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Murder Victim Speaks from the Grave in Courtroom Through AI
Chris Pelkey was shot and killed in a road rage incident. At his killer’s sentencing, he forgave the man via AI. In a historic first for Arizona, and possibly the U.S., artificial intelligence was used in court to let a murder victim deliver his own victim impact statement. What happened Pelkey, a 37-year-old Army veteran, was gunned down at a red light in 2021. This month, a realistic AI version of him appeared in court to address his killer, Gabriel Horcasitas. “In another life, we probably could’ve been friends,” said AI Pelkey in the video. “I believe in forgiveness, and The post Murder Victim Speaks from the Grave in Courtroom Through AI appeared first on DailyAI.
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Taiwan Semiconductor Manufacturing Just Showed the Artificial Intelligence (AI) Build-Out Is Alive and Well With This Jaw-Dropping Announcement - Yahoo Finance
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Could AI be conscious? - The Guardian
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In a bold move to tackle one of streaming’s biggest frustrations, endless scrolling, Netflix just unveiled a major redesign of its TV and mobile apps featuring a ChatGPT-powered AI chatbot and TikTok-style video reels. You’ll soon be able to ask Netflix in plain language what you’re in the mood for “funny and fast-paced” or “dark thrillers with strong female leads” and get instant, tailored recommendations. Netflix is partnering with OpenAI to power this feature, part of a broader overhaul aimed at making content discovery faster, more intuitive, and (finally) less painful. What’s changing Conversational AI Search: Powered by OpenAI, this The post Netflix Adds ChatGPT-Powered AI to Stop You From Scrolling Forever appeared first on DailyAI.
Best Universities To Study AI in 2026
Artificial intelligence has made enormous strides in the past few years – with the introduction of a wide range of AI tools changing the landscape of how we assess data and operate within online spaces forever. This page ranks the 50 best universities to study AI around the world, based on scope, prestige, and the level of AI-related research each institution has released. Career prospects in AI There is a huge demand for individuals with a high degree of skills in artificial intelligence and machine learning, making AI a potential lucrative career prospect with countless opportunities as AI continues to The post Best Universities To Study AI in 2026 appeared first on DailyAI.
10 top women in AI in 2026
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The agent evaluation gap: Enterprise AI organizations have a reality-alignment problem, not a coverage problem — and most are shipping to production anyway
Across 157 enterprises, organizations are granting AI agents more autonomy while trusting the evaluations meant to gate that autonomy less. Half have already shipped an agent that passed their internal evaluations and then failed a customer in production; only one in twenty fully trusts automated evaluation today; and the most-cited weakness is that evaluations do not align with real-world outcomes. Yet two-thirds already allow, or are actively engineering toward, deploying agent changes to production on automated evaluation alone — with no human in the loop. The result is an evaluation gap — the distance between how much autonomy enterprises are handing their agents and how far they trust the tests that are supposed to catch the failures. This wave of VentureBeat Pulse Research examines how technical leaders measure agent performance: which reliability and evaluation platforms they use, how they select and trust them, what breaks in production, and how far they are willing to let agents run without a human in the loop. The central finding is an evaluation gap — the distance between the autonomy enterprises are granting their agents and the trust they place in the evaluations meant to govern it. Half of organizations (50%) have, in the past year, deployed an agent or LLM feature that passed their internal evaluations and then caused a customer-facing failure, and a quarter have seen it happen more than once. Trust in the tests themselves is thin: only 5% say they fully trust automated evaluation today, and the single most-cited limitation is that evaluations align poorly with real-world outcomes (29%). Enterprises are discovering that a passing eval is not the same as a working agent. What makes the gap consequential is the direction of travel. Two-thirds of organizations (66%) already permit fully automated, zero-human-in-the-loop deployment for low-risk agents (34%) or are actively