# Thought Leadership Published articles for Thought Leadership. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Honoring #IconsOfQuality: Huib Schoots DevFeed: [Honoring #IconsOfQuality: Huib Schoots]() Original publisher: [Read original article]() Author: Rajrupa Roychowdhury Published: 2026-09-16T08:21:48Z Content type: article Language: en Sources: [BrowserStack Blog]() Topics: [Testing](), [Software Testing](), [Artificial Intelligence](), [Software Engineering]() Tags: [ai](), [icons-of-quality](), [quality](), [software-testing](), [thought-leadership]() ### AI overview An interview with Huib Schoots explores software testing as a thinking activity centered on questioning assumptions, exploring risks, and helping teams make better decisions. Schoots also discusses using AI to support testing while preserving human judgment. ### Source excerpt To celebrate the relentless passion and invaluable contributions of leaders in software quality, BrowserStack is proud to honour Icons of Quality. ## Honoring #IconsOfQuality: Ash Winter DevFeed: [Honoring #IconsOfQuality: Ash Winter]() Original publisher: [Read original article]() Author: Rajrupa Roychowdhury Published: 2026-09-16T08:21:31Z Content type: opinion Language: en Sources: [BrowserStack Blog]() Topics: [Testing](), [Artificial Intelligence](), [Large Language Model](), [Development]() Tags: [contributions](), [icons-of-quality](), [testing](), [thought-leadership]() ### AI overview BrowserStack highlights Ash Winter as an influential figure in software testing and quality. The article describes Winter's work in testability, exploratory testing, community collaboration, and the use of automation pipelines and LLMs to support testers. ### Source excerpt To celebrate the relentless passion and invaluable contributions of leaders in software quality, BrowserStack is proud to honour Icons of Quality. ## Safeguarding LLM-Assisted Dev at Guardsquare | Guardsquare DevFeed: [Safeguarding LLM-Assisted Dev at Guardsquare | Guardsquare]() Original publisher: [Read original article]() Author: Noah Fraiture - Backend Engineer Published: 2026-09-15T13:03:38Z Content type: article Language: en Sources: [Guardsquare Blog]() Topics: [Development](), [Large Language Model](), [Cybersecurity](), [Security & Privacy](), [agent observability]() Tags: [ai-agents](), [android](), [containers](), [cybersecurity](), [data](), [dev](), [developer](), [development](), [ios](), [llm](), [llm-gateway](), [thought-leadership]() ### AI overview Guardsquare explains why it adopted LLM-assisted software development despite risks involving sensitive intellectual property, personally identifiable information, and agent access to developer infrastructure. The post describes safeguards including separating sensitive code, isolating agent execution, and controlling model access and outbound data through an LLM gateway and guardrail service. ### Source excerpt This post is not meant to tell you how to use large language models (LLMs) or to claim we've found the right approach. As a cybersecurity company working with particularly sensitive IP, our decision to use LLMs for development was never just about productivity. The broader enthusiasm around LLMs was not itself a reason for us to adopt them quickly. For some time, our position was that the risks outweighed the productivity gains, and incidents involving AI agents elsewhere in the industry reinforced that assessment. ## Honoring #IconsOfQuality: Keith Klain DevFeed: [Honoring #IconsOfQuality: Keith Klain]() Original publisher: [Read original article]() Author: Rajrupa Roychowdhury Published: 2026-09-10T11:56:43Z Content type: article Language: en Sources: [BrowserStack Blog]() Topics: [Testing](), [Artificial Intelligence](), [systems](), [Software]() Tags: [ai](), [community](), [enterprise](), [financial-services](), [icons-of-quality](), [infrastructure](), [insights](), [integration](), [keynote](), [leadership](), [operational](), [quality-engineering](), [systems](), [technology](), [testing](), [thought-leadership](), [uk]() ### AI overview BrowserStack profiles Keith Klain as an influential figure in software quality and testing. The article highlights his work in AI assurance, quality