Quantum Progress and Roadmap: How Close Are We?

Event: Networking Field Day 37

Appearance: BT Presents at Networking Field Day 37

Company: BT

Video Links:

Personnel: Andrew Lord

How close are we to practical quantum computing? Andrew Lord assesses the current state of quantum technology, highlighting recent milestones, and BT’s role in driving innovation. Discussing realistic expectations for the next 10–15 years, including breakthroughs in quantum hardware and software. Gain a clear understanding of where quantum stands today and where it’s heading next.


Quantum Security Overview: The Cryptography Challenge

Event: Networking Field Day 37

Appearance: BT Presents at Networking Field Day 37

Company: BT

Video Links:

Personnel: Andrew Lord

Quantum computing presents a major challenge to traditional cryptography, threatening to break widely used encryption methods. Andrew Lord dives into how quantum computers could undermine current security systems and what’s being done to mitigate the risk. Learn about Post-Quantum Cryptography (PQC) and Quantum Key Distribution (QKD), two critical approaches to safeguarding data in the quantum era. Gain insights into how organisations are preparing for the shift to quantum-resistant security.


Introduction to Quantum Computing and Communications with BT

Event: Networking Field Day 37

Appearance: BT Presents at Networking Field Day 37

Company: BT

Video Links:

Personnel: Andrew Lord

Explore the fascinating world of quantum computing and communications. Andrew Lord introduces the fundamental concepts of quantum mechanics, explaining why quantum technology is poised to be transformational, and explore the potential capabilities of quantum computers. Covering how quantum communications could revolutionise data transfer and security. Discover why the quantum future is closer than you think and how it could reshape industries from finance to healthcare.


It is time for Operations teams to Code or die – Cloud Field Day 22 Delegate Roundtable

Event: Cloud Field Day 22

Appearance: Cloud Field Day 22 Delegate Roundtable Discussion

Company: Tech Field Day

Video Links:

Personnel: Alastair Cooke

This Cloud Field Day delegate roundtable grappled with the evolving and often confusing definition of “AI.” The discussion highlighted the blurring lines between artificial intelligence, machine learning (ML), and the recent surge in popularity of generative AI. Panelists noted the marketing tendency to label any automation or intelligent feature as “AI,” leading to a diluted understanding of the underlying technologies. The lack of clear distinctions confused both consumers and businesses, hindering the effective evaluation and application of AI solutions.

A central theme emerged around the distinction between machine learning, which the panelists viewed as a foundational science providing the underlying mathematical framework, and AI as the resulting intelligent application or product. Generative AI, with its flashy interfaces and readily accessible tools like ChatGPT, has further muddied the waters, overshadowing the broader field of AI and its various forms. The panelists agreed that many companies are using “AI” as a marketing ploy without demonstrating genuine, impactful AI integration.

Ultimately, the conversation concluded that AI, particularly generative AI, is often a feature rather than a standalone product. Its true value lies in enhancing existing tools and processes, automating tasks, and improving efficiency. The panelists stressed the need for focusing on real-world business use cases, moving beyond the hype, and clarifying the specific type of AI being employed. The potential for AI to transform cloud management and various industries was acknowledged, but a cautious approach was urged, emphasizing the need for careful evaluation and responsible implementation.


Just what is AI anyway – Cloud Field Day 22 Delegate Roundtable

Event: Cloud Field Day 22

Appearance: Cloud Field Day 22 Delegate Roundtable Discussion

Company: Tech Field Day

Video Links:

Personnel: Alastair Cooke

This Cloud Field Day delegate roundtable grappled with the evolving and often confusing definition of “AI.” The discussion highlighted the blurring lines between artificial intelligence, machine learning (ML), and the recent surge in popularity of generative AI. Panelists noted the marketing tendency to label any automation or intelligent feature as “AI,” leading to a diluted understanding of the underlying technologies. The lack of clear distinctions confused both consumers and businesses, hindering the effective evaluation and application of AI solutions.