engineering their pipelines to allow it within twelve months (33%). At the same time, the evaluation stack that would have to earn that trust is fragmented and immature: the most common primary tools are the model providers’ native evals, tied with having no dedicated tooling at all (17% each); and only about a quarter of enterprises run real-time quality checks on live production traffic. The autonomy is arriving faster than the assurance. Methodology VentureBeat fielded this survey as part of its ongoing Pulse Research series, this survey — the Agentic Reliability & Evals tracker — focused on how technical leaders evaluate agent performance and reliability. Responses are filtered to organizations with 100 or more employees (n=157), drawn from a single survey in June 2026; because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends. Where questions were multiple-select, those shares can sum to more than 100%. By role the sample is senior and buyer-credible: 38% are final decision-makers for AI purchases and another 34% recommenders or influencers. Product and program managers (15%), consultants and advisors (10%), directors of engineering/IT (8%), and CIOs/CTOs/CISOs (8%) lead the named titles, alongside a large “Other” function (37%). By organization size the sample is mid-market-weighted: 100–499 (37%) and 500–2,499 (27%) employees lead, with 2,500–9,999 (20%), 10,000–49,999 (10%), and 50,000+ (6%) above them. Technology/Software is the largest industry at 23%, followed by Retail/Consumer (15%), Healthcare/Life Sciences (12%), and Manufacturing (10%). At 157 respondents the sample is large enough to read directionally but should be treated as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It skews toward the mid-market, so it is best read as the view from organizations actively standing up agent evaluation practices rather than from the largest operators. Note: This survey was rebuilt for the June wave from the earlier “LLM observability and evaluations” survey; because the questions and sample differ, no comparisons are made to the April–May data. Finding 1: A passing eval is not a working agent Half have shipped an agent that passed evals, then failed a customer We asked whether, in the past 12 months, organizations had deployed an agent or LLM feature that passed their internal evaluations but then caused a customer-facing failure. Half of those that run evaluations had. This is the report’s defining number. Half of organizations (50%) have shipped an AI feature that cleared their internal evaluations and then failed in front of a customer — an incorrect output, a broken workflow, or a quality incident — and a quarter have seen it happen more than once. Only 36% report no such failure, and the remainder either run no pre-deployment evaluations (8%) or don’t track the root cause closely enough to know (6%). The failure is precise and expensive: the evaluation said the agent was ready, and it was not. Everything that follows — how enterprises trust their evals, what they monitor, and how much autonomy they grant — is shaped by this experience. Finding 2: Almost no one fully trusts automated evaluation The top complaint: Evals don't match real-world outcomes We asked which limitation most reduces trust in automated agent evaluations today. Only a sliver of enterprises had no complaint at all. Trust in automated evaluation is scarce, and specific. Only 5% of organizations say they fully trust automated evaluation as it stands — meaning 95% name a limitation that holds them back. The most common, at 29%, is the one that most directly explains Finding 1: evaluations align poorly with real-world outcomes, passing agents that later fail. Bias or inconsistency (21%) and a lack of explainability (18%) follow — enterprises cannot always tell why an evaluation reached its verdict — and 17% cite data-leakage or privacy concerns in the evaluation process itself. The tests meant to certify agents are not yet trusted to certify them, which is precisely why the autonomy trajectory in Finding 3 is so striking. Finding 3: The autonomy ceiling is rising anyway Two-thirds already allow, or are building toward, zero-human deployment We asked whether organizations would let an autonomous agent deploy a code or system change to production on automated evaluation results alone, with no human-in-the-loop validation. The trajectory runs straight through the trust gap. Here is the paradox at the heart of the report. Even though almost no one fully trusts automated evaluation (Finding 2), two-thirds of organizations (66%) either already allow zero-human-in-the-loop deployment for low-risk agents (34%) or are actively engineering their pipelines to permit it within a year (33%). Only 22% rule it out for