The Independent IT Influencer Event
Event: Security Field Day 12
Appearance: SonicWall Presents at Security Field Day 12
Company: SonicWall
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Personnel: Chirag Saxena
Chirag outlines SonicWall’s network security portfolio with common deployment scenarios, and demos how organizations can secure their sensitive data across branches, HQ, and distributed hybrid workforce.
Event: Security Field Day 12
Appearance: SonicWall Presents at Security Field Day 12
Company: SonicWall
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Personnel: Tarun Desikan
Tarun introduces SonicWall’s newest offering – Cloud Secure Edge (CSE), formerly Banyan Security. He demos how to apply a Zero Trust security model to enable your workforce to securely access any resource from any device.
Event: Security Field Day 12
Appearance: SonicWall Presents at Security Field Day 12
Company: SonicWall
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Personnel: Chirag Saxena
SonicWall has transformed from a traditional network security vendor to a comprehensive modern cybersecurity platform. Chirag will introduce the new SonicWall platform and detail its adoption by our partners and customers.
Event: Security Field Day 12
Appearance: Dell Technologies Presents at Security Field Day 12
Company: Dell Technologies
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Personnel: Steve Kenniston
In this wrap up, Steve Kenniston discusses how Dell is building a holistic cybersecurity solution for the future that embraces key technology advantages. Included is a discussion of post-quantum cryptography and how Dell will be implementing these new standards.
Event: Security Field Day 12
Appearance: Dell Technologies Presents at Security Field Day 12
Company: Dell Technologies
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Personnel: Brian White
In this session, Sr. Consultant Brian White will discuss how Dell Technologies helps elevate customers cyber resilience strategy to preserve the confidentiality, integrity, and availability of data to ensure recoverability in the event of a cyberattack.
Event: Security Field Day 12
Appearance: Dell Technologies Presents at Security Field Day 12
Company: Dell Technologies
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Personnel: Adam Miller
In this session, Adam Miller will discuss Dell Technologies’ strategy to help customers quickly detect and respond to threats across their organization, exploring what we’re hearing from our customers and how solutions from endpoints to managed services can.
Event: Security Field Day 12
Appearance: Dell Technologies Presents at Security Field Day 12
Company: Dell Technologies
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Personnel: Sameer Shah
During this section we will examine ways to minimize cybersecurity vulnerabilities and entry points. We will discuss strategies, best practices, and how Dell products and services help our customers advance these objectives.
Event: Security Field Day 12
Appearance: Dell Technologies Presents at Security Field Day 12
Company: Dell Technologies
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Personnel: Steve Kenniston
In this session we will review Dell’s overall cybersecurity strategy from the ground up starting with our Secure Supply Chain and a review of our key practice areas, Reduce the Attack Surface, Detect and Respond to Cyber Threats and Recover from a Cyberattack to leading with zero trust principles. We will outline how Dell approaches customers to help solve their most critical cybersecurity challenges.
Event: Security Field Day 12
Appearance: DigiCert Presents at Security Field Day 12
Company: DigiCert
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Personnel: Frank Agurto-Machado
In the final portion of the presentation, we provide a demonstration of DigiCert’s Trust Lifecycle Manager product. This demo showcases how the platform streamlines the lifecycle of digital certificates, from issuance to expiration, across a variety of use cases. By illustrating key features like automated workflows, certificate tracking, and centralized management, attendees can see firsthand how the solution enhances security, reduces risks, and simplifies digital trust management in complex environments.
Event: Security Field Day 12
Appearance: DigiCert Presents at Security Field Day 12
Company: DigiCert
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Personnel: Mike Nelson
In this portion of the event, we go over how DigiCert plays a crucial role in establishing and managing digital trust by issuing and overseeing infrastructure that verify identities, encrypt communications, and secure online interactions. We delve deeper into how DigiCert helps mitigate security risks and fosters a trusted, transparent digital environment.