A central theme emerged around the distinction between machine learning, which the panelists viewed as a foundational science providing the underlying mathematical framework, and AI as the resulting intelligent application or product. Generative AI, with its flashy interfaces and readily accessible tools like ChatGPT, has further muddied the waters, overshadowing the broader field of AI and its various forms. The panelists agreed that many companies are using “AI” as a marketing ploy without demonstrating genuine, impactful AI integration.

Ultimately, the conversation concluded that AI, particularly generative AI, is often a feature rather than a standalone product. Its true value lies in enhancing existing tools and processes, automating tasks, and improving efficiency. The panelists stressed the need for focusing on real-world business use cases, moving beyond the hype, and clarifying the specific type of AI being employed. The potential for AI to transform cloud management and various industries was acknowledged, but a cautious approach was urged, emphasizing the need for careful evaluation and responsible implementation.


Protecting the heartbeat of ML/AI use cases with HYCU

Event: Cloud Field Day 22

Appearance: HYCU Presents at Cloud Field Day 22

Company: HYCU

Video Links:

Personnel: David Noy, Sathya Sankaran

HYCU’s presentation at Cloud Field Day focused on the critical need for data protection within the rapidly expanding AI/ML landscape. The increasing adoption of AI mandates across organizations necessitates robust protection for the underlying data lakes and lake houses that fuel these systems, as well as the repositories AI creates. The presentation highlighted the broad coverage HYCU provides for this data stack, emphasizing its innovative solutions for Google BigQuery, a major data framework used in production environments.

A key aspect of the presentation centered on the various reasons why protecting AI data is essential beyond simply recreating results. Speakers discussed the importance of cyber resilience, the ability to revert to specific points in time to address performance issues or model drift (re-vectoring), and the crucial role of data protection in meeting legal and compliance requirements, such as demonstrating the absence of PII or IP infringement in training data. Furthermore, the complexity of reconstructing datasets spread across diverse sources (on-premises and cloud) was underscored as a significant challenge requiring a comprehensive data protection strategy.

The presentation showcased HYCU’s capabilities in addressing these challenges, specifically demonstrating its solutions for BigQuery. HYCU’s platform provides automated discovery and protection for a wide range of Google Cloud services and boasts a patent-pending technology enabling atomic backups. This innovation addresses the critical issue of data synchronization across multiple tables and datasets within a data lake house, ensuring consistency during backups and recovery. The discussion also highlighted the increasing reliance on data lake houses as central repositories for AI-related data, emphasizing the importance of robust protection for these often singular copies of crucial datasets.


Freedom of Choice for Cloud workloads with Dell and HYCU

Event: Cloud Field Day 22

Appearance: HYCU Presents at Cloud Field Day 22

Company: HYCU

Video Links:

Personnel: David Noy, Shiva Raja

Customers face ongoing challenges in determining optimal locations for their core workloads, driven by cloud economics, hypervisor competition, and the need for business agility. To address this, organizations require infrastructure enabling seamless cross-cloud and cross-hypervisor mobility with minimal effort. The HYCU presentation at Cloud Field Day, in collaboration with Dell PowerProtect DD, demonstrated how their joint solution delivers protected mobility across hypervisors and clouds, reducing data loss, recovery time, and costs while mitigating vendor lock-in. A customer story and live demo illustrated the benefits of this approach.

The core of the presentation highlighted the challenges of cloud workload mobility, focusing on application-based workloads hosted on VMs. These challenges included the complexities of navigating differing cloud vendor rules and APIs, the rising costs of cloud storage and egress fees (though this was somewhat mitigated by the fact that some major cloud providers are removing egress fees), and the ever-present threat of cyberattacks such as accidental deletion, insider threats, and ransomware. HYCU’s solution, in conjunction with Dell PowerProtect DD, was presented as overcoming these hurdles through continuous innovation, streamlining manual migration steps, and constant monitoring of changing cloud APIs.

