All news with #how to tag

🤖 The post proposes the orchestrator pattern to turn monolithic AI scripts into a team of specialized, distributed microservices that integrate directly with existing frontends. It demonstrates using Google's Agent Development Kit (ADK), the Agent-to-Agent (A2A) protocol, and Cloud Run to host separate researcher, judge, and orchestrator services. The design enables independent scaling, strict JSON contracts for reliable decision-making, and language-agnostic implementations. The authors emphasize production hardening: secure agent endpoints, mitigate latency across hops, and implement robust retries and error handling.

🔍 Observability is essential for production AI and agentic systems, enabling teams to detect risks, validate policies, and maintain operational control. The post stresses capturing full context—prompts, retrieval provenance, tool invocations, and multi-turn traces—because traditional health metrics can miss trust-boundary compromises. It recommends building AI-native telemetry into the SDL, aligning with standards like OpenTelemetry and platforms such as Azure Monitor, and making reconstructability a release requirement.

🤖 This article introduces Dev Signal, a prototype multi-agent system built with Google ADK, the Model Context Protocol (MCP), and Cloud Run to automate discovery, grounding, and content creation. It outlines prerequisites, project structure, and an MCP-based toolset that integrates a Reddit discovery proxy, the managed Developer Knowledge MCP for documentation grounding, and a local Nano Banana Pro image generator. The piece explains secure secret handling, subprocess-based local tooling, and the ADK modular design to accelerate development.

🔁 This article prescribes an operational model for predictable incident response across mixed on‑prem, cloud and SaaS environments. It argues for a shared incident language — a compact contract of rules and artifacts (severity by customer impact, one hypothesis, one timeline, named owners) — enforced via a single incident channel with an incident commander and domain leads. The author recommends portable telemetry in three layers: user journeys as the court of record, cross‑environment correlation IDs and strict clock discipline, plus a single change table. Practical escalation engineering (one‑page provider cards, time to human targets and a rollback/failover decision matrix) closes vendor and operations gaps.

🔒 The article argues the cyber perimeter was never dead but was abandoned, leaving unsupported firewalls, routers, and remote access appliances as easy footholds for attackers. It outlines the FBI’s Operation Winter SHIELD, a concentrated two-month effort targeting weak authentication, excessive privileges, and unpatched edge devices, and CISA’s BOD 26‑02, which mandates removal of end-of-life perimeter hardware within 18 months. The piece warns that neglecting edge devices undermines identity-first strategies and urges CISOs to regain total edge visibility and enforce disciplined asset lifecycles, strong hardware-based authentication, rapid patching, and strict privilege controls.

⚠️ Building LLM applications on Vertex AI can trigger 429 errors when request rates exceed available throughput, degrading user experience and increasing retries. This article explains consumption options—Standard and Priority PayGo, Provisioned Throughput, Flex PayGo, and Batch—and prescribes five operational practices: smart retries, global model routing, context caching, prompt optimization, and traffic shaping. Combining these approaches (for example PT for critical real-time traffic and Batch for latency-tolerant jobs) helps preserve performance and control costs.

🔒 Security leaders face a growing threat from harvest now, decrypt later attacks as quantum computing progresses. A webinar will outline practical steps—identify long-term sensitive data, map encryption use, and adopt hybrid cryptography such as ML-KEM alongside existing algorithms—to begin transitioning without operational disruption. The session also covers post-quantum traffic inspection and Zero Trust integration to maintain policy enforcement at scale.

🔒 This practical guide describes how to disable built‑in AI assistants that vendors are increasingly embedding across consumer products from Microsoft, Google, Apple, and Meta. It summarizes the privacy, security, and performance risks these agents introduce and gives concise, actionable steps to turn off AI features in Gmail and Google Docs, Chrome, Firefox, Edge, Windows (Copilot and Recall), WhatsApp, Android, macOS and iOS. Where uninstalling isn't possible, the article describes flag, settings, and registry workarounds and recommends periodic checks to ensure features haven't been reactivated.

🔒 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.

⚙️Data centers must evolve quickly to support AI workloads and deliver measurable business outcomes. This Spotlight report explains the technical and organizational shifts required to bring infrastructure into the AI age, spanning servers, storage, high-performance computing, networking, software, and security. IT leaders will find actionable guidance on roadmaps, partner selection, and prioritization to accelerate modernization and reduce deployment risk.

