Explore articles tagged with Machine Learning
Data drift happens when the statistical properties of a machine learning (ML) model's input data change over time, eventually rendering its predictions less accurate. Cybersecurity professionals who rely on ML for tasks like malware detection and network threat analysis find that undetected data drift can create vulnerabilities.
For the last 18 months, the CISO playbook for generative AI has been relatively simple: Control the browser. Security teams tightened cloud access security broker (CASB) policies, blocked or monitored traffic to well-known AI endpoints, and routed usage through sanctioned gateways.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Constellations —Constellations is a short story by Jeff VanderMeer, the author of the critically acclaimed, bestselling Southern Reach series.
The open-source AI movement has never lacked for options. Mistral, Falcon, and a growing field of open-weight models have been available to developers for years.
Anthropic’s most capable AI model has already found thousands of AI cybersecurity vulnerabilities across every major operating system and web browser. The company’s response was not to release it, but to quietly hand it to the organisations responsible for keeping the internet running.
Presented by Box As frontier models converge, the advantage in enterprise AI is moving away from the model and toward the data it can safely access. For most enterprises, that advantage lives in unstructured data: the contracts, case files, product specifications, and internal knowledge.
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AI systems are starting to move beyond simple responses. In many organisations, AI agents are now being tested to plan tasks, make decisions, and carry out actions with limited human input.
The security industry has spent the last year talking about models, copilots, and agents, but a quieter shift is happening one layer below all of that: Vendors are lining up around a shared way to describe security data. The Open Cybersecurity Schema Framework (OCSF), is emerging as one of the strongest candidates for that job.
Are you a subscriber to Anthropic's Claude Pro ($20 monthly) or Max ($100-$200 monthly) plans and use its Claude AI models and products to power third-party AI agents like OpenClaw? If so, you're in for an unpleasant surprise. Anthropic announced a few hours ago that starting tomorrow, Saturday, April 4, 2026, at 12 pm PT/3 pm ET, it will no longer be possible for those Claude subscribers to use their subscriptions to hook Anthropic's Claude models up to third-party agentic tools, citing the st.
With the launch of KiloClaw, enterprises now have a tool to enforce governance over autonomous agents and manage shadow AI. While businesses spent the last year securing large language models and formalising vendor agreements, developers and knowledge workers started moving on their own.
Every enterprise running AI coding agents has just lost a layer of defense. On March 31, Anthropic accidentally shipped a 59.
In the early days of large language models (LLMs), we grew accustomed to massive 10x jumps in reasoning and coding capability with every new model iteration. Today, those jumps have flattened into incremental gains.
Processing 200,000 tokens through a large language model is expensive and slow: the longer the context, the faster the costs spiral. Researchers at Tsinghua University and Z.
AI is hitting a wall with tasks that require an understanding of the physical world. This limitation is thrusting world models into the spotlight, attracting significant investments.
NTT DATA has announced an initiative to deliver NVIDIA-powered platforms designed to give organisations a repeatable, production-ready model for scaling AI. The offering integrates NVIDIA’s GPU-accelerated computing and high-performance networking with NVIDIA AI Enterprise software, including NeMo and NIM Microservices, into a full-stack agentic AI platform that can be deployed in cloud and edge environments.
Recent reports about AI project failure rates have raised uncomfortable questions for organizations investing heavily in AI. Much of the discussion has focused on technical factors like model accuracy and data quality, but after watching dozens of AI initiatives launch, I’ve noticed that the biggest opportunities for improvement are often cultural, not technical.