engineering, operational risk, systems thinking, and strategic enterprise transformation, along with his contributions to the testing community and thought leadership. ### Source excerpt To celebrate the relentless passion and invaluable contributions of leaders in software quality, BrowserStack is proud to honour Icons of Quality. ## The state of AI for security: Measuring what matters most for building trust DevFeed: [The state of AI for security: Measuring what matters most for building trust]() Original publisher: [Read original article]() Author: Anshumali Shrivastava Published: 2026-09-09T19:09:14Z Content type: article Language: en Sources: [AWS Security Blog]() Topics: [Cybersecurity](), [Vulnerabilities](), [dataset]() Tags: [advanced-300](), [ai](), [artificial-intelligence](), [benchmark](), [cybersecurity](), [generative-ai](), [machine-learning](), [open-source](), [security](), [security-blog](), [security-identity-compliance](), [thought-leadership](), [vulnerability](), [vulnerability-management]() ### AI overview The article introduces Deception Benchmark, a benchmark for evaluating whether AI models can distinguish real software vulnerabilities from safe code that appears risky. It argues that reducing false alarms is central to making AI security tools trustworthy and compares this focus with existing security evaluations. ### Source excerpt Security teams are starting to actively use AI for security work, including vulnerability triage, penetration testing, threat modeling, incident response, and code review. The promise is speed, but a security tool that moves fast and raises too many false alarms doesn't save time. Engineers spend time on false alarms, on-call is noisier, and teams distrust [...] ## Hybrid cloud orchestration: Modernizing on-premises infrastructure management with AWS DevFeed: [Hybrid cloud orchestration: Modernizing on-premises infrastructure management with AWS]() Original publisher: [Read original article]() Author: Sandeep Singh Published: 2026-09-01T14:01:10Z Content type: tutorial Language: en Sources: [AWS Architecture Blog]() Topics: [Orchestration](), [Kubernetes](), [Amazon EKS](), [AWS Lambda](), [Server](), [Hardware]() Tags: [amazon-dynamodb](), [amazon-eks](), [aws](), [aws-lambda](), [aws-step-functions](), [data-centers](), [deployment](), [event-driven](), [hardware](), [kubernetes](), [observability](), [orchestration](), [server](), [serverless](), [thought-leadership]() ### AI overview Tutorial on designing an AWS-based hybrid cloud orchestration system for centralized lifecycle management of distributed on-premises servers and EKS Anywhere clusters. ### Source excerpt Learn how to build a hybrid cloud orchestration solution that manages distributed on-premises infrastructure at scale using AWS serverless technologies and Amazon EKS Anywhere. Part 1 covers the core event-driven architecture patterns for automating server lifecycle and cluster management across hundreds of sites. ## CoreBreak proves agent guardrails need to live outside the agent DevFeed: [CoreBreak proves agent guardrails need to live outside the agent]() Original publisher: [Read original article]() Author: Tyler Akidau Published: 2026-08-31T00:00:00Z Content type: opinion Language: en Sources: [Redpanda]() Topics: [Artificial Intelligence](), [AI Agent](), [Security](), [Securing AI](), [Amazon Web Services](), [vercel ai sdk](), [Amazon Bedrock](), [Google](), [Vercel]() Tags: [agent](), [agentic-ai](), [ai](), [ai-agent](), [architecture](), [aws](), [bedrock](), [cve](), [google](), [policy](), [security](), [thought-leadership](), [vercel]() ### AI overview The article argues that CoreBreak exposed a structural security flaw in major AI agent stacks and that agent guardrails should be enforced outside the agent's control. ### Source excerpt At Black Hat 2026, CoreBreak exposed the same structural flaw across AWS, Google, and Vercel. Here's why AI agent security enforcement should live beyond the agent's reach. ## Closing the AI agent trust gap with graduated autonomy DevFeed: [Closing the AI agent trust gap with graduated autonomy]() Original publisher: [Read original article]() Author: Dev Arora Published: 