, and performance in large-scale enterprise systems.
Key takeaways:
* Building privacy-first architectures in cloud-native applications
* Integrating data masking into DevSecOps pipelines
* Securing non-production environments without impacting development velocity
* Using AI and automation to strengthen data protection workflows
* Practical lessons from enterprise-scale privacy engineering deployments
This session is intended for software architects, security engineers, DevSecOps practitioners, and technology leaders focused on secure software engineering and data protection.
Data Privacy and Security
Data Privacy and Security is the discipline of protecting sensitive information (like personal, financial, or health data) from unauthorized access, misuse, or exposure—while ensuring it remains usable for business, analytics, and operations.
• Data Privacy → What data is collected, how it’s used, and who can access it
• Data Security → How that data is protected technically (encryption, controls, etc.)
DeveloperWeek New York 2026 Sessionize Event
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