Event: Security Field Day 12
Appearance: DigiCert Presents at Security Field Day 12
Company: DigiCert
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Personnel: Mike Nelson
This portion of the presentation provides an overview of who DigiCert is today, highlighting its current role as a global leader digital trust. Here, we explore DigiCert’s evolution, its contributions to digital trust, and its commitment to future-proofing security in an increasingly digital world.
Event: AI Data Infrastructure Field Day 1
Appearance: Pure Storage Presents at AI Data Infrastructure Field Day 1
Company: Pure Storage
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Personnel: Robert Alvarez
In this presentation, Pure Storage outlines its approach to help organizations meet their AI storage needs while accelerating and simplifying adoption of their AI initiatives. With the Pure Storage platform, organizations can maximize performance and efficiency of AI workflows, unify data, simplify data storage management and take advantage of a scalable AI data infrastructure.
Scaling AI workloads—including large language models (LLMs), retrieval augmented generation (RAG) pipelines, and computer vision applications—introduces practical challenges that extend far beyond theoretical storage capabilities. AI environments require vast amounts of data, and as models grow in size and complexity, traditional storage systems can struggle to keep up with the demands of AI training and inference. Based on real-world customer experiences, this presentation shares valuable insights into building efficient, scalable, and reliable storage infrastructures to support these complex AI pipelines. We’ll explore how organizations can address storage bottlenecks and optimize their systems to ensure seamless AI operations at an enterprise level.
Presented by Robert Alvarez – Consulting Solutions Architect, Pure Storage
Event: AI Data Infrastructure Field Day 1
Appearance: Pure Storage Presents at AI Data Infrastructure Field Day 1
Company: Pure Storage
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Personnel: Boris Feigin
In this presentation, Pure Storage outlines its approach to help organizations meet their AI storage needs while accelerating and simplifying adoption of their AI initiatives. With the Pure Storage platform, organizations can maximize performance and efficiency of AI workflows, unify data, simplify data storage management and take advantage of a scalable AI data infrastructure. An essential cornerstone to it is Pure FlashBlade, a powerful scale-out storage solution specifically designed to meet the unique demands of AI workloads. It simplifies the integration and deployment of all scales of training and inference routines, and helps democratize AI to enterprises looking to accelerate their AI initiatives.
This section looks at the internal details of FlashBlade and Purity//FB, the software stack for FlashBlade. This part of the session focuses on the building blocks as well as the core architectural decisions that enable FlashBlade to shine in modern AI environments across many use cases. Central to Purity//FB’s success is its ability to handle massively parallel data processing, which is a crucial AI data infrastructure requirement for large-scale datasets. This part of the session will cover the modularThis section looks at the internal details of FlashBlade and Purity//FB, the software stack for FlashBlade. This part of the session focuses on the building blocks as well as the core architectural decisions that enable FlashBlade to shine in modern AI environments across many use cases. Central to Purity//FB’s success is its ability to handle massively parallel data processing, which is a crucial AI data infrastructure requirement for large-scale datasets. This part of the session will cover the modular design of Purity//FB, illustrating how its distributed architecture efficiently manages data flow.
Presented by Boris Feigin – Technical Director, FlashBlade Engineering, Pure Storage
Event: AI Data Infrastructure Field Day 1
Appearance: Pure Storage Presents at AI Data Infrastructure Field Day 1
Company: Pure Storage
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Personnel: Hari Kannan
In this presentation, Pure Storage outlines its approach to help organizations meet their AI storage needs while accelerating and simplifying adoption of their AI initiatives. With the Pure Storage platform, organizations can maximize performance and efficiency of AI workflows, unify data, simplify data storage management and take advantage of a scalable AI data infrastructure.
An essential cornerstone to it is Pure FlashBlade, a powerful scale-out storage solution specifically designed to meet the unique demands of AI workloads. With parallel data architecture and multi-dimensional performance, FlashBlade ensures minimal latency and high bandwidth, to accelerate model training and reduce time to AI results.
This section introduces FlashBlade’s design tenets that make it so well suited for AI projects, and deep dives into the technology that enables Pure’s industry-leading energy efficiency and helps customers overcome data center power constraints in their AI build-outs. The discussion in this section also includes Pure’s DirectFlash module which communicates directly with raw flash to enable greater control, optimized performance, and reduced latency.