2026-08-26T17:33:03Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [Artificial Intelligence](), [AI Agent](), [Authorization](), [Amazon Bedrock](), [Amazon Web Services](), [Large Language Model](), [tracing](), [configuration](), [Provisioning]() Tags: [agent](), [agents](), [ai](), [ai-agent](), [ai-agents](), [amazon](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [api](), [aws](), [compliance](), [evaluation](), [infrastructure](), [large-language-model](), [logs](), [model](), [policy](), [production](), [thought-leadership](), [tracing]() ### AI overview This article presents graduated autonomy as an architectural pattern for closing the trust gap in AI agents. Agents gain permissions through sustained reliability and lose them when performance degrades, using trust scoring, autonomy tiers, pre- and post-execution controls, policy enforcement, provenance, reversibility, and delivery gates. The design uses Amazon Bedrock AgentCore, Amazon DynamoDB, and AWS CodePipeline. ### Source excerpt Most teams give AI agents either full access or read-only, leaving value unused or risk unmanaged. This post describes graduated autonomy, an architectural pattern in which agents earn expanded permissions through sustained reliability and lose them when performance degrades, built on Amazon Bedrock AgentCore, Amazon DynamoDB, and AWS CodePipeline. ## What is Mobile App Reverse Engineering? | Guardsquare DevFeed: [What is Mobile App Reverse Engineering? | Guardsquare]() Original publisher: [Read original article]() Author: Guardsquare Published: 2026-08-25T10:43:34Z Content type: article Language: en Sources: [Guardsquare Blog]() Topics: [Reverse Engineering](), [Mobile Security](), [Mobile](), [Security](), [Ghidra](), [Hopper]() Tags: [analysis](), [android](), [dexguard](), [ios](), [ixguard](), [mobile](), [reverse-engineering](), [security](), [thought-leadership](), [tools](), [vulnerabilities]() ### AI overview The article explains mobile app reverse engineering as the analysis of compiled binaries to reconstruct an app's logic, data flows, and structure. It describes legitimate diagnostic uses, attacker risks such as credential extraction and vulnerability discovery, why iOS and Android protections do not fully protect application code, and static analysis using tools including Ghidra and Hopper. ### Source excerpt Reverse engineering is the process of analyzing compiled software to understand how it works, without having access to the original source code. In the context of mobile applications, it means taking a published app and working backward through its binary to reconstruct its internal logic, data flows, and structure. ## AI-powered clinical trial eligibility and safety using Amazon Bedrock AgentCore DevFeed: [AI-powered clinical trial eligibility and safety using Amazon Bedrock AgentCore]() Original publisher: [Read original article]() Author: Sachin Jain Published: 2026-08-19T13:11:51Z Content type: tutorial Language: en Sources: [AWS Architecture Blog]() Topics: [AI Bots]() Tags: [advanced-300](), [agent](), [agents](), [ai](), [ai-agents](), [amazon](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [aws](), [data](), [how-to](), [knowledge-graph](), [llm](), [reasoning](), [safety](), [thought-leadership]() ### AI overview An AWS architecture tutorial for a clinical-trial eligibility and safety agent. It uses Amazon Bedrock AgentCore, AWS HealthLake, knowledge-graph evidence, evaluations, and human-in-the-loop review to support clinician-led screening decisions. ### Source excerpt AI agents built on Amazon Bedrock AgentCore help clinical trial teams make fast, accurate enrollment decisions while keeping clinicians in control. This post shows how to architect an eligibility and safety screening agent using AWS HealthLake, AgentCore, and AgentCore Evaluations. ## Modern App Protection Requires Polymorphism | Guardsquare DevFeed: [Modern App Protection Requires Polymorphism | Guardsquare]() Original publisher: [Read original article]() Author: Jason Cortlund - Technical Marketing Writer Published: 2026-08-18T13:45:43Z Content type: article Language: en Sources: [Guardsquare Blog]() Topics: [Mobile](), [Mobile Security](), [Polymorphism](), [Security](), [Development](), [Artificial