Browse entries 553–576 of 1576. Return to the first page to search and filter the complete collection.
![Hassabis, Altman and AGI Labs Unite - AI Extinction Risk Statement [ft. Sutskever, Hinton + Voyager] video thumbnail](https://i.ytimg.com/vi/f20wXjWHh2o/hqdefault.jpg)
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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.
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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.
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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.
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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.
![What's Up With Bard? 9 Examples + 6 Reasons Google Fell Behind [ft. Muse, Med-PaLM 2 and more] video thumbnail](https://i.ytimg.com/vi/XmnTd92NqFw/hqdefault.jpg)
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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.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
![8 New Ways to Use Bing's Upgraded 8 [now 20] Message Limit (ft. pdfs, quizzes, tables, scenarios...) video thumbnail](https://i.ytimg.com/vi/weH9LKYNGWg/hqdefault.jpg)
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This AI Explained video reviews a major AI development through the lens of model capability and AI systems in practice. It is useful context for AI engineering, evaluation, governance, and operational risk.
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This AI Explained video reviews a major AI development through the lens of model capability and AI systems in practice. It is useful context for AI engineering, evaluation, governance, and operational risk.
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Sam Parsons walks through agent-assisted commerce from merchant discovery to hosted checkout and a payment token consumed by the merchant’s processor. The token can be constrained by merchant, amount, currency and expiry, allowing the shopping interface to pass scoped payment authority rather than raw card data. External checkout also requires correlating completion with the originating session and order. The demonstration explains integration boundaries; it does not establish a complete security audit or show that tokenization removes all payment-compliance obligations.
New Google Cloud session controls are deeply integrated and a granular feature of Context-Aware Access. Here’s what you need to know.
CVE-2026-64849 is an unauthenticated SSRF flaw in MLflow model-registry webhooks that lets anyone reaching a vulnerable Tracking Server proxy requests to internal services and cloud metadata endpoints. The redirect handling bypasses earlier fixes, and honeypot telemetry showed indiscriminate scanning within hours of disclosure aimed at stealing cloud credentials and secrets. MLflow fixed the issue in 3.15.0.
It’s time to have The Talk: Here’s what boards and CISOs should be discussing on AI-era security governance and business agility.
From defending networks to enabling attacks, artificial intelligence is changing every aspect of cybersecurity. Here's what dozens of experts say security leaders need to understand now.
Chris Betz, our new CISO, shares the 4 key lessons that helped us develop AI Threat Defense to fight AI with AI.
Cloudflare describes architecture and operational lessons for defending against frontier cyber models. Relevant to AI-enabled threat modeling, defensive controls, and internal security readiness.
Cloudflare article on accountability models as AI assistants and privacy proxies blur bot and human distinctions. Relevant to agent identity, abuse prevention, and web access controls.
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This AI Explained video reviews a major AI development through the lens of benchmarks and evaluation evidence. It is useful context for AI engineering, evaluation, governance, and operational risk.
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Jake Broekhuizen distinguishes retaining conversation traces from producing memory that changes later behavior. Facts, useful past examples, and procedural instructions serve different purposes and need different update policies. The talk describes extracting lessons from runs, storing them outside the immediate conversation, and loading relevant material on later tasks. LangChain’s memory documentation makes the tradeoffs explicit: a compact profile is easy to retrieve but difficult to update reliably, while a collection needs careful search, deletion, and reconciliation. Procedural changes can alter what an agent does, so learned instructions require stronger review than ordinary factual additions.
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Charles Frye explains an inference engine as a request server, a scheduler and a model runner. Queueing and prompt prefill contribute to time to first token; repeated decoding governs later token delivery. KV caching, prefix reuse, batching, CUDA graphs and speculative decoding address different parts of that workload and introduce their own resource tradeoffs. A museum application example illustrates how a demand spike can worsen both initial and subsequent token latency. The talk provides a diagnostic model, not a universal speedup claim.
ESET reported a safety-sensitive comment embedded in a UAC-0099 malicious VBS script, apparently intended to divert an AI analyst from examining the code. The disclosure establishes the presence and intended purpose of the text; it does not show that an analysis system stopped, missed the malware or changed its verdict.
What happens when you clear dozens of Trail of Bits engineers’ schedules, pair them with every open-source maintainer they can contact, and unleash the latest frontier models like GPT-5.5-Cyber on critical open-source targets?
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NDC Security 2026 talk on testing generative AI systems, arguing that AI red teaming needs to evaluate variable behavior, safety boundaries, and AI-assisted testing methods rather than a single exploit result.
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NDC Security 2026 talk on prompt injection in CI/CD and automation systems, including AI agents with access to shell commands, GitHub or GitLab tokens, issue editing, build workflows, and privileged pipeline context.
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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.