the foreseeable future. The direction is unambiguous: enterprises are moving to let evaluations gate production autonomously — removing the human check — at the same moment they say those evaluations don’t reliably match reality. The autonomy ceiling is rising faster than the assurance beneath it, which is the mechanism by which the false-confidence failures of Finding 1 will scale rather than shrink. Notably, the autonomy bet is not just a small company phenomenon. Splitting the sample by company size, larger enterprises are slightly further down the path toward zero human review than smaller companies (70% versus 64%) and slightly more likely to have shipped an evaluation-passing agent that then failed a customer (54% versus 48%). The assumption that large, regulated organizations are holding the human in the loop longest is, in this sample, backwards. To be sure, these are directional figures, since the survey was not a huge sample — 57 respondents from companies with 2,500+ employees and 100 from companies smaller than that. Finding 4: The evaluation stack is fragmented and provider-led Provider-native evals lead — tied with no dedicated tool at all We asked which agent reliability or evaluation platform enterprises primarily use today. The market has no clear leader — and a large share has nothing dedicated. The evaluation layer is early and unconsolidated. Provider-native tooling leads — OpenAI’s native evals and traces (17%) and Anthropic’s Claude Console evals (13%) together outweigh any independent platform — but it is tied at the top by a striking answer: 17% of enterprises use no dedicated agent-evaluation tooling at all, a notable gap for organizations shipping agents to customers. The specialist evaluation vendors — DeepEval (12%), Braintrust (8%), LangSmith, Weave, Promptfoo, Langfuse, Arize — are scattered across single to low double digits, and 11% have built their own. No independent platform has yet become the category standard, which leaves most enterprises evaluating agents with provider-native tools, home-grown scripts, or nothing. Finding 5: Production monitoring rarely watches output quality Only a quarter run real-time quality checks on live traffic Production monitoring for an AI agent can watch two very different things. It can watch whether the system is functioning — is the agent up and responding, did each request complete, how fast, at what cost, with any errors. Or it can watch whether the agent's output is correct — automated checks that evaluate the content of each answer as it goes out: did the agent give the right answer, take the right action, stay within policy. The distinction matters because a confidently wrong answer is invisible to the first kind of monitoring: the request completes, the response is fast, no error is thrown, and every functioning-metric reads healthy. We asked organizations which kind their live production monitoring is built for today. Grouped by what is actually being watched, the split is stark: 51% of organizations monitor only whether the agent is functioning, while 23% monitor whether its answers are right. Counting the ad-hoc reviewers and the don't-knows, roughly three-quarters of organizations run no automated, real-time evaluation of output correctness in production — they can see that the system is up and what it costs, and they are taking the correctness of its answers on faith. That blind spot is the runtime counterpart to the pre-deployment gap in Finding 1: the same organizations engineering the human out of the deployment decision mostly cannot see, in real time, when the deployed agent starts getting things wrong. Finding 6: Bought on cost, measured on consistency Price and integration drive selection; evaluation consistency is the goal We asked what most influenced enterprises’ choice of an evaluation vendor, and what they treat as their primary measure of success. Both answers are pragmatic. Enterprises buy evaluation tooling on economics and trust it on repeatability. Cost of evaluations (28%) narrowly leads selection, just ahead of ease of integration (27%) and evaluation accuracy (24%) — breadth of observability (13%) and vendor roadmap (4%) matter far less. On what success looks like, more than a third (36%) name evaluation consistency — getting the same verdict on the same behavior every time — well ahead of speed of experimentation (19%), reduction in failures (18%), production visibility (13%), and compliance (11%). The emphasis on consistency is telling: before enterprises can trust an evaluation’s verdict, they need it to be stable — the very property whose absence (bias and inconsistency) ranked among the top trust limitations in Finding 2. Satisfaction with current tooling is only moderate, averaging 3.8 