All news with #ai security tag

🔎 This article examines methods to infer user satisfaction from untagged chatbot conversations by combining linguistic and behavioral signals. It argues that conventional metrics such as accuracy and completion rates often miss subtle indicators of user sentiment, and recommends unsupervised and weakly supervised NLP techniques to surface those signals. The post highlights practical considerations including privacy-preserving aggregation, deployment complexity, and the potential business benefit of reducing churn and improving customer experience through targeted dialog improvements.

🚀 California is partnering with Google Cloud to modernize state and local services by applying AI, security, and infrastructure solutions. Agencies such as Covered California use Document AI, Assured Workloads, and AI-driven security to speed eligibility decisions and protect sensitive data. Universities including UCR and Caltech are using Vertex AI and AI-optimized HPC for research acceleration. Workspace, Gemini, and Agentspace are cited as productivity and information-management enablers.

🔒Prisma AIRS offers unified, AI-native security across the full AI lifecycle, from model development and training to deployment and runtime monitoring. The platform focuses on five core capabilities—model scanning, posture management, AI red teaming, runtime security and agent protection—to detect and mitigate threats such as prompt injection, data poisoning and tool misuse. By consolidating workflows and sharing intelligence across Prisma, it aims to simplify operations, accelerate remediation and reduce total cost of ownership so organizations can deploy bravely.

🔒 This upcoming 60‑minute webinar examines how quantum computing and AI are jointly reshaping cybersecurity and accelerating new attack vectors. Top experts will cut through the hype to explain quantum-safe cryptography, practical defenses against AI-driven phishing and "harvest now, decrypt later" risks, and industry-specific controls for finance, healthcare, and critical infrastructure. Attendees will leave with a concrete roadmap for assessment, deployment, and ongoing resilience. Seats are limited.

🛡️ Generative AI is now central to enterprise work, but rapid adoption has exposed gaps in legacy security models that were not designed for last‑mile behaviors. The piece argues buyers must reframe evaluations around real-world AI use — inside browsers and across sanctioned and shadow tools — and prioritize solutions offering real-time monitoring, contextual enforcement, and low‑friction deployment. It warns against blunt blocking and promotes nuanced controls such as redaction, just‑in‑time warnings, and conditional approvals to protect data while preserving productivity.

🛡️ New IO research reports that 26% of surveyed UK and US organisations have experienced data poisoning, and 37% observe employees using generative AI tools without permission. The third annual State of Information Security Report highlights rising concern around AI-generated phishing, misinformation, deepfakes and shadow AI. Despite the risks, most respondents say they feel prepared and are adopting acceptable use policies to curb unsanctioned tool use.

🧭 Google Cloud has expanded the Data Science Agent in Colab Enterprise notebooks to support BigQuery ML, BigQuery DataFrames and Spark, enabling large-scale data transformation, model training, and inference directly on BigQuery or via Serverless for Apache Spark. The agent can now auto-retrieve BigQuery table metadata and lets you add tables via an @ mention from your current project to provide prompt context. To invoke frameworks, include keywords such as BigQuery ML, BigFrames, or PySpark; sample prompts are provided to guide forecasting, supervised learning, and dimensionality reduction workflows. Notable limitations: generated PySpark targets Spark 4.0 and @ mentions only search the current project; BigQuery improvements are available now in BigQuery notebooks and coming soon to Vertex AI.

🧭 Villager, an AI-native penetration testing framework developed by Chinese group Cyberspike, has reached nearly 11,000 downloads on PyPI just two months after release. The tool integrates Kali Linux utilities with DeepSeek AI models and operates as a Model Context Protocol (MCP) client to automate red team workflows. Researchers at Straiker reported that Villager can spin up on-demand Kali containers, automate browser testing, use a database of more than 4,200 prompts for decision-making, and deploy self-destructing containers — features that lower the barrier to sophisticated attacks and raise concerns about dual-use abuse.

🚀 Check Point is acquiring Lakera to build a comprehensive AI security stack for enterprises adopting generative and AI-driven applications. The move aims to protect the emerging AI attack surface by combining Check Point's security platform with Lakera's AI threat analysis and model-protection capabilities. Customers should expect integrated defenses for models, data, and pipelines, increased visibility into model behavior, and tools for managing model risk and compliance.

🤖 Security leaders report early wins from AI in detection, triage, and automation, but emphasize limits and oversight. Prioritizing high-value telemetry for real-time detection while moving lower-priority logs to data lakes improves signal-to-noise and shortens response times, according to Myke Lyons. Financial firms are experimenting with agentic AI to block business email compromise in real time, yet researchers and practitioners warn of missed detections and 'ghost alerts.' Organizations that treat AI as a copilot with governance, explainability, and institutional context see more reliable, safer outcomes.

