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AI tool Xbow is one of America's best hackers

AI tool Xbow is one of America's best hackers

Time of India2 days ago

A hacker named Xbow has topped a prestigious security industry US leaderboard that tracks who has found and reported the most vulnerabilities in software from large companies. Xbow isn't a person — it's an artificial intelligence tool developed by a company of the same name.This is the first time a company's AI product has topped HackerOne's US leaderboard by reputation, which measures how many vulnerabilities have been found and the importance of each one, according to HackerOne cofounder Michiel Prins. Now, the year-old startup has raised $75 million in a new funding round led by Altimeter Capital, with participation from existing investors Sequoia Capital and NFDG. It declined to share its valuation.Security researchers and hackers have long automated parts of their work and AI has shown up as a key tool in the past two years, Prins said. Nearly all human hackers now augment their efforts with AI and there are a handful of firms trying to do what Xbow does — Prins calls them hackbot companies.Xbow, founded in January 2024 by GitHub veteran Oege de Moor, automates penetration testing, where hackers try to find security flaws and break into corporate networks. Companies often hire or employ people to do that, called red teams, as a way of improving and protecting their network and software. But red teaming and penetration testing is costly — $18,000 on average and few weeks of work for a test on a single system, says de Moor — and so it often doesn't get done frequently enough. De Moor wants to sell his product to enable customers to go through the process continuously or at least more often, and before new products and systems go live.'By automating this we can completely change the equation,' said de Moor, who formerly oversaw Microsoft Corp.-owned GitHub's Copilot for AI code-generation.The challenge is that well-financed hackers are also using AI algorithms to automate attacks and increase their frequency at a lower cost. Xbow has 'something that works now and it's exciting, but also somewhat terrifying because we are now in the era of machines hacking machines,' said Nat Friedman of NFDG, and a former GitHub chief executive officer.De Moor, who also spent two decades as a computer science professor at Oxford University, expects the balance of power to eventually favor defenders, using tools like Xbow. 'There might be a period of chaos where not everybody gets ready for these AI-powered attacks,' he said. Now, 'we can, for the first time, have a good hope that defenders can find and fix all the vulnerabilities before a system goes out.'De Moor founded Semmle, a startup for finding security flaws in code that was acquired by GitHub in 2019. Microsoft had bought GitHub the previous year and named Friedman CEO. He wanted to make a series of acquisitions to add new products and entrepreneurial talent.Friedman and Altimeter Capital partner Apoorv Agrawal said they were looking at ways AI could boost cybersecurity when de Moor began Xbow. 'Cybersecurity is going through a credibility crisis. There are a lot of alerts,' Agrawal said. What chief information security officers 'want is less, not more, they want simplicity and less alerts,' he added. 'How do you make this work? AI can help.'HackerOne offers a security platform where companies who want their software vetted can offer bounties for finding bugs. There are open programs and ones that are invitation-only. Xbow is active in both. When an AI like Xbow's finds a vulnerability, HackerOne requires a human at the company to vet it to filter out AI hallucinations. Then Xbow goes to the company whose product contains the supposed flaw. If it confirms the issue, Xbow earns reputation points — hackers get more points the more severe the issue.As part of that work, the Xbow product successfully found and reported security bugs to more than a dozen well-known companies, according to de Moor. The list includes Amazon.com Inc., Walt Disney Co., PayPal Holdings Inc. and Sony Group Corp. De Moor declined to name Xbow's current customers except to say they are large financial services and technology companies.Xbow's team includes GitHub veterans like Nico Waisman, who served as chief information security officer at Lyft Inc., and is now Xbow head of security, and Albert Ziegler, Xbow's head of AI, who worked at GitHub and Semmle.While Xbow's algorithm does well in finding things like common coding errors and security issues, it does poorly at realizing when a flaw results from product design logic. For example, it needs to be explicitly told when looking at a medical web site that prescriptions should be kept private, de Moor said. And it won't understand that while a doctor or a pharmacist needs to be able to access the prescriptions of multiple patients, it's a security problem if one patient can see another's meds.In the future, Xbow also wants to add the ability to tell customers how to correct the security flaws and make coding suggestions for those fixes.Widespread adoption will also require getting customers to change how they work, Altimeter's Agrawal said.'Whenever there's a sufficiently advanced technology, the last-mile adoption requires a change of workflows,' Agrawal said. 'It requires a change of people's behaviors that they've been doing for years, sometimes decades."

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How Microsoft's rift with OpenAI is making this a mandatory part of Microsoft's work culture
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  • Time of India

How Microsoft's rift with OpenAI is making this a mandatory part of Microsoft's work culture

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Want to turn off AI in Windows? Here's how to disable Copilot features
Want to turn off AI in Windows? Here's how to disable Copilot features

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  • Indian Express

Want to turn off AI in Windows? Here's how to disable Copilot features

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AI Tools & Skills Every Data Engineer Should Know in 2025
AI Tools & Skills Every Data Engineer Should Know in 2025

