Browse entries 433–456 of 1531. Return to the first page to search and filter the complete collection.
Play video
This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
Play video
This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
Play video
This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
Play video
This AI Explained video reviews a major AI development through the lens of agentic workflows and tool-use risk. It is useful context for AI engineering, evaluation, governance, and operational risk.
Play video
Charlie Holtz describes Conductor’s approach to coordinating coding agents through shared cloud workspaces and organizational context. His examples include CI-enforced human review for migrations, carefully maintained agent instructions, and a database of working information queried by agents. The presentation combines workflow advice with product demonstrations; it does not measure a general productivity advantage.
Play video
Cody Menefee proposes an AI-assisted pasture-rotation system combining grazing knowledge, field observations and GPS-collar controls. Cameras, drones or satellite imagery could supply changing pasture conditions, while an LLM recommends moves for a farmer to accept or reject. The talk identifies unresolved data and hardware-integration needs; improved farm capacity is a project goal rather than a demonstrated outcome.
Learn more about how Google Cloud empowers you with the tools, governance, and infrastructure you need to securely deploy workloads and maintain long-term trust.
Learn how better questions, trusted AI, and human judgment help security leaders make confident decisions and build resilient systems.
Join Microsoft Security at Black Hat USA 2026 for supply chain research, hands-on security experiences, expert conversations, and our reception.
Francis deSouza explains the crucial role that deep context plays in creating an AI advantage for defenders.
Microsoft’s latest Secure Future Initiative report outlines progress on secure foundations, AI-powered defense, and future-ready cybersecurity.
Read five key learnings from the Frost & Sullivan 2025 Frost Radar™ for CSPM to learn how CSPM is evolving from point-in-time compliance to continuous risk management.
Unit 42 analysis of phantom squatting: attackers register domains that LLMs may hallucinate in recommendations, code, documentation, or support answers. That turns model error into a supply-chain path, especially when users or agents follow generated links without independent validation.
Following our AI Threat Defense announcement, here's how we use AI to chart a path to autonomous software development lifecycle security.
A malicious Chromium-based extension that spoofs the AI-powered answer engine Perplexity AI redirects browser search traffic using MV3 APIs and intermediary infrastructure.
Cloudflare report on testing security-focused frontier models against real infrastructure code. Relevant to evaluating AI-assisted vulnerability discovery and production security workflows.
Play video
NDC Security 2026 talk on securing agentic systems through reference architecture, common risk analysis, and control prioritization for autonomous agents in the software development lifecycle.
This post dives deep into how Claude wrote an exploit for one of the vulnerabilities it found in Firefox.
A METR exploratory analysis uses 5,305 coding-agent transcripts from seven staff to estimate time saved on AI-assisted tasks. A model estimates counterfactual human effort, while message activity approximates actual human time. The judge was checked against only 34 human estimates, and transcript selection excludes work done without AI. Task substitution, specialization and uncertain success judgments further limit interpretation. The resulting ratios are proposed soft upper bounds, not causal measurements of overall productivity.
METR’s February 2026 modeling note builds a compact forecast of AI research automation from research labor, compute and software efficiency. Its equations connect capability progress to the share of tasks automated while representing remaining human work as a bottleneck. The accompanying model is useful for examining sensitivity to automation speed and research returns. The headline date follows selected parameter assumptions, many based on judgment, rather than an observed capability threshold. The model omits important effects, including parts of research judgment and the wider economy, and does not establish when real research organizations will become autonomous.
Play video
NDC AI 2025 talk introducing AI security for developers, including model lifecycle, training data, secure integration, data leakage, prompt injection, adversarial inputs, and model bias.
Play video
This AI Explained video reviews a major AI development through the lens of AI safety and model behavior. It is useful context for AI engineering, evaluation, governance, and operational risk.
Play video
This AI Explained video reviews a major AI development through the lens of governance and responsible deployment. It is useful context for AI engineering, evaluation, governance, and operational risk.
METR’s July 2025 analysis asks whether task-completion horizons transfer beyond its original software evaluations. It reanalyzes nine existing benchmarks, fitting success against human completion time where suitable measurements are available. The relationship is useful in several settings but differs across domains and weakens when proxies such as video length or task fees stand in for human effort. Some fits require constrained assumptions, and agents, datasets and scaffolds are not matched across benchmarks. The study supports checking the metric’s validity locally rather than treating a single horizon as a universal measure of autonomy.