HYCU’s key differentiators included seamless integration with diverse data sources through high-end API-level integration, ensuring secure and performant connections. The partnership with Dell PowerProtect DD, specifically the DDVE virtual appliance, offered proven storage optimization, particularly through deduplication and efficient data transfer, minimizing ingress and egress costs. Combined, HYCU and Dell PowerProtect DD provide air-gapped backup security, utilizing the DD Boost protocol for highly secure data transfers, and offer a range of recovery options, including granular and full restores, to any desired location. A customer case study showcased the success of this integrated approach in migrating workloads between VMware and Azure, emphasizing cost reduction, enhanced security, and simplified migration.


Evolving Cloud Data Protection Market Needs with Dell and HYCU

Event: Cloud Field Day 22

Appearance: HYCU Presents at Cloud Field Day 22

Company: HYCU

Video Links:

Personnel: David Noy

Dell’s presentation at Cloud Field Day, featuring David Noy, Vice President of Product Management at Dell DPS, offered insights into Dell’s cloud strategy and its partnership with HYCU. Noy highlighted the market need for cloud data protection solutions that avoid vendor lock-in, reduce total cost of ownership (TCO), and enhance security. He emphasized Dell’s PowerProtect Data Domain, boasting a significant market share and a 50:1 average deduplication ratio, as a key component of their strategy, enabling significant cost savings compared to cloud-native solutions.

A core element of Dell’s approach is leveraging an ecosystem of partners like HYCU to address the rapidly expanding landscape of cloud workloads and SaaS offerings. This collaboration allows Dell to offer comprehensive data protection for a broader range of applications without needing to develop in-house support for every service. The partnership with HYCU is particularly valuable due to HYCU’s deep understanding of various workloads and their ability to integrate seamlessly with Dell’s PowerProtect Data Domain, utilizing Dell’s Boost protocol for efficient and secure data transfer.

Dell’s existing customer base and established sales channels, including direct sales and channel partnerships, facilitate the deployment of the combined Dell-HYCU solution. The integrated approach simplifies the purchasing process for customers, providing a single quote encompassing both Dell’s storage and HYCU’s software. Noy underscored the significant cost reductions achievable through this combined offering, citing a real-world example showcasing a 66-75% reduction in costs compared to using native cloud infrastructure. This cost reduction, combined with improved resilience and support for modern applications, positions the Dell-HYCU partnership as a compelling solution in the evolving cloud data protection market.


Hybrid Cloud Data Protection An overview of HYCU

Event: Cloud Field Day 22

Appearance: HYCU Presents at Cloud Field Day 22

Company: HYCU

Video Links:

Personnel: Simon Taylor

HYCU, the world’s fastest-growing data protection as a service company, focuses on solving the challenges customers face in today’s data protection landscape. The company’s presentation at Cloud Field Day highlighted its evolution over the past few years and provided a business overview. A key problem HYCU addresses is the explosion of data sources—the average mid-market company uses at least 212—making traditional, single-source data protection solutions inadequate. HYCU’s approach is to provide a single, unified platform for protecting data regardless of its location, whether on-premises, in the public cloud, or within various SaaS applications.

The presentation emphasized HYCU’s comprehensive coverage, protecting over 80 SaaS services compared to the aggregate of less than 10 protected by all other companies in the Gartner Magic Quadrant. This broad protection extends to hybrid cloud infrastructure, modern cloud applications and services (including BigQuery, Cloud SQL, Jira, Confluence, and Salesforce), and even DevOps and ITSM tools like GitHub and GitLab. The company boasts a 91 Net Promoter Score and numerous industry awards, including recognition from Gartner, Google, and GigaOM. HYCU’s platform, R-Cloud, features R-Graph, an auto-discovery tool that visualizes data location and compliance status, showcasing its value in simplifying data protection management for even complex organizations.

