All news with #how to tag

🔐 This live webinar explains why unmanaged non-human identities—service accounts, API tokens, AI agent connections, and OAuth grants—are now a primary vector for cloud breaches. You will learn a repeatable discovery process to surface every automated credential, a framework to right-size permissions, and how to implement an automated lifecycle policy so dead credentials are revoked. Attendees receive an Identity Cleanup Checklist to apply immediately.

⚖️ This guide from Google Cloud outlines infrastructure options to manage generative AI costs without compromising performance or availability. It compares Pay-as-You-Go, Priority PayGo, Provisioned Throughput, Batch API, and Flex PayGo, explaining tiers, headers for request control, and SLAs. Practical recommendations show combining PT for baseload, Priority PayGo for spikes, and opportunistic PayGo or Batch/Flex for non‑critical work. Monitoring and cost‑sizing guidance is included.

🧪 This article describes how to validate the Dev Signal multi-agent system locally before deploying to Cloud Run. It covers configuring local secrets, an environment-aware env utility that initializes Vertex AI, and a test runner which connects to the cloud-based Vertex AI memory bank to persist user preferences. The guide demonstrates a two-phase scenario that teaches preferences, generates multimodal content, wipes local session history, and verifies cross-session memory recall.

🔧 Google Cloud outlines its strategy for building resilient GPU AI/ML infrastructure to support massive-scale training workloads. The post emphasizes measuring reliability beyond simple uptime with MTBI and Goodput, and describes four core principles — proactive prevention, continuous monitoring, transparency and control, and minimizing disruptions — to reduce interruptions and accelerate recovery. It frames infrastructure reliability as a commercial imperative when training at scale.

📚 Students at UC Berkeley describe AI as a learning partner—using it to explain concepts, summarize papers, and debug code rather than as a shortcut to finished assignments. In mixed-methods interviews they framed AI as a "tutor" that extends office hours, supports students with learning disabilities, and scaffolds exploration while preserving ownership of learning. They also set explicit guardrails—limiting model access, alternating assisted and unassisted work, and asking for hints instead of full answers. This selective approach aligns with DORA findings that targeted AI use frees developers to focus on higher-level problem solving.

🚚 FM Logistic used AlphaEvolve on Google Cloud to tackle large-scale warehouse routing by applying evolutionary code generation powered by Gemini models. Starting from an existing stepwise routing baseline, the agent generated, scored, and iterated thousands of candidate algorithms against a representative dataset to minimize average travel distance per pick while avoiding operational failures. The adapted routing logic delivered a 10.4% efficiency improvement and reduced annual warehouse travel by more than 15,000 km.

🛡️ This webinar, Exposure-Driven Resilience, demonstrates how teams can move from assumptions to evidence by automating tests that emulate real attacker behavior. The session explains how to pressure-test both technical controls and operational processes, use threat intelligence to prioritize what to test, and fold results into everyday SOC and incident response workflows without added complexity. Presenters Jermain Njemanze and Sébastien Miguel provide a practical walkthrough and a live demonstration to show how to prove defenses actually work.

🔒 This concise guide explains fast, practical steps to recover a compromised online account and limit attacker control. It recommends a prioritized, timed response—contain the incident, secure access, and check for persistent compromises—emphasizing actions like change passwords, remove unauthorized forwarding, enable two-factor authentication, and revoke sessions from a known-clean device. The piece also covers device cleanup, notifying contacts and banks, and long-term protections such as password managers, authenticator apps, hardware keys and regular software updates.

🤖 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.