Intelligence](), [Large Language Model]() Tags: [agentic-ai](), [ai](), [development](), [dexguard](), [ixguard](), [large-language-models-llms](), [mobile](), [polymorphism](), [protection](), [reverse-engineering](), [security](), [security-research](), [thought-leadership](), [vulnerabilities]() ### AI overview The article argues that mobile app protection should use polymorphism, with protections changing for each application build. It links this approach to the risks created by development speed, AI-generated code, and scalable reverse-engineering attacks. ### Source excerpt According to credit reporting agency Equifax, "...mobile app security is often neglected by developers -- making apps more vulnerable to fraud." The reason for this is quite simple for most organizations: development speed is the dominant priority. In fact, 79% of mobile developers cite time-to-market pressure as the top barrier to stronger protection. ## Rethinking Mobile App Security | Guardsquare DevFeed: [Rethinking Mobile App Security | Guardsquare]() Original publisher: [Read original article]() Author: Guardsquare Published: 2026-08-04T13:02:55Z Content type: article Language: en Sources: [Guardsquare Blog]() Topics: [Mobile Security](), [Security](), [Testing](), [Development]() Tags: [android](), [development](), [ios](), [mobile](), [mobile-security](), [process](), [protection](), [security](), [testing](), [thought-leadership](), [vulnerabilities]() ### AI overview The article argues that mobile app security failures span code, development, testing, APIs, and production rather than occurring in one isolated place. It recommends integrating continuous security testing into the development lifecycle and protecting application logic such as payment flows, authentication mechanisms, and proprietary algorithms. ### Source excerpt Mobile app security rarely breaks in a single place. Instead, it fails across layers that were never designed to work together. ## The Hidden Costs of DIY Android App Security | Guardsquare DevFeed: [The Hidden Costs of DIY Android App Security | Guardsquare]() Original publisher: [Read original article]() Author: Michael Olechna - Product Marketing Manager Published: 2026-07-28T13:00:55Z Content type: opinion Language: en Sources: [Guardsquare Blog]() Topics: [Security](), [Android](), [Mobile](), [obfuscation](), [R8](), [Open Source]() Tags: [android](), [dexguard](), [mobile](), [obfuscation](), [open-source](), [protection](), [r8](), [security](), [thought-leadership](), [threat-monitoring]() ### AI overview The article argues that DIY Android app security built around open-source tools can leave important protection gaps. It explains that R8 helps compile and optimize Android applications but is not a complete security solution, lacking capabilities such as string encryption, API endpoint security, and control-flow obfuscation. ### Source excerpt The DIY temptation to build with open-source is strong for mobile app developers. After all, their job is to build, secure, and design new applications, features, and architectures that benefit the users of their apps. ## Deploy agents you can trust with centralized AI governance DevFeed: [Deploy agents you can trust with centralized AI governance]() Original publisher: [Read original article]() Author: Kristin Crosier Published: 2026-07-21T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [ai-governance](), [Responsibility & Safety](), [audit trail](), [SIEM, Security, Observability](), [Model Context Protocol](), [observability]() Tags: [agentic](), [ai](), [ai-governance](), [audit-trail](), [autonomous](), [autonomous-agents](), [enterprise](), [hosting](), [infrastructure](), [mcp](), [observability](), [thought-leadership](), [vibe-coding]() ### AI overview This article explains why enterprises struggle to deploy autonomous agentic systems: organizations lack confidence in agents that can access data and tools without unique identities, audit trails, or kill switches. It argues for a centralized AI governance layer enforced by infrastructure that agents cannot access or modify, positioned between agents and the data, tools, identities, and models they use. The proposed governance platform also supports scaling through AI gateways, MCP hosting, observability, and budgeting. ### Source excerpt Discover why organizations are struggling to deploy and scale agentic systems, and how a centralized AI governance platform can help you trust and scale agents. ## What is an Agentic Data Plane? DevFeed: [What is an Agentic Data Plane?]