Dippu Kumar Singh
Senior Solutions Architect (For Emerging Data & Analytics)
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Dippu Kumar Singh has over 16 years of experience at the intersection of industry innovation and advanced research. He is a recognized authority in building scalable, trustworthy, and commercially viable AI systems. Being a Leader for Emerging Data & Analytics at Fujitsu North America, Dippu specializes in bridging the gap between theoretical AI concepts and enterprise-grade implementation. His strategic leadership has spearheaded multi-million in sales pipelines and delivered remarkable savings through AI-driven optimizations in transportation, manufacturing, utilities, and supply chain logistics.
Agentic AI and Security for Enterprise
Agentic AI is poised to become the new engine of enterprise productivity, autonomously running workflows and making decisions. Yet, for every promise of efficiency, there lies a shadow of unprecedented security risk. As Gartner confirms, the path to maturity is fraught with trust issues and a vastly expanded attack surface.
This session moves beyond the headlines to offer a C-suite playbook for secure Agentic AI adoption. We will analyze real-world adoption data, deconstruct the security models of hyperscalers, and provide a concrete framework for governance.
We will learn how to:
- Deconstruct the critical 'build vs. buy' dilemma and the security risks of each path.
- Evaluate the competing security ecosystems of Microsoft (Security Copilot) and Google (A2A Protocol) to inform your platform strategy.
- Implement a practical governance model to manage agent autonomy, control data access, and ensure explainable AI.
This session will be focused to harness the power of Agentic AI while fortifying enterprises against the next generation of threats.
Human Rights by Design: Building Trusted & Innovative AI for Tomorrow
The narrative often pits AI innovation against human rights. But what if protecting privacy and freedom isn't a constraint, but a catalyst for better, more trusted AI? Let us reframe the challenge: how do we embed Human Rights by Design into the core of AI development and deployment?
We'll explore the synergistic relationship between ethical considerations and successful AI implementation. Forget mere compliance; we're talking about building a competitive advantage through responsibility. Key areas include:
1) Proactive Privacy Engineering: Techniques that build privacy in, not bolt it on.
2) Fairness & Non-Discrimination: Mitigating bias to uphold freedoms and ensure equity.
3) Empowering Users: Designing AI interactions that respect autonomy and provide meaningful control.
4) Multi-Stakeholder Blueprints: How technologists, policymakers, and civil society can forge effective, innovation-friendly guidelines.
We will gain concrete strategies for integrating human rights considerations into their AI projects, fostering public trust, and mitigating risks. Learn how a 'Human Rights by Design' approach leads to more robust, sustainable, and ultimately more valuable AI solutions for everyone.
The Data Driven Intelligence Innovation Lifecycle: From Experimentation to Real-World Value
Data-driven Intelligence innovation is realized by employing proven techniques to identify valuable patterns, conducting experiments through a structured and methodical approach, swiftly terminating those that don't yield results, and nurturing successful ideas into tangible business value. Uncovering value within the vast amounts of data available today can open new business opportunities, enhance quality and efficiency, elevate customer service, and create differentiation to maintain a competitive edge. Imagine discovering value by applying human behavior analytics to proactively assist a customer in need within an aircraft cabin, automating quality assurance in airline engine production using cameras and vision intelligence, or utilizing a digital twin of your airline systems to predict and prevent maintenance issues.
With the immense volume of data generated by IoT sensors and IT systems, many companies struggle to identify the critical data and determine when to apply solutions like Artificial Intelligence (AI), vision intelligence, or real-time digital twins. Some find their experiments fall short of achieving desired objectives because ideas remain stuck in the experimental phase. Today's data doesn't always fit neatly into traditional tables and databases; much of it is unstructured, such as visual data from cameras, and often stored in disparate silos, complicating the task of correlating and identifying relationships. This data demands a different analytical approach than what has been traditionally applied. By implementing a focused data-driven innovation strategy aligned with your corporate goals, you can unlock the latent value within your data.
PlatformCon 2026 Sessionize Event Upcoming
AI DevSummit + DeveloperWeek Management 2026 Sessionize Event Upcoming
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