Presented by Hari Kannan – Lead Principal Technologist, Pure Storage
Event: AI Data Infrastructure Field Day 1
Appearance: Ignite Talks at AI Data Infrastructure Field Day 1
Company: The Futurum Group
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Personnel: Stephen Foskett
Stephen Foskett considers agentic AI, a transformative approach to automation quite unlike popular generative AI models. Unlike traditional AI applications that rely on user inputs, agentic AI involves autonomous agents that act on behalf of users, automating various processes. These agents can either run continuously, collecting information and performing actions, or serve as the glue in business process automation tasks. Foskett emphasizes that agentic AI can handle unstructured inputs and generate well-formed outputs, making it a powerful tool for business process automation. He draws parallels to existing automation tools like Zapier and IFTTT but highlights that agentic AI goes a step further by incorporating a level of intelligence that can adapt to changing inputs and unexpected scenarios.
Foskett provides practical examples to illustrate the potential of agentic AI. He describes scenarios where these AI agents can manage complex tasks such as processing photos, sensors, or handling enterprise data like insurance audits. These agents can adapt to various data formats and incomplete information, making decisions and taking actions autonomously. For instance, an AI agent could convert a PNG file to a JPEG if needed or wait for an upload to complete before proceeding with the next steps. This adaptability makes agentic AI particularly valuable in fields like sales automation and cybersecurity, where the ability to respond to real-time data and adjust actions accordingly can significantly enhance efficiency and effectiveness.
However, Foskett also addresses the challenges and ethical considerations associated with agentic AI. While these agents can act autonomously, they are not infallible and can still make errors or “hallucinate” incorrect data. This necessitates the implementation of guardrails to prevent costly mistakes. Additionally, there are ethical concerns about the promises these agents might make on behalf of businesses, which could lead to unintended commitments. Despite these challenges, Foskett is optimistic about the future of agentic AI, seeing it as a paradigm shift that will revolutionize customer service, operations, and business process automation. He believes that the rapid adoption of AI agent-based platforms is inevitable and will be the next significant wave in AI applications.
Event: AI Data Infrastructure Field Day 1
Appearance: Solidigm Presents at AI Data Infrastructure Field Day 1
Company: Solidigm
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Personnel: Ace Stryker
In this presentation, Ace Stryker from Solidigm discusses the company’s unique value proposition in the AI data infrastructure market, focusing on their high-density QLC SSDs and the recently announced Gen 5 TLC SSDs. He emphasizes the importance of selecting the right storage architecture for different phases of the AI pipeline, from data ingestion to archiving. Solidigm’s QLC SSDs, with their high density and power efficiency, are recommended for the beginning and end of the pipeline, where large volumes of unstructured data are handled. For the middle stages, where performance is critical, Solidigm offers the D7 PS1010 Gen 5 TLC SSD, which boasts impressive sequential and random read performance, making it ideal for keeping GPUs maximally utilized.
The presentation highlights the flexibility of Solidigm’s product portfolio, which allows customers to optimize for various goals, whether it’s power efficiency, GPU utilization, or overall performance. The Gen 5 TLC SSD, the D7 PS1010, is positioned as the performance leader, capable of delivering 14.5 gigabytes per second sequential read speeds. Additionally, Solidigm offers other options like the 5520 and 5430 drives, catering to different performance and endurance needs. The discussion also touches on the efficiency of these drives, with Solidigm’s products outperforming competitors in various AI workloads, as demonstrated by the ML Commons ML Perf Storage Benchmark results.
A notable case study presented is the collaboration with the Zoological Society of London to conserve urban hedgehogs. Solidigm’s high-density QLC SSDs are used in an edge data center at the zoo, enabling efficient processing and analysis of millions of images captured by motion-activated cameras. This setup allows the organization to assess hedgehog populations and make informed conservation decisions. The presentation concludes by emphasizing the importance of efficient data infrastructure in AI applications and Solidigm’s commitment to delivering high-density, power-efficient storage solutions that meet the evolving needs of AI workloads.