on a five-point scale across overall satisfaction, ease of implementation, and value for money. Finding 7: The next dollar goes to humans and observability Investment is flowing to oversight, not just automation We asked which reliability and evaluation investment will grow most over the next year. The money is going toward watching agents more closely — including with people. The second-largest planned investment — behind only production observability — is human review workflows, at 26%. Read against Finding 1, that is the report's quietest contradiction: at the same moment two-thirds of enterprises are engineering the human out of the deployment decision, more of them plan to grow spending on human reviewers (26%) than on the automated evaluation pipelines (16%) that would replace them. The zero-human trajectory and the human-review budget are rising in the same companies at the same time. Indeed, only 8% report that their budget is not increasing. Taken together, enterprises are hedging: building toward autonomy while spending to watch agents more closely and keep humans available for the calls that automated evaluation cannot yet be trusted to make. Finding 8: A tooling reshuffle is coming Nearly two-thirds plan to adopt or switch platforms within a year We asked whether enterprises plan to adopt a new, additional, or replacement evaluation platform, and which they are considering. Few intend to stand pat. The evaluation market is wide open. While 36% have no plans to change, a clear majority (64%) intend to adopt a new, additional, or replacement platform within twelve months, and 31% within the next quarter. The consideration set points where current usage is thinnest: Confident AI’s DeepEval leads what enterprises are evaluating (20%), ahead of OpenAI’s native evals (13%) and Braintrust (9%) — the open-source specialists drawing more interest than their present footprint. Given that so many enterprises today rely on provider-native tools or nothing at all (Finding 4), this is less a defection than a first real wave of tooling adoption — the moment the evaluation layer starts to consolidate. Which platforms earn that trust, in a market where almost no one trusts automated evaluation yet, is the open question this series will keep tracking. The bottom line: An evaluation gap that autonomy will widen, not close Organizations with 100 or more employees are granting AI agents more independence than they trust their evaluations to support. Half have already shipped an agent that passed its evals and then failed a customer; almost none fully trust automated evaluation, chiefly because it doesn’t match real-world outcomes; and most watch production for uptime and cost rather than for whether the agent’s answers are right. Yet two-thirds already allow, or are actively building toward, deploying to production on automated evaluation alone. The vendor market is early and unsettled: the most common primary evaluation tools are provider-native evals, tied with no dedicated tooling at all, and a clear majority plan to adopt or switch platforms within the year. Encouragingly, the next dollar is going to observability and — pointedly — human review, suggesting enterprises sense the gap even as they engineer past it. At 157 respondents in a single wave this is a directional read, skewed toward the mid-market — but the direction is clear: autonomy is being granted on the strength of evaluations that the people granting it do not yet trust. The evaluation gap is not a coverage problem that more tests alone will close; it is a problem of evaluations that reflect reality and can be trusted to gate it. The open question for later waves is whether assurance catches up to autonomy — or whether the false-confidence failures move from customer incidents into changes that deploy themselves. Based on survey responses from 157 qualified enterprise respondents (100+ employees), drawn from a single June 2026 wave. This is a directional read rather than a precise measurement — the sample is self-selected, not a probability sample, and skews toward the mid-market. Respondents include product and program managers, consultants and advisors, directors of engineering/IT, and CIOs/CTOs/CISOs, among other functions, across technology/software, retail/consumer, healthcare/life sciences, manufacturing, and other industries.
The AI ‘Memory Wall’ Is About to Get a Lot Taller: These 3 Stocks Will Win Big - 24/7 Wall St.
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China Unveils World’s First AI Hospital: 14 Virtual Doctors Ready to Treat Thousands Daily
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Google just redesigned the search box for the first time in 25 years — here’s why it matters more than you think.
