Latest news with #Cypher
Yahoo
7 days ago
- Business
- Yahoo
How one cybersecurity startup is using AI to keep clients safe
Artificial intelligence has become a key part of cybersecurity. It's helping cybersecurity companies keep their customers' data safe, but, unfortunately, it's also a new tool for criminals to exploit. Arctic Wolf ( CEO and president Nick Schneider joins Asking for a Trend with Josh Lipton to discuss how his company, valued at more than $4 billion, is leveraging AI to fight those who want to use the technology for harm. To watch more expert insights and analysis on the latest market action, check out more Asking for a Trend here. Let's talk about AI, a trend investors are very excited about. How is your company leveraging that tech? Yeah, so there's two primary ways. First of all, AI's been used in cybersecurity for a long time. There's two primary ways you can use it kind of in the back end or in the core of the platform to be able to make sense of all the data that you're ingesting. So, for example, our business is ingesting now somewhere in the neighborhood of 9 trillion security events a week. But our average customer is only really actioning, gonna call it two to three of those alerts per week. And really the magic between those two numbers is all through automation and AI, in AI. Now, that's not really customer facing. That's a lot of what's happening in the back end. The second way to leverage it is through LLMs or engagement with the end user of themselves. So, like, for example, we have a product called Cypher. It's an AI assistant. And effectively what that allows end customers to do is understand the data that they're getting through the platform to be able to take action within their environment or do something within their business. And we talked about how you're using AI. How are the bad guys using it? Yeah, the easiest one to like really wrap your head around is through like fishing campaigns, right? So it used to be a world where a fishing campaign was actually pretty easy to, you know, point out if you were an individual and you were paying attention. Now they've gotten pretty sophisticated, right? So the emails are written really well. They use logos that are really well depicted, the headers that they leverage for the emails that they send are really hard to decipher, like a one versus an I, for example. So so they've gotten really sophisticated with leveraging AI and then, you know, also leveraging the AI in a bulk fashion to do that to, you know, thousands of people at once as opposed to trying to kind of do it in a piecemeal fashion. When you talk about bad guys, Nick, who are we talking about in broad strokes? Yeah. Yeah, I got this question today, too. So um, so bad actors in general, generally speaking they're in groups of bad actors and I think that's where we see the majority of the activity. So those can be actually organizations in certain countries, or individuals now with the advent of AI can do this from their home. Uh but then you also see in certain industries concerned over like nation states, right? So if you're in critical infrastructure, anything that might be, you know, critical to a country or an organization that would be interesting to a nation state, you'll see some nation-state activity as well.
Geeky Gadgets
03-06-2025
- Business
- Geeky Gadgets
Unlock Hidden Data Insights with GraphRAG: The Future of AI Retrieval
What if your AI could not only retrieve information but also uncover the hidden relationships that make your data truly meaningful? Traditional vector-based retrieval methods, while effective for semantic searches, often miss the intricate connections buried within complex datasets. Enter GraphRAG, a new approach that combines the power of knowledge graphs and Cypher queries to transform how we retrieve and interpret information. By transforming unstructured text into structured data, GraphRAG offers a way to explore deeper insights and relationships that traditional methods simply can't match. Imagine not just finding the right answer but understanding the web of connections that brought you there. In this exploration of GraphRAG, the IBM Technology team explain how it uses the structured nature of graph databases to provide context-rich insights and unparalleled depth in data retrieval. From understanding the mechanics of entity and relationship extraction to seeing how natural language queries are transformed into precise Cypher commands, this overview will guide you through the core principles that make GraphRAG so powerful. Along the way, we'll compare it to VectorRAG, explore its advantages, and even touch on hybrid systems that combine the best of both worlds. By the end, you'll not only grasp how GraphRAG works but also why it's reshaping the future of AI-powered knowledge retrieval. Could this be the key to unlocking the full potential of your data? GraphRAG Overview What is GraphRAG? GraphRAG is a retrieval method that uses knowledge graphs to store and manage structured data, serving as an alternative