Event: AI Data Infrastructure Field Day 1
Appearance: Solidigm Presents at AI Data Infrastructure Field Day 1
Company: Solidigm
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Personnel: Manzur Rahman
In the presentation by Solidigm at AI Data Infrastructure Field Day 1, Manzur Rahman emphasized the critical issue of energy consumption in AI and data infrastructure. He referenced quotes from industry leaders like Sam Altman and Mark Zuckerberg, highlighting the significant challenge energy poses in scaling AI operations. Rahman discussed findings from white papers by Meta and Microsoft Azure, which revealed that a substantial portion of energy consumption in data centers is attributed to hard disk drives (HDDs). Specifically, Meta’s AI recommendation engine and Microsoft’s cloud services found that HDDs consumed 35% and 33% of total operational energy, respectively. This underscores the need for more energy-efficient storage solutions to manage the growing data demands.
Rahman then explored various use cases and the increasing need for network-attached storage (NAS) in AI applications. He noted that data is growing exponentially, with different modalities like text, audio, and video contributing to the data deluge. For instance, hyper-scale large language models (LLMs) and large video models (LVMs) require massive amounts of storage, ranging from 1.3 petabytes to 32 petabytes per GPU rack. The trend towards synthetic data and data repatriation is further driving the demand for NAS. Solidigm’s model for a 50-megawatt data center demonstrated that using QLC (Quad-Level Cell) storage instead of traditional HDDs and TLC (Triple-Level Cell) storage could significantly reduce energy consumption and increase the number of GPU racks that can be supported.
The presentation concluded with a comparison of different storage configurations, showing that QLC storage offers substantial energy savings and space efficiency. For example, a DGX H100 rack with QLC storage consumed only 6.9 kilowatts compared to 32 kilowatts for a setup with TLC and HDDs. This translates to 4x fewer storage racks, 80% less storage power, and 50% more DGX plus NAS racks in a 50-megawatt data center. Rahman also addressed concerns about heat generation and longevity, noting that while QLC may generate more heat and have fewer P/E cycles compared to TLC, the overall energy efficiency and performance benefits make it a viable solution for modern data centers. Solidigm’s high-density drives, such as the P-5520 and QLCP-P-5430, were highlighted as effective in reducing rack space and power consumption, further supporting the case for transitioning to more energy-efficient storage technologies.
Event: AI Data Infrastructure Field Day 1
Appearance: Solidigm Presents at AI Data Infrastructure Field Day 1
Company: Solidigm
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Personnel: Manzur Rahman
Manzur Rahman from Solidigm presented an in-depth analysis of Total Cost of Ownership (TCO) for data centers, emphasizing its growing importance in the AI era. TCO encompasses acquisition, operation, and maintenance costs, and is crucial for evaluating cost-effective, high-performance hardware like GPUs, storage, and AI chips. Rahman highlighted the need for energy-efficient solutions and the importance of right-sizing storage to avoid over or under-provisioning. He explained that TCO includes both direct costs (materials, labor, energy) and indirect costs (overheads, cooling, carbon tax), and uses a normalization method to provide a comprehensive cost per terabyte effective per month per rack.
Rahman detailed Solidigm’s TCO model, which incorporates dynamic variables such as hardware configuration, drive replacement cycles, and workload mixes. The model also factors in the time value of money, maintenance, disposal costs, and greenhouse gas taxes. By comparing HDD and SSD racks under various scenarios, Solidigm found that SSDs can offer significant TCO benefits, especially when variables like replacement cycles, capacity utilization, and data compression are optimized. For instance, extending the SSD replacement cycle from five to seven years can improve TCO by 22%, and increasing capacity utilization can lead to a 67% improvement.