For a quarter century, the Google search box has been one of the most recognizable interfaces in computing: a thin white rectangle, a blinking cursor, a few typed words, and a list of blue links. On Tuesday, Google will formally retire that paradigm. At its annual I/O developer conference, Google announced a sweeping redesign of the search box itself — the literal text field where billions of queries begin every day — transforming it from a simple keyword input into a dynamic, AI-driven conversation starter that can accept text, images, PDFs, videos, and even open Chrome tabs as inputs. The company is also merging its AI Overviews and AI Mode features into a single, seamless search flow, eliminating the friction that previously forced users to choose between a traditional results page and an AI-forward experience. Liz Reid, Google's vice president and head of Search, called it "the biggest upgrade to our iconic search box since its debut over 25 years ago" during a press briefing on Monday. The announcement arrived alongside a blizzard of other news — new Gemini models, a personal AI agent called Spark, an intelligent shopping cart, a reimagined developer platform — but the search box redesign may prove to be the most consequential. It is the clearest signal yet that Google views the future of its flagship product not as a place where users type fragmented keywords, but as an interface where they hold open-ended, multimodal conversations with an AI system backed by the entire web. The new search box expands, accepts files, and coaches you on what to ask The changes show a fundamental shift in how Google expects people to interact with the product that generates the vast majority of Alphabet's revenue. The box itself now dynamically expands to accommodate longer, more conversational queries. Where the old interface subtly encouraged brevity — a narrow field suited to two- or three-word keyword strings — the new design invites users to fully articulate complex questions in granular detail. It also now supports multimodal inputs directly. Users can upload images, PDFs, files, and videos, or drag in content from Chrome tabs, right from the main search interface. Previously, some of these capabilities existed in AI Mode, but reaching them required extra steps. Now they sit at the primary entry point. Google is also deploying what it describes as an AI-powered query suggestion system that "goes beyond autocomplete." Rather than simply predicting the next word a user might type based on popular searches, the system helps users formulate complex, nuanced queries — essentially coaching them toward the kind of detailed questions that AI Mode handles best. The new search box is starting to roll out immediately in all countries and languages where AI Mode is available. Google is merging AI overviews and AI mode into one seamless experience Perhaps more significant than the box itself is the architectural change happening behind it. Google is unifying AI Overviews — the AI-generated summary panels that appear atop traditional search results — with AI Mode, the more immersive conversational search experience the company launched at I/O one year ago. Starting Tuesday, this merged experience will be live across mobile and desktop worldwide. A user can type a question, receive an AI Overview alongside traditional results, and then continue directly into a back-and-forth AI Mode conversation to ask follow-up questions — all without navigating to a separate interface. Reid explained the logic during the press briefing: the new AI search box is "an upgrade of our traditional search box, and so the results take you directly to main search rather than AI mode." She noted that while some power users actively sought out AI Mode, "for most users, they don't actually want to have to think about, do they want more of a traditional page or an AI-forward search experience." The goal, she said, was to ensure that "for most users, they don't have to think about where to go, they can just go to the search box they're familiar with, and it feels like they get the best experience afterwards." One billion users and doubling queries reveal how fast search behavior is shifting Google's decision to redesign the foundational interface of its most important product did not happen in a vacuum. The company shared a set of usage statistics during the briefing that reveal just how rapidly user behavior is already changing. AI Mode, which launched in the United States at I/O 2025, has surpassed one billion monthly users in its first year. AI Mode queries have been doubling every quarter since launch. AI Overviews, the lighter-weight AI summaries, now reach more than 2.5 billion monthly users. And overall search query volume hit an all-time high last quarter — a data point the company had previously disclosed on its earnings call. Sundar Pichai, Google's CEO, framed these figures as evidence that AI features are additive, not cannibalistic, to search usage. "When people use our AI-powered features in search, they use search more," he said. He added that he loves "how search has become less about individual queries and feels more like an ongoing conversation, giving users deeper insights and connecting you with the vastness of the web." Reid reinforced the point: "It's not just that people are searching more, it's that they're searching differently. They're fully expressing their questions in granular detail, asking those follow-up questions and searching across modalities." Gemini 3.5 Flash gives Google's AI search the speed it needs to work at scale Under the hood, the new search experience runs on Gemini 3.5 Flash, Google's newest AI model, which the company also introduced at I/O. Google upgraded AI Mode's underlying model to 3.5 Flash to deliver what Reid described as "an even more powerful AI search experience." Gemini 3.5 Flash is the workhorse of this year's announcements. Google claims it outperforms its previous frontier model, Gemini 3.1 Pro, on nearly all benchmarks while running four times faster in output tokens per second than comparable frontier models. Pichai described it as being "in a league of its own in the top right quadrant" of the Artificial Analysis index, which