All news with #google tag

BigQuery AI Functions: Reimagining SQL for the AI Era

🤖 BigQuery is introducing managed AI functions in public preview — AI.IF, AI.CLASSIFY, and AI.SCORE — that let analysts apply generative AI directly inside SQL queries. These functions enable semantic filtering and joins, label-based classification of text and images, and natural-language ranking, while BigQuery applies prompt, query-plan, and endpoint optimizations to reduce LLM calls and control cost. They complement existing Gemini inference functions and remove much of the need for complex prompt tuning or separate model selection, making AI-driven analytics more accessible within familiar SQL workflows.

Flexible path to modern EUC with Cameyo by Google Launch

🔒 Cameyo by Google is a cloud-native Virtual App Delivery solution that streams legacy Windows and Linux applications into the browser or publishes them as Progressive Web Apps, avoiding the overhead of full VDI. Paired with Chrome Enterprise Premium, Cameyo brings legacy client apps under a single secure browsing context with advanced DLP and threat protection. IT teams benefit from faster deployments, reduced VPN and infrastructure complexity, and a clear migration path to ChromeOS while preserving critical Windows workloads.

BigQuery adds MATCH_RECOGNIZE for row-sequence SQL

🔍 BigQuery now supports MATCH_RECOGNIZE, a SQL clause for identifying ordered patterns across rows and time-series data. It lets analysts express complex sequence logic—using PARTITION BY, ORDER BY, PATTERN, DEFINE and MEASURES—inside a single query without heavy joins or external processing. The feature targets use cases like funnels, fraud detection, log sequencing, and financial pattern detection, and is immediately available to all BigQuery users.

Bringing Connected AI Work Experiences Across Devices

🚀 Google outlines its plan to embed Generative AI across enterprise platforms and endpoints, integrating Gemini into Chrome Enterprise, Android, Pixel phones and Chromebook Plus devices. The post highlights the general availability of Cameyo by Google to virtualize legacy and modern apps in the cloud and the launch of Gemini in Chrome with enterprise-grade controls. It also previews Android XR and Pixel features powered by Gemini Nano, while expanding data loss prevention and a one-click SecOps integration to help IT secure AI-driven workflows.

Emerging Threats Center in Google Security Operations

🛡️ The Emerging Threats Center in Google Security Operations uses the Gemini detection‑engineering agent to turn frontline intelligence from Mandiant, VirusTotal, and Google into actionable detections. It generates high‑fidelity synthetic events, evaluates existing rule coverage, and drafts candidate detection rules for analyst review. The capability surfaces campaign‑based IOC and detection matches across 12 months of telemetry to help teams rapidly determine exposure and validate their defensive posture.

Google Sues China-Based Operators of PhaaS 'Lighthouse'

⚖️ Google has filed a civil lawsuit in the U.S. District Court for the Southern District of New York against China-based operators of the PhaaS kit Lighthouse, which Google says has ensnared over one million users across 120 countries. The platform is accused of powering industrial-scale SMS phishing and smishing campaigns that impersonate trusted brands like E-ZPass and USPS to steal financial data. Google alleges the actors illegally used its trademarks on at least 107 spoofed sign-in templates and seeks to dismantle the infrastructure under the RICO, Lanham Act, and the Computer Fraud and Abuse Act. Security firms link Lighthouse to a broader PhaaS ecosystem including Darcula and Lucid, and to a smishing syndicate tracked as Smishing Triad.

Google Announces Private AI Compute for Cloud Privacy

🔒 Google on Tuesday introduced Private AI Compute, a cloud privacy capability that aims to deliver on-device-level assurances while harnessing the scale of Gemini models. The service uses Trillium TPUs and Titanium Intelligence Enclaves (TIE) and relies on an AMD-based Trusted Execution Environment to encrypt and isolate memory on trusted nodes. Workloads are mutually attested, cryptographically validated, and ephemeral so inputs and inferences are discarded after each session, with Google stating data remains private to the user — 'not even Google.' An external assessment by NCC Group flagged a low-risk timing side channel in the IP-blinding relay and three attestation implementation issues that Google is mitigating.

Lightricks Scales Video Diffusion Training with JAX

🚀 Lightricks rewrote its training stack in JAX to scale high-performance video diffusion models on TPUs after hitting limits with PyTorch/XLA. The migration enabled reliable sharding, fixed FlashAttention and data-loading issues, and delivered linear scaling across small and large TPU pods. These improvements translated to ~40% more training steps per day, faster iteration, and doubled team productivity. Their stack leverages Flax, Optax, Orbax, and the MaxText blueprint for robust, testable, and efficient large-scale training.

KONNI APT Abuses Google Find Hub to Wipe Android Devices

🔐 Genians Security Center (GSC) has attributed a recent destructive campaign to the KONNI APT, which abused Google’s Find Hub service to remotely wipe Android phones and tablets. Threat actors distributed a signed MSI via compromised KakaoTalk accounts, installed an AutoIt loader, and stole Google credentials to trigger remote resets when victims were away. GSC describes this as the first confirmed state-linked misuse of Find Hub and recommends stronger authentication, verification for remote wipes, and enhanced EDR and behavioral monitoring.

Pixnapping vulnerability: Android screen-snooping risk

🔒 A newly disclosed exploit named Pixnapping (CVE-2025-48561) allows a malicious Android app with no special permissions to read screen pixels from other apps and reconstruct sensitive content. The attack chains intent-based off-screen rendering, translucent overlays, and a GPU compression timing side channel to infer pixel values. Google issued a September patch but researchers bypassed it, and a more robust fix is planned.

