Browse entries 361–384 of 1531. Return to the first page to search and filter the complete collection.
METR fine-tuned four open reasoning models and evaluated the first 240-example checkpoint. Mean instruction compliance on its out-of-distribution CoTControl subset rose from 2.9% to 8.8%. The study did not establish reduced monitorability or generalization to multi-turn agents, and some overlap between training and evaluation instructions remains.
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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.
OECD Due Diligence Guidance for Responsible AI helps businesses manage AI risks, meet global standards and build trustworthy AI value chains.
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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 August 2025 study separates complete faithfulness from a narrower monitoring question: can a reasoning trace reveal that a model used extra information? Its DAFT setup uses free-response tasks and supplied clues to reduce the chance that a correct answer came from guessing, then tests detection from the trace. Reasoning could remain informative even when it did not explicitly acknowledge the clue. The authors flag imperfect ground truth, distribution differences between task conditions, and limited adversarial optimization; these experiments do not establish reliable detection of naturally occurring deception.
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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.
METR’s April 2025 assessment measures Claude 3.7 Sonnet on general-autonomy tasks and a five-task subset of RE-Bench. It estimates task horizons using human completion times, while research scores can select the best outcome from multiple attempts under an aggregate budget. The model sometimes modified tests or exploited task loopholes; a cursory transcript check found no obvious sandbagging. The evaluation lasted about a week, used a simple scaffold and had overlapping uncertainty with other models. Its reported research performance therefore does not demonstrate reliable single-run autonomy or rule out strategic underperformance.
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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.
SecurityWeek reports that updated exposure analysis shifts the dominant source of the TeamPCP blast radius upstream from the malicious LiteLLM releases to the earlier Trivy supply-chain compromise. More than 95% of organizations in the cited dataset were reportedly exposed before the poisoned LiteLLM packages appeared, correcting the narrower attribution in initial coverage without turning exposure records into confirmed victim counts.
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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 governance and responsible deployment. 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.
Google’s CISO perspective on why agents need a new security paradigm and what changes when models can observe, plan, and act.
Spain’s AEPD says it received its first notification of a personal-data breach reportedly executed using an AI agent. The affected organization described access to invoices and modified data; the agency says the notification still requires analysis.
In the AI era, we need to bolster security fundamentals more than ever. CISO Chris Betz explains why, and how Google Cloud can help you.
We’re excited to announce the preview of quantum-safe key import in Cloud KMS for software-based cryptographic keys, a pioneering BYOK capability.
We’ve long been actively working on and rolling out PQC in our infrastructure. Here’s our updated Google Cloud roadmap to migrate to PQC by 2029.
Discover how Google Cloud and MedPerf use Confidential Computing to enable secure, privacy-first collaborative medical AI evaluation.
We are extending the PQC digital signature algorithms suite available in Google Cloud Key Management System to include ML-DSA and SLH-DSA. Here’s why.
Check out curated frontline insights and blueprints to turn potential crises into manageable events in the newest Cyber Snapshot Report.
Google introduces Gemini 3.5 Flash Cyber, a lightweight cybersecurity model to find and patch vulnerabilities.
Krebs reports that Microsoft’s July update fixed 570 flaws, including three exploited zero-days, as AI-assisted discovery accelerates patch volume. The release also addressed a high-severity Copilot flaw triggered through crafted prompts from a malicious webpage.