A core component of HYCU’s strategy is its 100% channel approach and its organic growth since its founding in 2018. The company has focused on building a user-friendly platform that solves the “equivalency problem,” making data protection equally simple regardless of the source. Their partnership with Anthropic has been instrumental in rapidly expanding their integrations, surpassing competitors with over 80 integrations. The presentation concluded with a discussion of the often-overlooked need for data protection in emerging areas like AI/ML and DBaaS, further highlighting HYCU’s mission of building a safer world through universal data protection.


Breaking Down Silos – Unified Security for Faster Automated Threat Resolution with Fortinet

Event: Cloud Field Day 22

Appearance: Fortinet Presents at Cloud Field Day 22

Company: Fortinet

Video Links:

Personnel: Julian Petersohn

Security teams often struggle with disparate security tools and disjointed workflows, leading to delayed threat responses. Fortinet’s presentation at Cloud Field Day showcased how its FortiSOAR platform addresses this challenge by orchestrating threat intelligence from FortiNDR (Network Detection and Response) and FortiCNAPP (Cloud Native Application Protection Platform). This integration seamlessly connects network and cloud threat data, enabling automated responses to reduce SOC workload and accelerate threat mitigation.

The demonstration highlighted how FortiSOAR ingests alerts from various sources, including FortiNDR and FortiCNAPP, correlating them to build a comprehensive picture of an attack. For example, FortiNDR provides network-level details like malicious IP addresses and file downloads, while FortiCNAPP offers insights into cloud-based activity, such as suspicious container behavior. FortiSOAR then uses these combined insights to trigger automated remediation playbooks, such as blocking malicious IP addresses, deleting compromised deployments, and redeploying clean instances.

Furthermore, FortiSOAR leverages AI capabilities, currently utilizing OpenAI’s GPT technology but with the potential for other integrations, to enhance threat analysis and incident response. This AI assistance allows SOC analysts to gain better context from alerts, receive severity assessments, discover similar incidents, and even automate some of the investigative and response processes. This ultimately improves the efficiency and effectiveness of security operations, enabling faster and more accurate threat resolution.


Network Intelligence Unleashed Turn Traffic into Actionable Threat Insights with Fortinet

Event: Cloud Field Day 22

Appearance: Fortinet Presents at Cloud Field Day 22

Company: Fortinet

Video Links:

Personnel: Derrick Gooch, Gabriel O’Brien

Fortinet’s Cloud Field Day presentation highlighted the untapped potential of network traffic for security insights. Derrick Gooch demonstrated how Fortinet’s AI-powered threat detection analyzes virtual machine traffic in real-time, minimizing performance impact and transforming raw network data into actionable intelligence for swift threat detection and mitigation within cloud environments. This is crucial because attackers frequently bypass perimeter defenses, making internal network monitoring essential.

The core of Fortinet’s solution, FortiNDR, leverages AI and machine learning to identify anomalies and malware. It ingests data from various sources, including hardware and virtual appliances, spanning on-premises and cloud environments (supporting AWS, Azure, and Google, as well as popular hypervisors). FortiNDR analyzes this data, classifying traffic as benign, non-malicious, or suspicious, using advanced techniques like gradient-boosted decision trees for web shell detection and deep neural networks for domain generation algorithm identification. The system also incorporates malware analysis through unpacking and deep code analysis using artificial neural networks.

Beyond detection, FortiNDR facilitates remediation and escalation through integration with Fortinet’s security fabric (FortiGate, FortiNAC, FortiSwitch, FortiSOAR) and third-party tools (CrowdStrike, Active Directory, VirusTotal, Cyber Threat Alliance). This allows for automated responses like blocking malicious IP addresses or integrating with existing SIEM systems (FortiAnalyzer, Cortex, Splunk). The presentation concluded with a technical overview of how FortiNDR is deployed in an AWS environment, emphasizing the use of traffic mirroring for efficient data collection.