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  • Hans India

AI Tools & Skills Every Data Engineer Should Know in 2025

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AI isn't just about smart algorithms—it's about building systems that can learn, predict, and improve over time. That's where data engineers play a central role: preparing clean, structured, and scalable data systems that fuel AI. To support AI and machine learning, engineers must understand: Supervised and unsupervised learning models Feature engineering and data labeling Data pipelines that serve AI in real-time ETL/ELT frameworks tailored for model training Courses like an AI and Machine Learning Course or a machine learning engineer course can help engineers bridge their current skills with AI expertise. As a result, many professionals are now pursuing AI and ML certification to validate their cross-functional capabilities. One key trend? Engineers are building pipelines not just for reporting, but to feed AI models dynamically, especially in applications like recommendation engines, anomaly detection, and real-time personalization. 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Paxata Paxata is an AI-powered data prep platform that streamlines data transformation and cleaning. It's particularly useful when dealing with big, dirty datasets. How it's useful: Automates tedious hours of data preparation with intelligent automation. Major Features: Recommends transformations, facilitates collaboration, and integrates real-time workflows. Ideal For: Data engineers who want to prepare data for analytics or ML. 9. Dataiku Dataiku is a full-stack AI and data science platform. You can visually create data pipelines and has AI optimization suggestions. Why it's useful: Simplifies managing the complexity of ML workflows and facilitates collaboration. Key Features: Visual pipeline builder, AI-based data cleaning, big data integration. Best For: Big teams dealing with complex, scalable data operations. 10. Fivetran Fivetran is an enterprise-managed data integration platform. With enhanced AI capabilities in 2024, it automatically scales sync procedures and manages schema changes with minimal human intervention. Why it's useful: Automates time-consuming ETL/ELT processes and makes data pipelines operate efficiently. Key Features: Intelligent scheduling, AI-driven error handling, and support for schema evolution. Best For: Engineers running multi-source data pipelines for warehousing or BI. These tools aren't fashionable – they're revolutionizing the way data engineering is done. Whether you're reading code, creating scalable ML pipelines, or handling large data workflows, there's a tool here that can Best suited for data engineers and ML scientists working on large-scale machine learning pipelines, especially those involving complex deep learning models. Feature / Tool DeepCode AI GitHub Copilot Tabnine Apache MXNet TensorFlow Primary Use Code Review Code Assistance Code Completion Deep Learning Machine Learning Language Support Multiple Multiple Multiple Multiple Multiple Ideal for Code Quality Coding Efficiency Coding Speed Large-Scale Models Advanced ML Models Real-Time Assistance Yes Yes Yes No No Integration Various IDEs Various IDEs Various IDEs Flexible Flexible Learning Curve Moderate Moderate Easy Steep Steep Hands-On AI Skills Every Data Engineer Should Develop Being AI-aware is no longer enough. Companies are seeking data engineers who can also prototype and support ML pipelines. Below are essential hands-on skills to master: 1. Programming Proficiency in Python and SQL Python remains the primary language for AI and ML. Libraries like Pandas, NumPy, and Scikit-learn are foundational. 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Version Control for Data and Models AI projects require versioning not only of code but also of data and models. Tools like DVC (Data Version Control) are increasingly being adopted by engineers working with ML teams. If you're serious about excelling in this space, enrolling in a specialized data engineer training or data engineer online course that covers AI integration is a strategic move. Integrating Generative AI & LLMs into Modern Data Engineering The advent of Generative AI and Large Language Models (LLMs) like GPT and BERT has redefined what's possible in AI-powered data pipelines. For data engineers, this means learning how to integrate LLMs for tasks such as: Data summarization and text classification and Anomaly detection in unstructured logs or customer data in unstructured logs or customer data Metadata enrichment using AI-powered tagging using AI-powered tagging Chatbot and voice assistant data pipelines To support these complex models, engineers need to create low-latency, high-throughput pipelines and use vector databases (like Pinecone or Weaviate) for embedding storage and retrieval. Additionally, understanding transformer architectures and prompt engineering—even at a basic level—empowers data engineers to collaborate more effectively with AI and machine learning teams. If you're a Microsoft Fabric Data Engineer, it's worth noting that tools like Microsoft Synapse and Azure OpenAI are offering native support for LLM-driven insights, making it easier than ever to build generative AI use cases within unified data platforms. Want to sharpen your cloud integration skills too? Consider upskilling with niche courses like cloud engineer courses or AWS data engineer courses to broaden your toolset. Creating an AI-Centric Data Engineering Portfolio In a competitive job market, it's not just about what you know—it's about what you've built. As a data engineer aiming to specialize in AI, your portfolio must reflect real-world experience and proficiency. What to Include: End-to-end ML pipeline : From data ingestion to model serving : From data ingestion to model serving AI model integration : Real-time dashboards powered by predictive analytics : Real-time dashboards powered by predictive analytics LLM-based project : Chatbot, intelligent document parsing, or content recommendation : Chatbot, intelligent document parsing, or content recommendation Data quality and observability: Showcase how you monitor and improve AI pipelines Your GitHub should be as well-maintained as your résumé. If you've taken a data engineering certification online or completed an AI ML Course, be sure to back it up with publicly available, working code. Remember: Recruiters are increasingly valuing hybrid profiles. Those who combine data engineering for machine learning with AI deployment skills are poised for the most in-demand roles of the future. Pro tip: Complement your technical portfolio with a capstone project from a top-rated Data Analysis Course to demonstrate your ability to derive insights from model outputs. Conclusion AI is not a separate domain anymore—it's embedded in the very core of modern data engineering. As a data engineer, your role is expanding into new territory that blends system design, ML integration, and real-time decision-making. To thrive in this future, embrace continuous learning through AI and Machine Learning Courses, seek certifications like AI ML certification, and explore hands-on data engineering courses tailored for AI integration. Whether you're starting out or upskilling, taking a solid data engineer online course with an AI focus is your ticket to relevance. Platforms like Prepzee make it easier by offering curated, industry-relevant programs designed to help you stay ahead of the curve. The fusion of AI tools and data engineering isn't just a trend—it's the new standard. So gear up, build smart, and lead the future of intelligent data systems with confidence and clarity.

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