All news with #rag security tag

🧭 This post launches Google Cloud's "Data Strategy = AI Strategy" series and reframes the database as the central context engine for production AI. It argues that by using fully PostgreSQL-compatible services such as AlloyDB and Cloud SQL, teams can eliminate latency and improve retrieval accuracy while reducing infrastructure friction. The article emphasizes three enterprise pillars — speed, scale, and security — and describes hands-on labs that cover batch embeddings, real-time inference with Gemini 3 Flash, and row-level security for zero-trust agents.

🔒 This Google Cloud blog outlines a reference architecture to deliver private-IP only connectivity for retrieval-augmented generation (RAG) applications that must not transit the public internet. It describes a multi-project topology—routing project, Shared VPC host, and service projects for Data Ingestion, Serving, and Frontend—and maps required services such as Cloud Interconnect/Cloud VPN, Network Connectivity Center, Private Service Connect, Cloud Router, Cloud Armor, and VPC Service Controls. The post also details RAG population and inference flows to show end-to-end private traffic paths and highlights management and routing orchestration for hybrid and VPC spokes.

🔗 Gemini CLI's Neo4j extension connects graph databases to Gemini's reasoning via the Model Context Protocol (MCP). The extension bundles four MCP servers to manage Neo4j Aura, translate natural language into Cypher, support interactive data modeling and visualization, and use Neo4j as long-term memory for agentic flows. Developers can provision databases, run Cypher queries, and persist knowledge from the terminal to accelerate GraphRAG workflows.

🔔 Amazon Neptune Analytics is now available in seven additional AWS Regions: Middle East (Bahrain), Middle East (UAE), Israel (Tel Aviv), Africa (Cape Town), Canada (Calgary), Asia Pacific (Malaysia), and Europe (Zurich). Neptune is a serverless graph database that automatically scales graph workloads, reduces operational overhead, and improves AI accuracy and explainability by modeling connected data. It also provides fully managed GraphRAG with Amazon Bedrock Knowledge Bases and integrates with the Strands AI Agents SDK and popular agentic memory tools to accelerate graph-powered AI development.

🛡️As AI copilots and assistants are embedded into daily work, recent incidents show the primary risk lies in surrounding workflows rather than in the models themselves. Malicious Chrome extensions that exfiltrated ChatGPT and DeepSeek chats and prompt injections that tricked an AI coding assistant into executing malware exploited integration contexts, not model internals. The piece advises mapping AI usage, applying least-privilege, enforcing middleware guardrails to scan outputs, and using dynamic SaaS platforms like Reco to detect and control risky workflows.

🔐 This article explains how sensitive data commonly leaks from AI systems — from RAG retrievals and agentic tool chains to user-initiated oversharing — and why LLMs cannot enforce document-level permissions. It recommends a layered, defense-in-depth approach: automatic identification and classification, data minimization at ingress, sanitization, redaction, and strict access controls that follow data through the pipeline. The authors also stress threat modeling and vendor due diligence to limit regulatory, competitive, and reputational harm.

🔒 Google has patched a zero-click vulnerability in Gemini Enterprise and Vertex AI Search that could have allowed attackers to exfiltrate corporate data via hidden instructions embedded in shared Workspace content. Discovered by Noma Security in June 2025 and dubbed "GeminiJack," the flaw exploited Retrieval-Augmented Generation (RAG) retrieval to execute indirect prompt injection without any user interaction. Google updated how the systems interact, separated Vertex AI Search from Gemini Enterprise, and changed retrieval and indexing workflows to mitigate the issue.

🚀 Amazon S3 Vectors is now generally available, delivering native, purpose-built vector storage and query capabilities in cloud object storage. It supports up to two billion vectors per index, 10,000 indexes per vector bucket, and offers up to 90% lower costs to upload, store, and query vectors. S3 Vectors integrates with Amazon Bedrock, SageMaker Unified Studio, and OpenSearch Service, supports SSE-S3 and optional SSE-KMS encryption with per-index keys, and provides tagging for ABAC and cost allocation.

📘 Amazon Connect now supports connecting existing Amazon Bedrock Knowledge Bases directly to AI agents and allows multiple knowledge bases per agent. You can attach Bedrock KBs in a few clicks with no additional setup or data duplication, and leverage Bedrock connectors such as Adobe Experience Manager, Confluence, SharePoint, and OneDrive. With multiple KBs per agent, AI agents can query several sources in parallel for more comprehensive responses. This capability is available in all AWS Regions where both services are offered.

🔍 AWS has announced general availability of multimodal retrieval in Amazon Bedrock Knowledge Bases, enabling unified search across text, images, audio, and video. The managed Retrieval Augmented Generation (RAG) workflow provides developers full control over ingestion, parsing, chunking, embedding (including Amazon Nova multimodal), and vector storage. Users can submit text or image queries and receive relevant text, image, audio, and video segments back, which can be combined with the LLM of their choice to generate richer, lower-latency responses. Region availability varies by feature set and is documented by AWS.

🔍 In early 2024 Google introduced native vector search in BigQuery, embedding semantic search directly into the data warehouse to remove the need for separate vector databases. Users can create indexes with a simple CREATE VECTOR INDEX statement and run semantic queries via the VECTOR_SEARCH function or through Python integrations like LangChain. BigQuery provides serverless scaling, asynchronous index refreshes, model rebuilds with no downtime, partitioned indexes, and ScaNN-based TreeAH for improved price/performance, while retaining row- and column-level security and a pay-as-you-go pricing model.

🌐 Web Grounding is now generally available as a built-in tool for Nova models, usable today with Nova Premier via the Amazon Bedrock tool use API. It retrieves and incorporates publicly available information with citations to support responses, enabling a turnkey RAG solution that reduces hallucinations and improves accuracy. Cross-region inference makes the tool available in US East (N. Virginia), US East (Ohio), and US West (Oregon). Support for additional Nova models will follow.

🚀 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).

🚀 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.

🚀 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.

🤖 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.

🔒 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.

🛡️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.

💬 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.

🚀 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.