to VectorRAG (Vector Retrieval Augmented Generation). While vector databases rely on embeddings to identify semantic similarities, knowledge graphs represent data as nodes (entities) and edges (relationships). This structure provides a more interconnected and holistic view of the dataset, allowing for the retrieval of information with greater depth and context. By focusing on structured data, GraphRAG enables you to explore relationships and patterns that are often missed by traditional vector-based methods. This makes it particularly useful for tasks requiring detailed exploration and analysis of complex datasets. How Does GraphRAG Work? GraphRAG operates by transforming unstructured text into a structured format and storing it in a graph database. The process involves several key steps: Entity and Relationship Extraction: A large language model (LLM) identifies entities and their relationships within unstructured text. A large language model (LLM) identifies entities and their relationships within unstructured text. Data Structuring: The extracted information is organized into nodes (entities) and edges (relationships) to form a knowledge graph. The extracted information is organized into nodes (entities) and edges (relationships) to form a knowledge graph. Querying: Natural language queries are converted into Cypher, a graph database query language, to retrieve relevant data. Natural language queries are converted into Cypher, a graph database query language, to retrieve relevant data. Result Interpretation: The retrieved data is translated back into natural language for easy understanding. This structured approach allows you to explore complex interconnections within datasets, offering insights that traditional vector-based methods often overlook. By using the power of knowledge graphs, GraphRAG provides a more nuanced and comprehensive understanding of the data. GraphRAG Explained: AI Retrieval with Knowledge Graphs & Cypher Watch this video on YouTube. Advance your skills in AI knowledge by reading more of our detailed content. System Setup Implementing GraphRAG requires a combination of tools and technologies to create and manage the knowledge graph effectively. Here's how you can set it up: Knowledge Graph Creation: Use an LLM to extract entities and relationships from unstructured text and populate a graph database like Neo4j. Use an LLM to extract entities and relationships from unstructured text and populate a graph database like Neo4j. Containerized Environments: Tools such as Docker or Podman ensure scalability and simplify deployment. Tools such as Docker or Podman ensure scalability and simplify deployment. Programming Libraries: Python libraries like LangChain and IBM are essential for configuring and managing the system. This setup ensures a scalable, efficient, and user-friendly environment for implementing GraphRAG. By combining these tools, you can streamline the process of transforming unstructured data into actionable insights. Transforming Data: From Unstructured to Structured A cornerstone of GraphRAG is its ability to transform unstructured text into structured data. This transformation process involves several steps: Entity Identification: The LLM identifies key entities (nodes) within the text. The LLM identifies key entities (nodes) within the text. Relationship Mapping: Relationships (edges) between entities are extracted to form meaningful connections. Relationships (edges) between entities are extracted to form meaningful connections. Controlled Structuring: By limiting the types of nodes and relationships, you can improve the graph's accuracy and relevance. This structured representation enhances data retrieval and allows for the exploration of intricate patterns and relationships within the dataset. By converting unstructured text into a graph format, GraphRAG enables you to uncover hidden connections and gain a deeper understanding of the data. Querying the Knowledge Graph Natural language processing (NLP) plays a pivotal role in querying knowledge graphs. When you input a query in plain language, the system converts it into Cypher, a specialized query language for graph databases. The process involves: Query Conversion: The LLM translates your natural language query into a Cypher query. The LLM translates your natural language query into a Cypher query. Data Retrieval: The Cypher query retrieves relevant information from the graph database. The Cypher query retrieves relevant information from the graph database. Result Translation: The retrieved data is converted back into natural language for easy interpretation. Prompt engineering ensures that the generated Cypher queries are accurate and the responses are well-structured. This process improves the overall user experience by making complex data retrieval tasks more intuitive and accessible. Advantages of GraphRAG GraphRAG offers several distinct advantages over traditional retrieval methods: Holistic Retrieval: Unlike vector-based methods, GraphRAG retrieves information across the entire