Visibility into the Cloud – Identify Risks and Threats in Your Cloud Environment with Fortinet

Event: Cloud Field Day 22

Appearance: Fortinet Presents at Cloud Field Day 22

Company: Fortinet

Video Links:

Personnel: Derrick Gooch, Gabriel O’Brien

As cloud adoption accelerates, security teams struggle to keep pace with emerging risks and active threats. Fortinet’s FortiCNAP (Cloud Native Application Protection Platform) provides deep visibility into cloud environments, proactively identifying vulnerabilities and detecting real-time threats to enhance cloud security and compliance. The platform addresses cloud security as a big data problem, ingesting data from various cloud sources and performing hourly analysis to establish a baseline of normal activity. FortiCNAP then focuses on highlighting only anomalous activities, such as unusual geolocation logins or unexpected outbound network connections, reducing alert fatigue and prioritizing critical security events.

FortiCNAP achieves this visibility through a combination of agentless and agent-based approaches. Agentless capabilities integrate directly with cloud providers using infrastructure-as-code, pulling in activity logs, configurations, and permissions data for analysis. Agent-based capabilities, requiring some developer involvement for deployment, offer real-time telemetry, including network monitoring, file change detection, and limited vulnerability scanning. Crucially, the agent’s functionality is designed to be performant and avoid impacting the underlying system resources. Furthermore, FortiCNAP integrates with code repositories like GitHub, Bitbucket, and GitLab to enable code security scanning and identify vulnerabilities before they reach production.

All these features are unified under a single platform, although different pricing tiers might be available depending on the included features. Customers can choose to leverage only the components relevant to their needs, such as opting out of the code scanning functionality if they already have a suitable solution in place. The presentation demonstrated FortiCNAP’s capabilities through a live scenario, showcasing its ability to detect and analyze various attack vectors, including cryptojacking, compromised Kubernetes clusters, and reverse shells. The platform’s ability to correlate multiple events into composite alerts and provide detailed root cause analysis, coupled with its integration with existing developer workflows via Terraform modules, GitHub integration, and VS Code plugins, positions FortiCNAP as a comprehensive solution for improving cloud security posture and reducing the burden on both security and development teams.


Stopping the Unseen, AI for High Confidence Low-risk Threat Response with Fortinet

Event: Cloud Field Day 22

Appearance: Fortinet Presents at Cloud Field Day 22

Company: Fortinet

Video Links:

Personnel: Aidan Walden, Julian Petersohn

Cyber threats are increasingly sophisticated, often evading traditional detection methods and remaining undetected until significant damage occurs. Fortinet’s presentation at Cloud Field Day highlighted how AI-driven insights from network and cloud intelligence significantly improve threat detection, enhance overall visibility, and enable faster, lower-risk responses for security teams. The core message emphasizes empowering security operators by providing high-fidelity insights that allow for efficient and safe remediation, ultimately minimizing attack surface and reducing organizational risk.

The presentation detailed how Fortinet’s solutions address the challenges of increasingly complex cloud environments with numerous ingress/egress points and ephemeral networks. They ingest a vast amount of signals from various sources, both Fortinet and third-party, then utilize advanced machine learning to correlate this data into high-confidence threat assessments. This composite risk view prioritizes threats and presents actionable information to security operators, even providing AI-powered assistance for investigation and remediation steps, thus reducing the burden of low-value tasks.

A live demonstration showcased how Fortinet’s AI-powered security solutions could uncover and prevent an attack. The scenario, presented by a Fortinet threat analyst, simulated a real-world attack leveraging a known vulnerability, demonstrating the system’s ability to detect the initial compromise, trace the attack’s escalation across cloud infrastructure, and pinpoint critical misconfigurations. The presentation concluded by emphasizing Fortinet’s commitment to innovation and leadership in threat intelligence, backed by a significant patent portfolio and a broad range of security solutions designed to assist organizations in maintaining security posture in dynamic cloud environments.


The IPM Platform in Action with Catchpoint

Event: Cloud Field Day 22

Appearance: Catchpoint Presents at Cloud Field Day 22

Company: Catchpoint

Video Links:

Personnel: Brandon DeLap, Mehdi Daoudi

Catchpoint’s Internet Performance Monitoring (IPM) platform proactively ensures the resilience and performance of digital experiences. The platform’s core function is to reduce mean time to repair (MTTR) by enabling detection, identification, escalation, and validation of issues. This is achieved through a multi-faceted approach that goes beyond simply identifying symptoms, such as a slow website, to pinpoint the root cause through triangulation of data from various sources.