The presentation concluded with a sensitivity analysis showing that high-density QLC SSDs can significantly reduce TCO compared to HDDs. Even with higher upfront costs, the overall TCO is lower due to better performance, longer replacement cycles, and higher capacity utilization. Rahman projected that high-density QLC SSDs will continue to offer TCO improvements in the coming years, making them a promising solution for data centers, particularly in AI environments. The analysis demonstrated that while CAPEX for SSDs is higher, the overall cost per terabyte effective is lower, making SSDs a cost-effective choice for future data center deployments.
Event: AI Data Infrastructure Field Day 1
Appearance: Solidigm Presents at AI Data Infrastructure Field Day 1
Company: Solidigm
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Personnel: Ace Stryker
Ace Stryker from Solidigm presented on the critical role of data infrastructure in AI value creation, emphasizing the importance of quality and quantity in training data. He illustrated this with an AI-generated image of a hand with an incorrect number of fingers, highlighting the limitations of AI models that lack intrinsic understanding of the objects they depict. This example underscored the necessity for high-quality training data to improve AI model outputs. Stryker explained that AI models predict desired outputs based on training data, which often lacks comprehensive information about the objects, leading to errors. He stressed that these challenges are not unique to image generation but are prevalent across various AI applications, where data variety, low error margins, and limited training data pose significant hurdles.
Stryker outlined the AI data pipeline, breaking it down into five stages: data ingestion, data preparation, model development, inference, and archiving. He detailed the specific data and performance requirements at each stage, noting that data magnitude decreases as it moves through the pipeline, while the type of I/O operations varies. For instance, data ingestion involves large sequential writes to object storage, while model training requires random reads from high-performance storage. He also discussed the importance of checkpointing during model training to prevent data loss and ensure efficient recovery. Stryker highlighted the growing trend of distributing AI workloads across core data centers, regional data centers, and edge servers, driven by the need for faster processing, data security, and reduced data transfer costs.
The presentation also addressed the challenges and opportunities of deploying AI at the edge. Stryker noted that edge environments often have lower power budgets, space constraints, and higher serviceability requirements compared to core data centers. He provided examples of edge deployments, such as medical imaging in hospitals and autonomous driving solutions, where high-density storage solutions like QLC SSDs are used to enhance data collection and processing. Stryker emphasized the need for storage vendors to adapt to these evolving requirements, ensuring that their products can meet the demands of both core and edge AI applications. The session concluded with a discussion on Solidigm’s product portfolio and how their SSDs are designed to optimize performance, energy efficiency, and cost in AI deployments.
Event: AI Data Infrastructure Field Day 1
Appearance: Infinidat Presents at AI Data Infrastructure Field Day 1
Company: INFINIDAT
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Personnel: Bill Basinas
Infinidat’s presentation on next-generation data protection and recovery emphasizes the critical need for robust cyber-focused strategies to safeguard corporate infrastructure and critical data assets. Bill Basinas, the Senior Director of Product Marketing, highlights the importance of moving beyond traditional backup and recovery methods to a more proactive approach that prioritizes business recovery. Infinidat’s solutions are designed to protect data efficiently and ensure its availability, leveraging advanced technologies like immutable snapshots and automated cyber protection to provide a resilient and secure storage environment.
The core of Infinidat’s approach lies in its InfiniSafe technology, which offers immutable snapshots, logical air gapping, and instant recovery capabilities. These features ensure that data remains protected and can be quickly restored in the event of a cyber attack. The immutable snapshots are particularly crucial as they cannot be altered or deleted until their expiration, providing a reliable safeguard against data tampering. Additionally, InfiniSafe’s automated cyber protection (ACP) integrates seamlessly with existing security infrastructures, enabling real-time responses to potential threats and ensuring that data is continuously validated and verified.
Infinidat also collaborates with Index Engines to enhance its cyber detection capabilities. This partnership allows Infinidat to offer advanced content-level scanning and pattern matching to detect anomalies and potential threats with high accuracy. The integration of these technologies ensures that any compromised data is quickly identified and isolated, minimizing the impact of cyber attacks. By focusing on recovery first and ensuring that validated data is readily available, Infinidat provides a comprehensive solution that addresses the evolving challenges of data protection in today’s cyber-centric landscape.
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