dataset, not just the top results. Unlike vector-based methods, GraphRAG retrieves information across the entire dataset, not just the top results. Contextual Insights: The structured nature of knowledge graphs provides deeper contextual understanding and reveals hidden connections. The structured nature of knowledge graphs provides deeper contextual understanding and reveals hidden connections. Enhanced Exploration: Relationships and patterns that are difficult to capture with vector-based methods become accessible through GraphRAG. These benefits make GraphRAG a powerful tool for tasks requiring comprehensive data retrieval and analysis. Its ability to provide context-rich insights sets it apart from traditional methods. GraphRAG vs. VectorRAG The key difference between GraphRAG and VectorRAG lies in their approach to data retrieval: VectorRAG: Relies on embeddings and semantic similarity to retrieve the most relevant results. Relies on embeddings and semantic similarity to retrieve the most relevant results. GraphRAG: Uses structured data and Cypher queries to explore the entire dataset, uncovering deeper connections. While VectorRAG excels at quick semantic searches, GraphRAG is better suited for tasks requiring detailed exploration and summarization of complex datasets. Each method has its strengths, and the choice between them depends on the specific requirements of your use case. HybridRAG Systems: Combining Strengths HybridRAG systems integrate the strengths of both GraphRAG and VectorRAG to create a more versatile retrieval framework. By combining vector-based semantic search with the structured insights of knowledge graphs, HybridRAG systems offer: Enhanced Retrieval: Use the best of both methods for diverse datasets and complex queries. Use the best of both methods for diverse datasets and complex queries. Improved Flexibility: Adapt to a wide range of use cases, from quick searches to in-depth analysis. This hybrid approach ensures a robust and comprehensive retrieval system. By balancing the speed of vector-based methods with the depth of graph-based insights, HybridRAG systems provide a powerful solution for modern data retrieval challenges. Media Credit: IBM Technology Filed Under: AI, Top News Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Time of India
01-06-2025
- Entertainment
- Time of India
'Gen V' season 2 teaser trailer: Emotional first look addresses Chance Perdomo's death; Hamish Linklater joins cast as Dean Cypher
'Gen V' season 2 teaser trailer: Emotional first look addresses Chance Perdomo's death; Hamish Linklater joins cast as Dean Cypher Get ready, 'Gen V' fans! The hit spin-off of 'The Boys' is back, and the new season looks darker, crazier, and more explosive than ever. The first teaser trailer for Season 2 has just dropped, and it gives us a glimpse into the chaos brewing at Godolkin University. Season 2 teaser revealed at CCXP México At a special panel hosted by a major streaming platform at CCXP México, fans got their first proper look at what's coming next. The event featured stars of the show including Jaz Sinclair , Lizze Broadway , London Thor, and Derek Luh, who all helped reveal the teaser and shared the release details. Mark your calendars – 'Gen V' is back this September It's official – Season 2 of 'Gen V' will premiere on 17 September 2025. Fans will get a triple treat, as the first three episodes will drop on the same day. After that, a new episode will be released every week, leading up to the grand finale in October. What's Season 2 all about? This time, things are even more intense. Season 2 picks up after the shocking events of last season, and America is still reeling under Homelander's terrifying rule. The students are back at Godolkin University – but nothing is normal anymore. by Taboola by Taboola Sponsored Links Sponsored Links Promoted Links Promoted Links You May Like Esse novo alarme com câmera é quase gratuito em Valparaíso De Goiás (consulte o preço) Alarmes Saiba Mais Undo Expect fresh drama, new faces, and lots of secrets. Actor Hamish Linklater joins the cast as Dean Cypher, a mysterious new character who's expected to shake things up. War is coming – and not just in the classroom According to a report by Deadline, the official logline hints at big changes, 'Cate and Sam are celebrated heroes, while Marie, Jordan, and Emma reluctantly return to college, burdened by months of trauma and loss. But parties and classes are hard to care about with war brewing between humans and Supes, both on and off campus. The gang learns of a secret program that goes back to the founding of Godolkin University that may have larger implications than they realise. And, somehow, Marie is a part of it.' That's right – we're diving deeper into the secrets of Godolkin. Expect messy alliances, betrayals, and perhaps even more ties to 'The Boys' universe. Check out our list of the latest Hindi , English , Tamil , Telugu , Malayalam , and Kannada movies . Don't miss our picks for the best Hindi movies , best Tamil movies, and best Telugu films .