Central to Catchpoint’s approach is its use of “mystery shoppers,” or synthetic monitoring agents, deployed globally across various ISPs and even in residential locations to provide a comprehensive view of performance from the end-user perspective. This is complemented by real user monitoring (RUM) using JavaScript beacons, BGP data collection for network-level insights, and integrations with tools like WebPageTest.org and Tundra for deeper application performance analysis. All this data feeds into a central platform for dashboards, alerting, and analysis.

The platform provides a holistic view of digital service health, focusing on accessibility, uptime, speed, and reliability. Key features include the StackMap visualization for a single-pane-of-glass view of performance, Internet Sonar for AI-driven issue identification, and end-to-end tracing using open telemetry. Catchpoint also offers advanced features like automated experiments to help web developers optimize performance and integrates with various alerting and ticketing systems for seamless workflow integration. The platform’s flexibility allows for customization to meet the specific needs of diverse organizations, from simple alert routing to sophisticated data integration with observability platforms.


The Internet Performance Mamagement (IPM) Platform with Catchpoint

Event: Cloud Field Day 22

Appearance: Catchpoint Presents at Cloud Field Day 22

Company: Catchpoint

Video Links:

Personnel: Mehdi Daoudi

Catchpoint’s Internet Performance Monitoring (IPM) platform proactively ensures the resilience and performance of digital experiences. The platform’s core function is to reduce mean time to repair (MTTR) by enabling detection, identification, escalation, and validation of issues. This is achieved through a multi-faceted approach that goes beyond simply identifying symptoms, such as a slow website, to pinpoint the root cause through triangulation of data from various sources.

Central to Catchpoint’s approach is its use of “mystery shoppers,” or synthetic monitoring agents, deployed globally across various ISPs and even in residential locations to provide a comprehensive view of performance from the end-user perspective. This is complemented by real user monitoring (RUM) using JavaScript beacons, BGP data collection for network-level insights, and integrations with tools like WebPageTest.org and Tundra for deeper application performance analysis. All this data feeds into a central platform for dashboards, alerting, and analysis.

The platform provides a holistic view of digital service health, focusing on accessibility, uptime, speed, and reliability. Key features include the StackMap visualization for a single-pane-of-glass view of performance, Internet Sonar for AI-driven issue identification, and end-to-end tracing using open telemetry. Catchpoint also offers advanced features like automated experiments to help web developers optimize performance and integrates with various alerting and ticketing systems for seamless workflow integration. The platform’s flexibility allows for customization to meet the specific needs of diverse organizations, from simple alert routing to sophisticated data integration with observability platforms.


Innovate or Perish IT Organizations Must Rethink Observability in the Cloud Age with Catchpoint

Event: Cloud Field Day 22

Appearance: Catchpoint Presents at Cloud Field Day 22

Company: Catchpoint

Video Links:

Personnel: Mehdi Daoudi

Catchpoint’s presentation at Cloud Field Day highlighted the critical dependence of modern organizations on digital experiences for business operations and employee productivity. The increasing frequency of outages and performance issues underscores the limitations of traditional application-centric monitoring. By shifting to proactive user experience monitoring, Catchpoint argues, organizations can prevent costly incidents, improve revenue generation, and boost employee satisfaction. This approach is vital in today’s complex digital landscape where users expect the flawless performance seen in companies like Google, Apple, and TikTok.

Mehdi Daoudi, CEO of Catchpoint, emphasized the growing complexity of modern applications, which often involve numerous third-party vendors, CDNs, and cloud providers, resulting in hundreds of requests per web page. This complexity necessitates a shift from reactive to proactive monitoring. Catchpoint’s solution offers an “internet-centric” approach, visualizing the entire internet stack and providing insights into the performance of every component, even those outside the direct control of the organization. This complements existing APM tools by providing an “outside-in” perspective, enabling faster identification and resolution of issues.