() Original publisher: [Read original article]() Author: Marc Millstone Published: 2026-07-09T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Artificial Intelligence](), [agent observability](), [Amazon API Gateway](), [AWS IAM](), [observability](), [SIEM, Security](), [Amazon Web Services]() Tags: [agentic](), [agents](), [ai](), [ai-agents](), [api](), [api-gateway](), [aws](), [bedrock](), [enterprise](), [governance](), [iam](), [identity](), [identity-management](), [llm](), [models](), [observability](), [siem](), [thought-leadership](), [token](), [tool](), [tools]() ### AI overview The article defines an Agentic Data Plane as a governed governance and runtime layer between enterprise AI agents and the data, tools, identities, and models they access. It explains why existing IAM, API gateways, and observability or SIEM systems do not adequately govern agent-specific actions, identity propagation, model selection, token spending, policy enforcement, and end-to-end tracing. ### Source excerpt The Agentic Data Plane is the governance and runtime layer that connects your AI agents to everything they act on. Learn what it does, why existing tools can't replace it, and what to look for in an enterprise-grade one. ## Mobile App Security in the Age of SoftPOS | Guardsquare DevFeed: [Mobile App Security in the Age of SoftPOS | Guardsquare]() Original publisher: [Read original article]() Author: Guardsquare Published: 2026-07-07T11:42:02Z Content type: article Language: en Sources: [Guardsquare Blog]() Topics: [Mobile](), [Mobile Security](), [Security](), [App](), [API](), [Exploit](), [SDKs](), [Malware]() Tags: [android](), [api](), [compliance](), [credentials](), [devices](), [exploit](), [financial-services](), [integrity](), [ios](), [malware](), [mobile](), [payment](), [payments](), [pos](), [protection](), [sdks](), [security](), [smartphone](), [thought-leadership]() ### AI overview This article examines the security implications of SoftPOS, which turns smartphones into card-present payment terminals. It explains that moving payment functions to mobile devices shifts responsibility to the device, operating system, application integrity, APIs, and runtime environment, and discusses threats including app tampering, reverse engineering, API abuse, credential theft, malware, and bypassed environment checks. ### Source excerpt As retailers look for faster, more flexible ways to accept payments, SoftPOS is becoming a cornerstone of modern payment strategies. It's expected that by 2027, more than 34.5 million merchants will accept payments through SoftPOS technology. The ability to turn any smartphone into a card-present terminal reduces hardware costs, simplifies onboarding, and supports new use cases like curbside, pop-up stores, and in-aisle checkout. ## The four pillars for AI agent governance at scale DevFeed: [The four pillars for AI agent governance at scale]() Original publisher: [Read original article]() Author: Tyler Akidau Published: 2026-06-09T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [AI Agent](), [Authorization](), [ai security](), [Security](), [Artificial Intelligence](), [Securing AI](), [observability](), [prompt injection]() Tags: [ai](), [ai-agent](), [ai-security](), [authorization](), [enterprise](), [observability](), [prompt-injection](), [security](), [thought-leadership]() ### AI overview The article presents enterprise AI agent governance as an infrastructure problem rather than a model-quality problem. It identifies identity, authorization, observability, and accountability as four necessary pillars for deploying imperfect agents safely at scale, with controls enforced through infrastructure outside the agent's reach. ### Source excerpt AI agents need governance infrastructure, not just "better models". Here are the four pillars every enterprise needs to deploy agents safely at scale: identity, authorization, observability, and