InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Virtual panel: Security in the Machine Age: Expert Insights on AI Threat Evolution
This virtual panel brings together AI security experts to examine the evolution of AI-driven threats, from prompt injection and data poisoning to agent abuse and AI-powered social engineering. The discussion explores emerging attack patterns, incident response challenges, and the changes security teams must make as AI systems become more autonomous and integrated into critical workflows.
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Understanding ML Model Poisoning: How it Happens and How to Detect it
In this article, the author explores data poisoning as a threat to machine learning systems, covering techniques such as label flipping, backdoors, clean-label poisoning, and gradient manipulation. The article reviews real-world incidents, discusses the challenges of detecting poisoned data, and presents practical defenses, tools, and operational practices for securing ML training pipelines.
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Governing AI in the Cloud: a Practical Guide for Architects
In this article, the author outlines a practical approach to AI governance in the cloud, covering discovery of shadow AI, data classification at creation, IAM-based enforcement, policy-as-code, and operational controls. The article shows how organizations can embed governance into delivery pipelines, balancing security, compliance, and developer productivity without relying on manual processes.
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Artificial Intelligence-Driven Phishing: How Phishing Technique Is Evolving and Implemented
In this article, the author examines how AI is transforming phishing from a manual, targeted activity into an automated and scalable attack model. The article breaks down each stage of the phishing lifecycle, showing how AI improves reconnaissance, profiling, content generation, delivery, and interaction, while outlining layered defenses that combine controls, processes, and user awareness.
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The Technology Adoption Curve, Twenty Years On
Today, June 8th, InfoQ celebrates 20 years. This is not a comprehensive history, but a deliberately selective look at the technologies and practices InfoQ identified early, where they sit on the adoption curve in 2026, and how that curve may evolve over the next five to ten years.
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Article Series: Securing the AI Stack: from Model to Production
This series provides your roadmap for the machine age, exploring how to move from vulnerable prototypes to resilient systems through layered defense, robust MLOps, and integrated governance.
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Two Misconfigurations That Caused Spark OOM Failures on Kubernetes
After migrating Spark pipelines to Azure Kubernetes Service, two infrastructure settings interacted destructively: spark.kubernetes.local.dirs.tmpfs=true backed shuffle spill with RAM instead of disk, and a hard podAffinity rule forced all executors onto one node. Together, they caused repeated OOM kills invisible to standard diagnostics.
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Why Vector Search Alone Isn't Enough: Hybrid Retrieval for RAG
In this article, author Aaditya Chauhan discusses the limitations of RAG pipelines based purely on vector search and how an internal omni-search application using Reciprocal Rank Fusion (RRF) that combines BM25 and vector results, can enhance the search solution.
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The AI Productivity Paradox in Test Automation: Moving beyond Structural Validation to Perception and Intent
The AI productivity paradox states that AI scales whatever abstraction it is built on. If that abstraction is structurally brittle, it scales structural brittleness. This article shows that to build a future of reliable, AI-driven test automation, we must stop scaling DOM-centric abstractions and build a new testing paradigm grounded in perception and intent.
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Architecting Cloud-Native Kafka: from Tiered Storage towards a Diskless Future
This article explores Kafka's transition toward a cloud-native architecture, examining how tiered storage, FinOps telemetry, elastic consumer scaling, virtual clusters, and Share Groups reshape the operational and economic model of event streaming platforms. It also analyzes emerging diskless-storage proposals and their architectural trade-offs.
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Building a Secure MCP Server on AWS for a Million-Company B2B Platform
We wanted to expose a B2B intelligence platform built on more than one million company profiles to an LLM client through an MCP server so a user can ask “find SaaS companies in Germany with 50-200 employees” and receive results through the LLM client. The engineering problem was: how do you make that workflow useful without creating an unsafe bridge between an LLM and production data?
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Time-Series Storage: Design Choices That Shape Cost and Performance
Every time-series database makes a set of storage design decisions: how to lay out rows, when to compress, what to partition on. These decisions determine cost and query performance more than the choice of database itself. This article works through those fundamentals from first principles, using widely available tools like PostgreSQL and Apache Parquet to make each trade-off measurable.