The threat isn't waiting for a future quantum computer. It's already underway. Does the growth of “Harvest Now, Decipher Later” HNDL represent a clear and present danger? Do the systems we use today have a path forward? Who is providing new innovations and how are they recommending we migrate? This session examines the real-world implications of HNDL, identifies which cryptographic systems are most vulnerable, and explores the emerging technologies and migration strategies being recommended by security leaders.
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A sufficiently powerful quantum computer could solve mathematical problems that classical computers cannot solve efficiently. The remedy that National Institute of Standards and Technology has developed to address the threat of quantum computers breaking today’s encryption is called Post-Quantum Cryptography (PQC). Embedded in PQC is the concept of Cyber-Agility designing encryption methods that can be swapped quickly in the future. This session explores Post-Quantum Cryptography (PQC) and the emerging concept of cryptographic agility—the ability to rapidly adapt encryption technologies as new threats and standards emerge.
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Here is some good news, discovering your data’s vulnerability is easier than ever thanks to AI-powered tools. The bad news is that you may discover flaws that need to be updated. Whatever the result, the impact of quantum security threats is the decision to stay vigilant and continue to monitor and prioritize PQC security solutions.
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Edge AI and robotics are forcing architects to make hard choices about what runs in the cloud, what runs at the edge, and how much autonomy to give machines in the field. From warehouse robots and mobile swarms to humanoids and industrial cobots, we see the same pattern: millisecond decisions happen on-device, while learning, coordination, and governance live across a distributed edge–cloud fabric. This session mines concrete lessons from robotics—swarm intelligence, neurosymbolic control loops, and fleet management—to frame a pragmatic playbook for “balancing the edge.” We will examine when to push more intelligence onto constrained devices, when to pull work back to the cloud, and how to design architectures that evolve over time without breaking safety or compliance. Attendees will leave with patterns, cautionary tales, and reference questions they can ask their own vendors and teams.
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Building and Deploying AIoT solutions delves into the practical aspects of creating and implementing AIoT systems. Starting with establishing best practice measurements for Testing Frameworks, Scalable Architecture Design, Continuous Integration and Deployment (CI/CD). This session will look at proven methodologies and strategies for designing, developing, and deploying AIoT solutions; followed by Real-World Case studies across various industries to provide insight into successful implementations.

Attendees will learn:

What are the critical components involved in building and deploying AIoT solutions.

What insights come from practical challenges and solutions associated with AIoT systems.

What case studies demonstrate about driving innovation and efficiency.

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Traditional streaming analytics struggles to keep pace with the scale, velocity, and complexity of modern IoT telemetry. Digital twins change the game by maintaining rich, real-time state for each device or asset, while embedded ML and AI models continuously interpret incoming data and trigger intelligent actions within milliseconds. In this session, William Bain, CEO and founder of ScaleOut Software, will show how pairing real-time digital twins with machine learning unlocks a new class of streaming analytics—delivering deeper situational awareness, earlier anomaly detection, and more precise, automated responses across large fleets and critical systems. What You’ll Learn How real-time digital twins combined with ML/AI transform streaming analytics architectures, enabling stateful, per-entity intelligence at massive scale. Practical design patterns and deployment strategies for building AI-powered digital twin solutions that improve detection, prediction, and decision-making in live IoT environments.
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