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All news with #rag security tag

29 articles · page 2 of 2

Amazon Nova Multimodal Embeddings — Unified Cross-Modal

🚀 Amazon announces general availability of Amazon Nova Multimodal Embeddings, a unified embedding model designed for agentic RAG and semantic search across text, documents, images, video, and audio. The model handles inputs up to 8K tokens and video/audio segments up to 30 seconds, with segmentation for larger files and selectable embedding dimensions. Both synchronous and asynchronous APIs are supported to balance latency and throughput, and Nova is available in Amazon Bedrock in US East (N. Virginia).
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Amazon ElastiCache Adds Vector Search with Valkey 8.2

🚀 Amazon ElastiCache now offers vector search generally available with Valkey 8.2, enabling indexing, searching, and updating billions of high-dimensional embeddings from providers such as Amazon Bedrock, Amazon SageMaker, Anthropic, and OpenAI with microsecond latency and up to 99% recall. Key use cases include semantic caching for LLMs, multi-turn conversational agents, and RAG-enabled agentic systems to reduce latency and cost. Vector search runs on node-based clusters in all AWS Regions at no additional cost, and existing Valkey or Redis OSS clusters can be upgraded to Valkey 8.2 with no downtime.
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INDOT Used Google AI to Save 360 Hours and Meet Deadline

🚀 Indiana Department of Transportation built a week-long pilot on Google Cloud to meet a 30-day executive order, using a Retrieval-Augmented Generation workflow that combined rapid ETL, Vertex AI Search indexing, and Gemini. The system scraped and parsed decades of internal policies and manuals, produced draft reports across nine divisions with 98% fidelity, and saved an estimated 360 hours of manual effort, enabling INDOT to submit on time.
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Deutsche Bank launches DB Lumina for AI research platform

🤖 DB Lumina is Deutsche Bank Research’s AI-powered assistant, built on Google Cloud and integrating multimodal Gemini models, RAG retrieval, and vector search. It provides a conversational chat interface, reusable prompt templates, and document-grounded answers with inline citations and enterprise guardrails for compliance. Early deployment to roughly 5,000 analysts has yielded measurable time savings, deeper analysis, and improved editorial accuracy.
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Source-of-Truth Authorization for RAG Knowledge Bases

🔒 This post presents an architecture to enforce strong, source-of-truth authorization for Retrieval-Augmented Generation (RAG) knowledge bases using Amazon S3 Access Grants with Amazon Bedrock. It explains why vector DB metadata filtering is insufficient—permission changes can be delayed and complex identity memberships are hard to represent—and recommends validating permissions at the data source before returning chunks to an LLM. The blog includes a practical Python walkthrough for exchanging identity tokens, retrieving caller grant scopes, filtering returned chunks, and logging withheld items to reduce the risk of sensitive data leaking into LLM prompts.
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Cloudy-driven Email Detection Summaries and Guardrails

🛡️Cloudflare extended its AI agent Cloudy to generate clear, concise explanations for email security detections so SOC teams can understand why messages are blocked. Early LLM implementations produced dangerous hallucinations when asked to interpret complex, multi-model signals, so Cloudflare implemented a Retrieval-Augmented Generation approach and enriched contextual prompts to ground outputs. Testing shows these guardrails yield more reliable summaries, and a controlled beta will validate performance before wider rollout.
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Google Conversational Analytics API Brings Chat to Your Data

💬 The Conversational Analytics API lets developers embed natural‑language data queries and chat‑driven analysis directly into custom applications, internal tools, and workflows. It combines Google's AI, Looker’s semantic layer, and BigQuery context engineering to deliver data, chart, and text answers with trusted access controls. Features include agentic orchestration, a Python Code Interpreter, RAG‑assisted context engineering, and both stateful and stateless conversation modes. Enterprise controls such as RBAC, row‑ and column‑level access, and query limits are built in.
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Amazon RDS Supports MariaDB 11.8 with Vector Engine

🚀 Amazon RDS for MariaDB now supports MariaDB 11.8 (minor 11.8.3), the community's latest long-term maintenance release. The update introduces MariaDB Vector, enabling storage of vector embeddings and use of retrieval-augmented generation (RAG) directly in the managed database. It also adds controls to limit maximum temporary file and table sizes to better manage storage. You can upgrade manually, via snapshot restore, or with Amazon RDS Managed Blue/Green deployments; 11.8 is available in all regions where RDS MariaDB is offered.
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Amazon Neptune Adds BYOKG RAG Support via GraphRAG

🔍 Amazon Web Services announced general availability of Bring Your Own Knowledge Graph (BYOKG) support for Retrieval-Augmented Generation (RAG) using the open-source GraphRAG Toolkit. Developers can now connect domain-specific graphs stored in Amazon Neptune (Database or Analytics) directly to LLM workflows, combining graph queries with vector search. This reduces hallucinations and improves multi-hop and temporal reasoning, easing operationalization of graph-aware generative AI.
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