USA Today
22-05-2025
- Sport
- USA Today
Spartans offer elite-level 2027 North Carolina LB prospect Quinton Cypher
Spartans offer elite-level 2027 North Carolina LB prospect Quinton Cypher Michigan State football has extended an offer to an elite-level linebacker in the 2027 class. Quinton Cypher of Raleigh, N.C. announced earlier this week that he's received an offer from the Spartans. Cypher posted on his social media X account about the offer from Michigan State. Cypher is a highly-rated four-star linebacker in the 2027 class. According to 247Sports, he holds a recruiting rating of 93.09. Cypher ranks as the No. 10 linebacker in 247Sports' composite rankings for the 2027 class. He is also listed as the No. 4 player from North Carolina and No. 170 overall prospect in the class. Michigan State is one of nearly 25 schools to extend an offer to Cypher, according to 247Sports. He also holds an offer from Alabama, Arkansas, Duke, Florida State, Georgia, Louisville, Maryland, Michigan, NC State, North Carolina, Notre Dame, Ohio State, Penn State, South Carolina, Tennessee, Texas A&M, Virginia Tech, Wisconsin, Boston College, Charlotte, East Carolina, Liberty and Troy. Contact/Follow us @The SpartansWire on X (formerly Twitter) and like our page on Facebook to follow ongoing coverage of Michigan State news, notes and opinion. You can also follow Robert Bondy on X @RobertBondy5.
Yahoo
11-04-2025
- Business
- Yahoo
The Rise of Graph Database Market: A $2,143.0 million Industry Dominated by IBM Corporation (US), Oracle (US), Graphwise (Australia)
Delray Beach, FL, April 11, 2025 (GLOBE NEWSWIRE) -- The Graph Database Market is estimated to grow USD 2,143.0 million by 2030 from USD 507.6 million in 2024, at a Compound Annual Growth Rate (CAGR) of 27.1 % during the forecast period , according to a new report by MarketsandMarkets™. Graph databases ensure enterprise knowledge management by rebuilding complex data with interconnected nodes and relationships and providing a more straightforward way to navigate and retrieve information. It helps businesses build a comprehensive knowledge graph uniting disparate data sources and enables complex semantic search, context-aware recommendations, and data discovery. Graph databases support better decision-making, foster innovation, and improve team cooperation by mapping relationships between organizational knowledge. They are handy for large organizations, which depend on accessing and utilizing vast amounts of structured and unstructured data to be productive and competitive. Browse in-depth TOC on "Graph Database Market" 386 - Tables 54 - Figures 368 - Pages Download Report Brochure @ Graph Database Market Dynamics: DRIVERS Rising demand for AI/generative AI solutions Rapid growth in data volume and complexity Growing demand for semantic search RESTRAINTS Data quality and integration challenges Navigation of saturated data management tool landscape Scalability issues OPPORTUNITIES Leveraging LLMs to reduce knowledge graph construction costs Data unification and rapid proliferation of knowledge graphs Increasing adoption in healthcare and life sciences to revolutionize data management and enhance patient outcomes List of Key Companies in Graph Database Market: IBM Corporation (US) Oracle (US) Neo4j (US) DataStax (US) Graphwise (Australia) AWS (US) RelationalAI (US) Progress Software (US) TigerGraph (US) … and more Request Sample Pages@ Based on model type, the property graph segment to hold the largest market size during the forecast period. A property graph model is a structure of a graph database that represents data as nodes, edges, and properties. Nodes represent entities, edges represent relationships between entities, and properties are key-value pairs that provide additional metadata for both nodes and edges. This model allows for a very flexible and detailed representation of data that can be used for complex queries and analytics. Property Graphs allow for traversal and pattern-matching operations, typically using a query language specific to that model, like Cypher. It is used extensively in applications where detailed insights into relationships are needed, such as fraud detection, recommendation engines, and social network analysis, because it can efficiently manage connected and dynamic datasets. The services segment will have the highest growth during the forecast period. Graph