The presentation showcased how Catchpoint’s Sonar solution, combined with real-user and synthetic data, allows organizations to identify problems proactively, even those caused by external factors like fiber cuts. This data can empower manual or automated remediation efforts, for example, by dynamically rerouting traffic to alternative CDNs or cloud providers based on performance. Catchpoint also highlighted the growing trend toward AI-driven automation, using insights from monitoring data to create automated playbooks and runbooks for resolving performance issues, furthering the shift toward a proactive and autonomous IT management strategy.


Focused on Monitoring Internet User Experience Introducing Catchpoint

Event: Cloud Field Day 22

Appearance: Catchpoint Presents at Cloud Field Day 22

Company: Catchpoint

Video Links:

Personnel: Mehdi Daoudi

Catchpoint, founded in 2008 by Mehdi Daoudi and two others, focuses monitoring on the user experience rather than solely on the application stack. Daoudi’s experience at DoubleClick, where he was responsible for resolving a major outage, solidified his belief that performance monitoring is not just an IT concern, but a critical business imperative impacting revenue, brand reputation, and employee morale. Catchpoint works with large global organizations, emphasizing the importance of trust and reliability in maintaining customer relationships and preventing costly outages.

The company advocates for an end-user-centric approach to monitoring, arguing that traditional infrastructure-up methods are insufficient in today’s complex, distributed environments. They highlight the inefficiency of waiting for massive error thresholds to trigger alerts, emphasizing the need to detect problems early, before they impact customers and cause brand damage. Catchpoint uses real-world examples of how their platform has helped companies identify and resolve performance issues much more quickly than using traditional methods, saving time, money, and reputational harm.

Daoudi stressed that many organizations overcomplicate their monitoring strategies, accumulating excessive data that hinders rather than helps problem solving. He advocates for a simpler, more focused approach where monitoring is designed to answer specific business questions related to key user actions and business outcomes. He draws on examples from Google, highlighting their performance-driven culture and the importance of designing for performance from the outset. Ultimately, Catchpoint aims to help businesses move beyond reactive troubleshooting to proactive, user-experience-driven monitoring to ensure that their digital services are consistently reliable and meet user expectations.


A Deep Dive into AIOps with Selector AI

Event: Cloud Field Day 22

Appearance: Selector AI Presents at Cloud Field Day 22

Company: Selector AI

Video Links:

Personnel: Sachin Natu

Selector AI’s presentation at Cloud Field Day 22 offered a technical deep dive into the architecture and functionality of its platform. The core of the platform relies on machine learning (ML) techniques for data ingestion and analysis, ingesting raw data such as metrics, events, and syslogs to understand network behavior without generating content. This ML-driven process focuses on accuracy, utilizing methods like regressions, clustering, and cosine similarities to identify patterns and correlations within the data, avoiding the hallucinations often associated with Large Language Models (LLMs).

The platform’s unique strength is its ability to handle a wide variety of data sources, leveraging a declarative ETL and compiler to easily ingest new data types. This flexibility is showcased by the system’s ability to process data from diverse sources, ranging from legacy network devices and modern cloud services to custom CSV files. The system’s architecture is built on Kubernetes, ensuring horizontal scalability to handle the volume and velocity of data ingested, with a strong focus on creating context through the integration of CMDB data and metadata to give meaning to the raw data. This data integration is a critical component, bringing together disparate data silos to provide a holistic view of network operations.

Generative AI plays a supporting role in Selector AI’s platform, primarily enhancing user experience. It translates natural language queries into the platform’s query language and converts the resulting JSON output back into human-readable English. This use of generative AI is carefully managed to ensure accuracy, acting as a complementary tool to the underlying ML engine and not replacing it. The platform is designed to be flexible, allowing customers to utilize various LLMs and ensuring that the system learns and adapts to the specific details of each customer’s network infrastructure over time. The company emphasizes its commitment to customer support, providing ongoing platform maintenance and support throughout the entire lifecycle of their product implementation.