The AI Fix #76 — AI self-awareness and the death of comedy

🧠 In episode 76 of The AI Fix, hosts Graham Cluley and Mark Stockley navigate a string of alarming and absurd AI stories from November 2025. They discuss US judges who blamed AI for invented case law, a Chinese humanoid that dramatically shed its outer skin onstage, Toyota’s unsettling walking chair, and Google’s plan to put specialised AI chips in orbit. The conversation explores reliability, public trust and whether prompting an LLM to "notice its noticing" changes how conscious it sounds.

North Korean Hackers Abuse Google's Find Hub for Wipes

🔒 Genians Security Center (GSC) reports that North Korea–linked KONNI actors abused Google's Android device‑tracing and management service Find Hub to remotely track and wipe victims' phones. Attackers compromised legitimate Google accounts—often via spear‑phishing impersonating South Korea’s National Tax Service—and used Find Hub to confirm location and issue reset commands that silenced alerts. The campaign also spread malware through compromised KakaoTalk contacts sending apps disguised as 'stress-relief' programs.

Agent Sandbox: Kubernetes Enhancements for AI Agents

🛡️ Agent Sandbox is a new Kubernetes primitive designed to run AI agents with strong, kernel-level isolation. Built on gVisor with optional Kata Containers and developed in the Kubernetes community as a CNCF project, it reduces risks from agent-executed code. On GKE, managed gVisor, container-optimized compute and pre-warmed sandbox pools deliver sub-second startup latency and up to 90% cold-start improvement. A Python SDK and a simple API abstract YAML so AI engineers can manage sandbox lifecycles without deep infrastructure expertise; Agent Sandbox is open source and deployable on GKE today.

Google Cloud Expands AI Infrastructure and Services in India

🤝 Google Cloud is increasing local AI compute in India with its AI Hypercomputer powered by Trillium TPUs, enabling training and serving of advanced Gemini models with data residency and sovereignty controls. New local offerings include batch support for Gemini 2.5 Flash, a preview of Document AI, and real‑time grounding using Google Maps for location‑aware responses. Google is also supporting Indic Arena at IIT Madras with cloud credits to benchmark Indian multilingual models and to help grow the local AI ecosystem.

Konni Exploits Google's Find Hub to Remotely Wipe Devices

⚠️ The North Korea-linked Konni threat actor has been observed combining spear-phishing and signed installers to compromise Windows and Android systems and exfiltrate credentials. Genians Security Center reports attackers used stolen Google account credentials to access Google Find Hub and remotely reset devices, causing unauthorized data deletion. The campaign, detected in early September 2025, uses malicious MSI packages and RATs including EndRAT and Remcos to maintain long-term access and propagate via compromised KakaoTalk sessions.

Full-Stack Approach to Scaling RL for LLMs on GKE at Scale

🚀 Google Cloud describes a full-stack solution for running high-scale Reinforcement Learning (RL) with LLMs, combining custom TPU hardware, NVIDIA GPUs, and optimized software libraries. The approach addresses RL's hybrid demands—reducing sampler latency, easing memory contention across actor/critic/reward models, and accelerating weight copying—by co-designing hardware, storage (Managed Lustre, Cloud Storage), and orchestration on GKE. The blog emphasizes open-source contributions (vLLM, llm-d, MaxText, Tunix) and integrations with Ray and NeMo RL recipes to improve portability and developer productivity. It also highlights mega-scale orchestration and multi-cluster strategies to run production RL jobs at tens of thousands of nodes.

Google Cloud N4D VMs with AMD EPYC Turin Generally Available

🚀 Google Cloud announces general availability of the N4D machine series built on 5th Gen AMD EPYC 'Turin' processors and Google's Titanium infrastructure. N4D targets cost-optimized, general-purpose workloads — web and app servers, data analytics, and containerized microservices — with up to 96 vCPUs, 768 GB DDR5, 50 Gbps networking, and Hyperdisk storage. Google cites up to 3.5x web-serving throughput versus N2D and material price-performance gains for general compute and Java workloads.

Gemini Code Assist adds persistent memory for reviews

🧠 Gemini Code Assist on GitHub now supports persistent memory that learns from merged pull request interactions to capture a team's coding standards, style, and best practices. The memory is stored securely in a Google-managed project specific to each installation and is applied selectively to relevant reviews. It infers reusable rules from review threads and uses them both to shape initial analysis and to filter draft suggestions so the agent adapts over time and reduces repetitive feedback.

Google Public Sector Achieves CMMC Level 2 Certification

🔒 Google Public Sector announced it has achieved CMMC Level 2 certification, validated by a certified third-party assessment organization (C3PAO). The certification confirms that its internal systems used to process and store Controlled Unclassified Information (CUI) meet DoD cybersecurity expectations. While the certification covers Google’s internal systems and does not extend to customer environments, Google highlights support for the Defense Industrial Base through FedRAMP-authorized cloud services and published compliance resources, including a Google Workspace CMMC Implementation Guide, to help partners accelerate their own CMMC journeys.

When to Use Sub-Agents Versus Agents as Tools for ADK

🧭 This post explains when to use sub-agents versus packaging agents as tools when building multi-agent systems with Google's Agent Development Kit (ADK). It contrasts agents-as-tools — encapsulated, stateless specialists invoked like deterministic function calls — with sub-agents, which are stateful, context-aware delegates that manage multi-step workflows. The guidance highlights trade-offs across task complexity, context sharing, reusability, and autonomy, and illustrates the patterns with data-agent and travel-planner examples to help architects choose efficient, scalable designs.