database services are divided into managed services and professional services, targeting different stages of implementation and operation. Managed services include end-to-end management of graph database solutions, including hosting, monitoring, performance optimization, and scalability on cloud platforms. Professional services include consulting services, which help organizations design a tailored graph database strategy; deployment and integration services, which implement the database within existing systems to ensure seamless compatibility; and support and maintenance services, which provide ongoing assistance, updates, and troubleshooting to ensure optimal performance. These services help businesses to effectively utilize graph databases, thereby reducing complexity and accelerating adoptions. Inquire Before Buying@ Asia Pacific is expected to hold the highest market growth rate during the forecast period. The graph database market of the Asia-Pacific region is rapidly evolving amidst digital transformation and higher demand for sophisticated data management solutions. In China businesses are embracing graph database technology to drive innovation and operational efficiency in various industries such as in e-commerce, telecommunications, and energy to handle complex, interconnected datasets. In Australia, Australian National Graph is working with Neo4j's technology to construct a national-scale graph database, aiming to improve research collaboration and sustainability initiatives through collaborations between agencies and universities. The continuous expansion of cloud platforms in the region also enables enterprises across sectors to deploy graph databases with ease to support scalability and real-time data analytics. Market Players The major vendors covered in the Graph Database market are IBM Corporation (US), Oracle (US), Microsoft Corporation (US), AWS (US), Neo4j (US), RelationalAI (US), Progress Software (US), TigerGraph (US), Stardog (US), Datastax (US), Franz Inc (US), Openlink Software (US), Dgraph Labs (US), Graphwise (US), Altair (US), Bitnine (South Korea) ArangoDB (US), Fluree (US), Blazegraph (US), Memgraph UK), Objectivity (US), GraphBase (Australia), Graph Story (US), Oxford Semantic Technologies (UK), and FalkorDB (Israel). These players have adopted various growth strategies, such as partnerships, agreements and collaborations, new product launches, enhancements, and acquisitions to expand their footprint in the Graph Database market. Get access to the latest updates on Graph Database Companies and Graph Database Industry CONTACT: About MarketsandMarkets™ MarketsandMarkets™ has been recognized as one of America's Best Management Consulting Firms by Forbes, as per their recent report. MarketsandMarkets™ is a blue ocean alternative in growth consulting and program management, leveraging a man-machine offering to drive supernormal growth for progressive organizations in the B2B space. With the widest lens on emerging technologies, we are proficient in co-creating supernormal growth for clients across the globe. Today, 80% of Fortune 2000 companies rely on MarketsandMarkets, and 90 of the top 100 companies in each sector trust us to accelerate their revenue growth. With a global clientele of over 13,000 organizations, we help businesses thrive in a disruptive ecosystem. The B2B economy is witnessing the emergence of $25 trillion in new revenue streams that are replacing existing ones within this decade. We work with clients on growth programs, helping them monetize this $25 trillion opportunity through our service lines – TAM Expansion, Go-to-Market (GTM) Strategy to Execution, Market Share Gain, Account Enablement, and Thought Leadership Marketing. Built on the 'GIVE Growth' principle, we collaborate with several Forbes Global 2000 B2B companies to keep them future-ready. Our insights and strategies are powered by industry experts, cutting-edge AI, and our Market Intelligence Cloud, KnowledgeStore™, which integrates research and provides ecosystem-wide visibility into revenue shifts. In addition, MarketsandMarkets SalesIQ enables sales teams to identify high-priority accounts and uncover hidden opportunities, helping them build more pipeline and win more deals with precision. To find out more, visit or follow us on Twitter, LinkedIn and Facebook. 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