accountability. ## 5 predictions about agentic AI and analytics in 2026 DevFeed: [5 predictions about agentic AI and analytics in 2026]() Original publisher: [Read original article]() Author: Kristin Crosier Published: 2026-05-12T00:00:00Z Content type: opinion Language: en Sources: [Redpanda]() Topics: [Artificial Intelligence](), [data](), [Security]() Tags: [2026](), [agentic](), [agentic-ai](), [ai](), [analytics](), [aws](), [collaboration](), [cto](), [executive](), [gartner](), [governance](), [predictions](), [thought-leadership]() ### AI overview The article presents five predictions about agentic AI and analytics in 2026, drawing on Gartner's research and Peter Corless's industry observations. It emphasizes the opportunities and governance risks of agentic systems, along with the need for executive accountability, cross-team collaboration, and new skills and best practices. ### Source excerpt Learn about what's top of mind among enterprises planning for agentic systems, and top AI predictions for 2026 and beyond. ## Five principles for governed autonomy with enterprise AI DevFeed: [Five principles for governed autonomy with enterprise AI]() Original publisher: [Read original article]() Author: Robert Siwicki Published: 2026-04-28T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Artificial Intelligence](), [AI Agent](), [observability](), [AI Architecture](), [Streaming](), [real-time](), [Resilience](), [data](), [systems]() Tags: [agentic](), [ai](), [ai-agent](), [ai-architecture](), [architecture](), [customer](), [enterprise](), [event](), [governance](), [logs](), [memory](), [observability](), [real-time](), [resilience](), [streaming](), [thought-leadership](), [tool](), [workflows]() ### AI overview The article presents five principles for evolving Redleader, Redpanda's customer Slackbot, into a governed multi-agent architecture. It emphasizes stable streams, event-based coordination, human oversight, explicit terminal states, and real-time observability to make agent behavior reliable, replayable, measurable, and scalable. ### Source excerpt How to turn opaque agent behavior into governed, provable workflows. Based on our own tried and true experience with Redpanda's customer Slackbot. ## Redleader revamp: How the Agentic Data Plane enables governed multi-agent AI DevFeed: [Redleader revamp: How the Agentic Data Plane enables governed multi-agent AI]() Original publisher: [Read original article]() Author: Robert Siwicki Published: 2026-04-16T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Artificial Intelligence](), [Streaming](), [systems](), [Cloud]() Tags: [agentic](), [ai](), [architecture](), [autonomous](), [cloud](), [multi-agent-systems](), [streaming](), [thought-leadership]() ### AI overview Redleader was revamped from a single prompt-driven agent into a governed, bounded multi-agent system running on Redpanda's Agentic Data Plane. Specialist agents collaborate through structured, durable event streams that support coordination, governance, operational tracking, and continuous improvement. ### Source excerpt How to build safe, multi-agent systems in Redpanda Cloud. ## OpenClaw is not for the enterprise | Tyler Rockwood, Redpanda DevFeed: [OpenClaw is not for the enterprise | Tyler Rockwood, Redpanda]() Original publisher: [Read original article]() Author: Tyler Rockwood Published: 2026-04-14T00:00:00Z Content type: opinion Language: en Sources: [Redpanda]() Topics: [OpenClaw](), [Security](), [Architecture & Design](), [agent observability](), [Artificial Intelligence]() Tags: [agent](), [ai-agents](), [enterprise](), [gateway](), [observability](), [openclaw](), [security](), [thought-leadership]() ### AI overview The article argues that OpenClaw's sandboxing is not an adequate enterprise security model. It proposes a governed architecture centered on a gateway that controls agent access, provides observability, enforces rate limits and guardrails, and supports centralized shutdown of rogue agents. ### Source excerpt A sandbox isn't a security model. OpenClaw runs great on a developer's machine, but it isn't made for enterprise scale. Here's what is. ## Real-time AI: what is it and why it needs streaming data