All news with #retrieval-augmented generation tag

Cloudflare Launches Content Signals Policy for robots.txt

🛡️ Cloudflare introduced the Content Signals Policy, an extension to robots.txt that lets site operators express how crawlers may use content after it has been accessed. The policy defines three machine-readable signals — search, ai-input, and ai-train — each set to yes/no or left unset. Cloudflare will add a default signal set (search=yes, ai-train=no) to managed robots.txt for ~3.8M domains, serve commented guidance for free zones, and publish the spec under CC0. Cloudflare emphasizes signals are preferences, not technical enforcement, and recommends pairing them with WAF and Bot Management.

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.

Cloudflare 2025 Founders’ Letter: AI, Content, and Web

📣 Cloudflare’s 2025 Founders’ Letter reflects on 15 years of Internet change, highlighting encryption’s rise thanks in part to Universal SSL, slow IPv6 adoption, and the rising costs of scarce IPv4 space. It warns that AI answer engines are shifting value away from traffic-based business models and threatening publishers. Cloudflare previews tools and partnerships — including AI Crawl Control — to help creators control access and negotiate compensation.

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.

Seattle Children’s Uses AI to Accelerate Pediatric Care

🤖 Seattle Children’s partnered with Google Cloud to build Pathway Assistant, a multimodal AI chatbot that turns thousands of pediatric clinical pathway PDFs into conversational, searchable guidance. Using Vertex AI and Gemini, the assistant extracts JSON metadata, parses diagrams and flowcharts, and returns cited answers in seconds. The tool logs clinician feedback to BigQuery and stores source documents in Cloud Storage, enabling continuous improvement of documentation and metadata.

Mr. Cooper and Google Cloud Build Multi-Agent AI Team

🤖 Mr. Cooper partnered with Google Cloud to develop CIERA, a modular agentic AI framework that assembles specialized agents to support mortgage servicing representatives and customers. The design assigns distinct roles — orchestration, task execution, data retrieval, memory, and evaluation — while keeping humans in the loop for verification and personalization. Built on Vertex AI, CIERA aims to reduce research time, lower average handling time, and preserve trust and compliance in regulated workflows.

New LLM Attack Vectors and Practical Security Steps

🔐This article reviews emerging attack vectors against large language model assistants demonstrated in 2025, highlighting research from Black Hat and other teams. Researchers showed how prompt injections or so‑called promptware — hidden instructions embedded in calendar invites, emails, images, or audio — can coerce assistants like Gemini, Copilot, and Claude into leaking data or performing unauthorized actions. Practical mitigations include early threat modeling, role‑based access for agents, mandatory human confirmation for high‑risk operations, vendor audits, and role‑specific employee training.

Check Point Acquires Lakera to Build AI Security Stack

🔐 Check Point has agreed to acquire Lakera, an AI-native security platform focused on protecting agentic AI and LLM-based deployments, in a deal expected to close in Q4 2025 for an undisclosed sum. Lakera’s Gandalf adversarial engine reportedly leverages over 80 million attack patterns and delivers detection rates above 98% with sub-50ms latency and low false positives. Check Point will embed Lakera into the Infinity architecture, initially integrating into CloudGuard WAF and GenAI Protect, offering near-immediate, API-based protection as an add-on for existing customers.

OWASP LLM AI Cybersecurity and Governance Checklist

🔒 OWASP has published an LLM AI Cybersecurity & Governance Checklist to help executives and security teams identify core risks from generative AI and large language models. The guidance categorises threats and recommends a six-step strategy covering adversarial risk, threat modeling, inventory and training. It also highlights TEVV, model and risk cards, RAG, supplier audits and AI red‑teaming to validate controls. Organisations should pair these measures with legal and regulatory reviews and clear governance.

Gemini and Open-Source Text Embeddings Now in BigQuery ML

🚀 Google expanded BigQuery ML to generate embeddings from Gemini and over 13,000 open-source text-embedding models via Hugging Face, all callable with simple SQL. The post summarizes model tiers to help teams trade off quality, cost, and scalability, and introduces Gemini's Tokens Per Minute (TPM) quota for throughput control. It shows a practical workflow to deploy OSS models to Vertex AI endpoints, run ML.GENERATE_EMBEDDING for batch jobs, and undeploy to minimize idle costs, plus a Colab tutorial and cost/scale guidance.

Avnet Reclaims Security Data, Cuts Costs, Boosts AI

🔐 Avnet moved away from vendor-bound SIEM, EDR and RBVM silos toward a centralized security data pipeline built on Cribl, prompted by a legacy SIEM renewal that became a strategy inflection point. The redesign gave Avnet full ownership of telemetry, enabled large-scale ETL and flexible routing, and freed analysts from vendor dashboards. Operationally, licensing and storage costs dropped dramatically to 15% of prior levels while processing capacity doubled and pipeline staffing fell from four engineers to one. With its own data layer in place, Avnet is accelerating analytics and AI use cases such as tailored LLMs and retrieval-augmented generation (RAG) to improve investigations and reduce analyst workload.

Amazon Neptune Integrates with Zep for Long-Term Memory

🧠 Amazon Web Services announced integration of Amazon Neptune with Zep, an open-source memory server for LLM applications, enabling persistent long-term memory and contextual history. Developers can use Neptune Database or Neptune Analytics as the graph store and Amazon OpenSearch as the text-search layer within Zep’s memory system. The integration enables graph-powered retrieval, multi-hop reasoning, and hybrid search across graph, vector, and keyword modalities, simplifying the creation of personalized, context-aware LLM agents.

Secure AI at Machine Speed: Full-Stack Enterprise Defense

🔒 CrowdStrike explains how widespread AI adoption expands the enterprise attack surface, exposing models, data pipelines, APIs, and autonomous agents to new adversary techniques. The post argues that legacy controls and fragmented tooling are insufficient and advocates for real-time, full‑stack protections. The Falcon platform is presented as a unified solution offering telemetry, lifecycle protection, GenAI-aware data loss prevention, and agent governance to detect, prevent, and remediate AI-related threats.

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.

Google Cloud: Monthly AI product and security update

🔔 This month Google Cloud expanded its AI stack across models, tooling, and security. Highlights include Gemini 2.5 Flash with native image generation and SynthID watermarking on Vertex AI, new Veo video models, the Gemini CLI, and a global Anthropic Claude endpoint. Google also published 101 gen‑AI blueprints, developer guidance for choosing tools, and security advances for agents and AI workloads.

AI Crawler Traffic: Purpose and Industry Breakdown

🔍 Cloudflare Radar introduces industry-focused AI crawler insights and a new crawl purpose selector that classifies bots as Training, Search, User action, or Undeclared. The update surfaces top bot trends, crawl-to-refer ratios, and per-industry views so publishers can see who crawls their content and why. Data shows Training drives nearly 80% of crawl requests, while User action and Undeclared exhibit smaller, cyclical patterns.

Make Websites Conversational with NLWeb and AutoRAG

🤖 Cloudflare offers a one-click path to conversational search by combining Microsoft’s NLWeb open standard with Cloudflare’s managed retrieval engine, AutoRAG. The integration crawls and indexes site content into R2 and a managed vector store, serves embeddings and inference via Workers AI, and exposes both a user-facing /ask endpoint and an agent-focused /mcp endpoint. Publishers get continuous re-indexing, controlled agent access, and observability through an AI Gateway, removing much of the infrastructure burden for conversational experiences.

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.

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.

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.