🔒 CrowdStrike announced its intent to acquire Pangea to deliver the industry’s first AI detection and response (AIDR) capability, securing enterprise AI use and development across data, models, agents, identities, infrastructure, and interactions. Unveiled at Fal.Con 2025 by Michael Sentonas, the deal will integrate Pangea’s prompt‑layer and interaction security with the Falcon platform to provide unified visibility, governance, and enforcement across the AI lifecycle. The combined solution targets prompt injection, model manipulation, shadow AI and sensitive data exfiltration while enabling developers and security teams to innovate faster with built‑in safeguards.

🤖 At the Google Cloud CISO Community event in Singapore, APAC security leaders highlighted accelerating investment in cybersecurity AI to scale operations and enable business outcomes. They emphasized priorities: getting AI implementation and governance right, securing the AI supply chain, and translating cyber risk into board-level impact. Practical wins noted include reduced investigation time, agentic SOC automation, and strengthened threat intelligence sharing.

🔒 Cybersecurity leaders report the mission to defend organizations is unchanged, but threats, technology and operating pressures are evolving rapidly. Five trends — shrinking or stagnating budgets, AI-enabled attacks, the rise of agentic AI, accelerating business speed, and heightened vendor M&A — are forcing changes in strategy. CISOs are simplifying tech stacks, increasing automation and outsourcing, and deploying AI for detection and response while wrestling with new authentication/authorization gaps. Vendor viability and consolidation now factor into resilience planning.

🔒 Laura Deaner, newly appointed CISO at the Depository Trust & Clearing Corporation (DTCC), explains how AI and machine learning are transforming threat detection and incident response. She cautions that quantum computing could break current encryption by 2030, urging immediate focus on post-quantum cryptography and comprehensive crypto inventories. Deaner also stresses that modern CISOs must combine curiosity with disciplined asset hygiene to lead security transformations effectively.

🔒 Security leaders are balancing safe AI adoption with operational gains and focusing on five practical use cases where AI can improve security outcomes. Organizations are connecting LLMs to internal telemetry via standards like MCP, using agents and models such as Claude, Gemini and GPT-4o to automate threat hunting, translate technical metrics for executives, assess vendor and internal risk, and streamline Tier‑1 SOC work. Early deployments report time savings, clearer executive reporting and reduced analyst fatigue, but require robust guardrails, validation and feedback loops to ensure accuracy and trust.

🔒 An AI-integrated browser embeds large multimodal models into standard web browsers, allowing agents to view pages and perform actions—opening links, filling forms, downloading files—directly on a user’s device. This enables faster, context-aware automation and access to subscription or blocked content, but raises substantial privacy and security risks, including data exfiltration, prompt-injection and malware delivery. Users should demand features like per-site AI controls, choice of local models, explicit confirmation for sensitive actions, and OS-level file restrictions, though no browser currently implements all these protections.

🚀 GKE Inference Gateway and GKE Inference Quickstart are now generally available, bringing production-ready inferencing features built on AI Hypercomputer. New capabilities include prefix-aware load balancing, disaggregated serving, vLLM support on TPUs and Ironwood TPUs, and model streaming with Anywhere Cache to cut model load times. These features target faster time-to-first-token and time-per-output-token, higher throughput, and lower inference costs, while Quickstart offers data-driven accelerator and configuration recommendations.

⚡ This post announces a reproducible recipe to deploy NVIDIA Dynamo for disaggregated LLM inference on Google Cloud’s AI Hypercomputer using Google Kubernetes Engine, vLLM, and A3 Ultra (H200) GPUs. The recipe separates prefill and decode phases across dedicated GPU pools to reduce contention and lower latency. It includes single-node and multi-node examples and step-by-step deployment actions. The repository provides configuration guidance and future plans for broader GPU and engine support.

🔁 AWS announced native anomaly detection enhancements for AWS IoT SiteWise, including automated model retraining, flexible promotion modes, and exposed model metrics. Retraining can be scheduled between 30 days and one year to keep models current with changing equipment conditions. Customers can choose automatic service-managed promotion or manual customer-managed promotion using exposed metrics such as precision, recall, and AUC. Multivariate detection is available in N. Virginia, Ireland, and Sydney.

🔒 AWS Labs published a Model Context Protocol (MCP) server for CloudTrail that enables AI assistants to perform security and compliance analysis via natural‑language queries. The server provides direct access to CloudTrail events and CloudTrail Lake, allowing searches of 90‑day management event histories and Trino SQL queries on Lake data spanning up to 10 years. By exposing these capabilities through a conversational interface, the MCP server removes the need for bespoke API integrations and streamlines investigation and compliance workflows. The component is available in regions that support CloudTrail LookupEvents or CloudTrail Lake and is available with code and documentation in the AWS Labs repository.