DevFeed: [Real-time AI: what is it and why it needs streaming data]() Original publisher: [Read original article]() Author: Jenny Medeiros Published: 2025-09-04T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Artificial Intelligence](), [Streaming](), [real-time](), [Latency](), [autonomous vehicles](), [Transactions]() Tags: [agentic](), [ai](), [autonomous-vehicles](), [batch](), [latency](), [rag](), [reactive](), [real-time](), [streaming](), [thought-leadership](), [transactions]() ### AI overview The article explains real-time AI as systems that perceive, interpret, and act on streaming events with minimal delay. It contrasts this approach with batch-oriented AI and describes applications such as autonomous-vehicle navigation, fraud detection, and emergency-room prioritization. ### Source excerpt Learn how streaming data takes your AI from reactive to proactive and the real-world applications driving instant intelligence. ## IoT for fun and Prophet: Scaling IoT and predicting the future DevFeed: [IoT for fun and Prophet: Scaling IoT and predicting the future]() Original publisher: [Read original article]() Author: Bryan Wood Published: 2025-07-22T00:00:00Z Content type: tutorial Language: en Sources: [Redpanda]() Topics: [Apache Iceberg](), [Internet of things](), [Redpanda-Connect](), [Streaming](), [Time Series](), [ESP32](), [Amazon Web Services](), [Amazon S3](), [schema-evolution](), [event driven](), [Hardware](), [Kafka]() Tags: [apache-iceberg](), [apache-iceberg-for-scalable-storage](), [aws](), [core](), [devices](), [esp32-mqtt-iot-example](), [event-driven](), [hardware](), [integrating-iceberg-and-redpanda](), [iot](), [iot-data-pipeline-architecture](), [iot-predictive-analytics](), [iot-sensor-data-forecasting](), [predictive-analysis-with-iot](), [prophet-forecasting-for-iot](), [real-time-iot-data-streaming](), [redpanda-connect](), [redpanda-connect-for-iot](), [s3](), [scaling-iot-with-prophet](), [schema-evolution](), [schema-management-in-iot](), [sensor](), [streaming](), [thought-leadership]() ### AI overview This tutorial presents a scalable IoT data pipeline for real-time streaming and forecasting. It combines Redpanda and Redpanda Connect for ingestion and messaging, Apache Iceberg for scalable time-series data storage, AWS IoT for device-to-cloud communication, and ESP32 hardware for affordable connected-device experiments and deployments. ### Source excerpt Check out this real-world example of scaling IoT for predictive analysis with Redpanda, Iceberg, and Prophet--without high costs or complexity. ## Why streaming is the backbone for AI-native data platforms DevFeed: [Why streaming is the backbone for AI-native data platforms]() Original publisher: [Read original article]() Author: Tyler Rockwood Published: 2025-06-24T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Streaming](), [data-platforms](), [Artificial Intelligence](), [AI Development](), [Data analysis](), [Embeddings](), [SQL](), [dashboards](), [personalization](), [Development]() Tags: [ai](), [ai-agents](), [ai-and-data-warehouse-evolution](), [ai-automation-in-products](), [ai-data-analysis-automation](), [ai-enhanced-product-development](), [ai-native-data-platforms](), [ai-product-integration](), [ai-technology-integration](), [dashboards](), [data-analysis](), [data-platforms](), [data-platforms-for-ai-scalability](), [development](), [embeddings](), [modern-data-warehouse-ai](), [personalization](), [real-time-ai-personalization](), [sql](), [streaming](), [streaming-for-ai-data-platforms](), [structuring-data-lakes-with-ai](), [thought-leadership]() ### AI overview The article argues that streaming is the backbone of AI-native data platforms. It presents the modern data warehouse as a central source of context for AI and describes how AI can support analysis, dashboards, SQL queries, trend detection, embeddings, personalization, recommendations, automation, and faster product development. It also emphasizes guardrails and performance measurement for AI-driven automation. ### Source excerpt To power AI at scale, organizations must adapt to the evolving role of the modern data warehouse. Here's what you need to keep up in a rapidly evolving industry. [Next page]()