Customer Use Cases and Product Demonstration with Selector AI

Event: Cloud Field Day 22

Appearance: Selector AI Presents at Cloud Field Day 22

Company: Selector AI

Video Links:

Personnel: John Heintz

Selector AI’s presentation at Cloud Field Day 22 showcased real customer use cases demonstrating how its platform aids operators and management teams in making critical cloud-based business decisions. John Heintz, Global Systems Engineering Director, highlighted the platform’s ability to resolve critical issues rapidly, emphasizing its unique position in meeting the daily needs of operators and management. The presentation featured a recorded demo showcasing the platform’s functionality, including the creation of “smart tickets” that automatically summarize events, provide context, and suggest remediation actions. The demo further illustrated how these tickets integrate with collaboration tools like Slack, offering a streamlined workflow for incident management.

A key aspect of the demo involved the platform’s dynamic dashboarding capabilities. These dashboards are contextually driven, automatically generated based on the details of an alert, presenting relevant topology renderings, color-coded KPIs, and drill-down capabilities for deeper investigation. The presenter addressed audience questions regarding the dashboards’ dynamic generation, highlighting the utilization of JSON data from alerts to build visualizations on the fly, while emphasizing the possibility of customer customization. He also explained how the platform’s chat ops functionality allows users to interact with the system using natural language, eliminating the need for complex queries and streamlining the investigation process.

The demo also showcased Selector AI’s capacity to integrate various data sources, including cloud providers’ native monitoring services, and its ability to correlate events across different systems to pinpoint root causes. The presenter highlighted features like correlation graphs and a time-series DVR function, which allow users to visualize the sequence of events leading to an incident. Finally, the discussion addressed the platform’s architecture, emphasizing its scalability built on Kubernetes and the use of techniques like Kafka for efficient data processing, and the flexibility to deploy agents to collect data from various environments. This ability to ingest data from diverse sources, whether existing monitoring tools or directly from the target systems, represents a core strength of the Selector AI platform.


A Day in the Life of the Enterprise Administrator with Selector AI

Event: Cloud Field Day 22

Appearance: Selector AI Presents at Cloud Field Day 22

Company: Selector AI

Video Links:

Personnel: Sachin Natu

Selector AI’s presentation at Cloud Field Day 22, delivered by VP of Product Sachin Natu, focused on illustrating a typical day for a cloud engineer managing Fortune 500 company environments. Natu highlighted the significant challenges these engineers face, emphasizing the complexity arising from the multitude of technologies, administrative domains, and siloed views across on-premise networks, multiple ISPs, and hybrid cloud deployments. The core problem showcased was the difficulty in troubleshooting even a seemingly simple issue like a malfunctioning chatbot, requiring investigation across numerous interconnected systems and data sources.

The presentation centered around Selector AI’s platform, which aims to address these complexities by providing a Slack-native AI-powered interface. Using a scenario involving a chatbot outage, Natu demonstrated how the platform proactively identifies problems, correlates events across various systems (including network devices, ISPs, and cloud services), and provides actionable recommendations within the Slack workspace itself. The platform doesn’t simply identify issues; it also offers contextual information, such as historical data and user-added notes, to build a clear picture of the situation, allowing for more efficient and informed decision-making.

Crucially, Natu differentiated Selector AI’s approach from generative AI, emphasizing the platform’s reliance on machine learning techniques for accurate data analysis and correlation, rather than generating hypothetical solutions. While leveraging Large Language Models (LLMs) for natural language interaction, the core of the platform uses precise data analysis to ensure accurate representations of the systems being monitored. This approach addresses concerns about the reliability of AI-driven insights in critical infrastructure management and ensures that the recommendations provided are grounded in factual data and historical context. The presentation concluded with a planned deeper dive into the platform’s architecture and data processing methods.


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