💡Budget Bytes is a new video series that shows developers how to build production-quality AI applications on Azure for under $25. Each episode walks through end-to-end scenarios using the Azure SQL Database Free Offer, with live cost tallies, authentic debugging, and complete GitHub repos you can deploy yourself. Expect practical patterns and demonstrations of tools like Microsoft Foundry, Copilot Studio, and the Model Context Protocol, plus links to Microsoft Learn for deeper dives.

💬 This Google Cloud post by David Tamaki Szajngarten demonstrates how to build a context-aware conversational agent for BigQuery using the Conversational Analytics API powered by Gemini. It provides a reference Python SDK flow to register BigQuery tables, create a DataAgent with system instructions and permitted datasources, and deploy stateful or stateless conversations. The article shows a streaming chat loop that returns generated SQL, DataFrame-like results, Vega‑Lite chart specs, and final natural-language answers, and highlights integration with the Agent Development Kit (ADK) and lifecycle controls.

📘 Microsoft published a new e-book, Establishing proactive defense—A maturity-based guide for adopting a dynamic, risk-based approach to exposure management, that helps security teams move from fragmented, reactive practices to a unified, risk-driven exposure management model. The guide describes five maturity levels, common pain points, and practical next steps to prioritize and verify mitigations. It is intended for security leaders seeking to turn telemetry into measurable risk reduction.

🔍 This webinar examines how modern cloud forensics replaces slow, manual log stitching with automated, context-aware investigation across transient infrastructure. You’ll learn why traditional incident response fails when compromised instances, rotating identities, and expiring logs erase evidence, and why three capabilities — host-level visibility, context mapping, and automated evidence capture — are essential. The session demonstrates real investigations where correlated signals rebuild full attack timelines in minutes, enabling faster scoping, clearer attribution, and more confident remediation.

🧭 I spent a day using Corelight's Investigator NDR to learn how network detection and response supports SOC workflows. The interface prioritized high-risk detections, showed packet-level evidence and MITRE ATT&CK context, and let me dig into suspicious DNS, reverse shells, and exploit tool activity. Built-in GenAI provided step-by-step investigative actions, and integrations with SIEM, EDR and firewalls demonstrated how NDR enriches and correlates network telemetry for faster triage.

📷 This article examines whether parents should allow children to post selfies online, arguing that prohibition rarely works and parental guidance is a more effective approach. It details specific harms — from predator grooming and AI-enabled sextortion (via nudifier tools) to identity theft, cyberbullying and long-term reputational damage — and highlights correlations between heavy social-media use and worsening adolescent mental health. Practical recommendations include open communication, using privacy settings and geolocation controls, selective follower approval, routine digital clean-ups and household screen-time rules, while urging parents to model responsible sharing and reduce their own “sharenting.”

🛡️ This article presents a layered, AI-enhanced defense-in-depth architecture for protecting serverless microservices on AWS. It outlines seven security layers—from edge DDoS and WAF protections to identity, API gateway controls, network isolation, compute hardening, secrets management, and data encryption—integrating GuardDuty, Cognito, API Gateway, Secrets Manager, and DynamoDB. The guidance emphasizes continuous monitoring, automated incident response using Amazon Bedrock and EventBridge, and operational practices that balance security, compliance, and developer velocity.

🔧 This guide explains how to adapt foundation models on Google Cloud by fine-tuning both managed and self-managed workflows. It contrasts a fully managed Vertex AI Supervised Fine-Tuning path for models like Gemini with a customizable GKE approach using LoRA on open-source models such as Llama. The labs walk through data preparation, baseline evaluation, tuning, and automated evaluation metrics, as well as GKE infrastructure, GPU provisioning, security with Workload Identity, and containerized training for production readiness.

🧰 The Google Cloud samples team describes building a specialized end-to-end system that uses Gemini on Vertex AI and Genkit to produce production-ready educational code samples across many languages and products. Their architecture separates generation, validation, and delivery so LLM outputs are combined with deterministic automations, linters, unit tests, and human review. The post presents seven practical technical takeaways—decomposition, determinism, precise prompts, vetted evaluation, scaled downstream processes, end-to-end testing, and solid engineering practices—that drove reliable, scalable sample generation.

🛡️ Breach & Attack Simulation (BAS) tools automate validation of security controls by emulating adversary tactics and mapping those behaviors to frameworks such as MITRE ATT&CK or the Cyber Kill Chain. BAS focuses on verifying detection and prevention capabilities across endpoints, networks, mail gateways and identity systems rather than discovering unknown vulnerabilities. When evaluating products, prioritize realistic, customizable scenarios, scalable automated testing, clear reporting, integration with existing tooling, and vendor support or managed options.