159 articles
Palantir is a US defence-intelligence company, born from the CIA’s venture arm that now operates inside the UK public sector. We examine the claim that its technology does what no other supplier can
Artificial intelligence is rapidly evolving, requiring robust security strategies that traditional methods can’t keep up with. Chief Information Security Officers (CISOs) must protect AI model weights, defend against threats like prompt injection, and comply with regulations without impeding development. Google Cloud offers a comprehensive blueprint for securing AI workloads on Google Kubernetes Engine (GKE), emphasizing effective security layers from infrastructure to application security. This approach includes hardware-level encryption, zero-trust networking, model integrity controls, and defenses against AI-specific threats. It advocates a phased security strategy: deploying foundational configurations, hardening production systems, and automating compliance. By leveraging GKE, teams can ensure enhanced security without compromising AI deployment speed.
Running large language model (LLM) workloads in-house is one of several patterns teams adopt alongside managed API services. Managed API services are convenient and well suited to many workloads. Self-hosting is a complementary option that some…
A three-person agency received a $14,000 AWS bill in one day after attackers extracted static access keys and burned Claude invocations on Bedrock. Combined with May’s DN42 incident, where an autonomous agent provisioned $6,531 of oversized infrastructure in 24 hours, practitioners warn that cloud billing lags roughly a day behind agent-speed spend. By Steef-Jan Wiggers
Owen Flowers and Thalha Jubair, the hackers behind the 2024 TfL cyber attack, have been sentenced to five-year prison terms at Woolwich Crown Court
While support ended in October 2025, community-driven insights from Lansweeper has found that smaller businesses are still using Windows 10
Microsoft promised “infinite” cloud scalability, but signs of capacity strain and regional rollbacks suggest it may be overstretched, forcing customers to rethink vendor lock-in
To fix grid crisis caused by mismanagement under her previous administrations, Danish prime minister opens third term in office with laws against datacentres
The CNCF Technical Oversight Committee (TOC) has voted to accept HAMi as a CNCF incubating project. About HAMi Modern AI infrastructure teams run into the same problem over and over: expensive GPUs often sit fragmented and…
The deployment of the Gemini Enterprise app greatly enhances organizational productivity by offering advanced AI tools and specialized solutions. However, as its usage scales, managing telemetry data presents challenges in audit, governance, and insight extraction. Google Cloud addresses this with pre-computed dashboards and the integration of BigQuery, enabling enhanced analysis of usage patterns, compliance verification, and safety audits. Utilizing BigQuery’s capabilities, administrators can visualize data, streamline queries, and generate insights effectively, ensuring successful integration and governance of the Gemini Enterprise app within their organizations.
AlloyDB enhances enterprise search by integrating advanced hybrid capabilities that merge text, vector, and keyword searches into one SQL query. The recent addition of RUM index support further improves full-text search, especially for logographical languages like Chinese, Japanese, and Korean, where traditional indexing struggles due to the absence of whitespace. Through AlloyDB AI Functions, users can utilize Gemini’s multilingual models for efficient text parsing and segmentation without the complexity of external processing pipelines. This approach facilitates high-accuracy searches, optimized for handling continuous text. The native integration in AlloyDB streamlines operations by keeping data processing within the database, improving performance, and simplifying data management.
IDC’s 2026 AI in Networking Special Report Survey reveals that while enterprises are rapidly piloting AI initiatives, transitioning from pilot to production is hindered primarily by infrastructure issues, particularly in networking. Key concerns include security challenges (32.6%), automation difficulties (26.8%), and talent restrictions (24.7%). The rise of agentic AI intensifies these issues due to its complex, distributed nature, emphasizing the need for robust, consistent networking to support operational control and governance. Organizations must balance platform-based solutions with best-of-breed capabilities to ensure security, reduce complexity, and enable fast deployment. An adaptable, extensible infrastructure is vital for managing the demands of agentic AI systems effectively.