plots intelligence against speed — meaning it delivers near-frontier quality at dramatically lower latency. That speed matters enormously for search. A conversational AI search experience that feels sluggish would be dead on arrival for a product that serves billions of queries daily. By coupling the redesigned interface with a model optimized for both quality and throughput, Google is attempting to make AI-powered search feel as instantaneous as the old keyword experience — while being dramatically more capable. Search can now build interactive visuals and custom mini apps on the fly The redesigned search box is also the gateway to a set of new capabilities that push search far beyond text-based answers. Google announced what it calls "generative UI" — the ability for search to dynamically build custom widgets, interactive visualizations, and even mini applications in real time, tailored to a user's specific question. Reid offered a concrete example during the briefing: a user could ask "How do black holes affect space time?" and receive an interactive visual in an AI Overview that brings the concept to life. Follow-up questions would trigger the system to dynamically generate entirely new visuals in real time. This is possible, she explained, because of "a novel real-time code generation system we built in partnership with the Google DeepMind team" that runs on Gemini 3.5 Flash. Generative UI capabilities will roll out to everyone this summer, free of charge. But Google is going further still. For ongoing tasks — planning a wedding, organizing a move, tracking a fitness routine — users will be able to build what the company describes as customizable, stateful experiences within search, powered by its Antigravity development platform. These require no coding expertise. Users simply describe what they want in natural language, and search builds it. Those experiences will be available in coming months, starting with Google AI Pro and Ultra subscribers in the United States. AI agents that monitor the web around the clock are coming to search results The redesign also opens the door to what Google calls "information agents" — AI agents that users can configure directly within search to monitor the web 24/7 for specific conditions and deliver synthesized updates when those conditions are met. A user could, for example, set up an agent to track market movements in a particular sector with specific parameters. The agent would create a monitoring plan, tap into real-time finance data, and proactively notify the user when conditions are met — complete with links and context for further research. Other use cases include apartment hunting, tracking sneaker drops, or monitoring any topic a user cares about. Information agents will launch first for Google AI Pro and Ultra subscribers this summer. These agents sit within a much larger strategic pivot that Google articulated throughout the briefing: the company is going all-in on AI systems that don't just answer questions but proactively take actions on users' behalf. Beyond search, Google introduced Gemini Spark, a 24/7 personal AI agent that runs on dedicated virtual machines in Google Cloud. It unveiled the Universal Cart, an intelligent cross-merchant shopping cart. It announced the Agent Payments Protocol for agents to make secure purchases. And it expanded its Antigravity developer platform into a full ecosystem for building autonomous AI agents. Publishers, advertisers, and SEO professionals face a new reality The redesign raises profound questions for the sprawling ecosystem — publishers, advertisers, SEO professionals — that has been built around the old model of keyword search and blue links. If users increasingly express their needs as full, conversational sentences rather than fragmented keywords, the entire discipline of search engine optimization will need to evolve. Keyword-density strategies become less relevant when the AI is parsing natural language intent rather than matching strings. Content that answers deep, nuanced questions in authoritative ways becomes more valuable; content engineered to rank for two-word keyword fragments becomes less so. For publishers, the stakes are existential. AI Overviews already synthesize information from across the web and present it directly in search results, reducing the need for users to click through to source material. The new seamless AI Mode integration deepens that dynamic: users can now get an AI-generated answer and ask multiple follow-up questions without ever leaving the search page. Google has consistently maintained that its AI features drive more traffic to publishers, but the redesign puts that claim under renewed scrutiny as the search results page becomes more self-contained. For advertisers — who fund the vast majority of Google's revenue — the shift from keywords to conversations changes the calculus of ad targeting. Conversational queries contain richer intent signals, which could make ad targeting more precise and valuable. But they also create new ambiguities: when a user is in the middle of a multi-turn conversation with AI Mode, where does an ad naturally fit? Google did not detail changes to its advertising model during the briefing, but the structural shift in the interface will inevitably reshape how ads are surfaced and measured. The search box was always more than a product — it was a habit for billions of people There is a reason Google chose to redesign the search box rather than simply adding new features behind it. The search box is not just a product element at this point; it is a cultural artifact — one of the few pieces of digital infrastructure used by essentially the entire internet-connected world. Changing it sends an unmistakable message about where the company believes computing is headed. For 25 years, the search box trained billions of people to think in keywords — to compress their curiosity into the shortest possible string of words. The new box invites them to do the opposite: to think out loud, to upload what they're looking at, to ask follow-up questions, to let an AI system handle the compression. Pichai tied the company's broader ambitions to a striking statistic: Google's surfaces now process over 3.2 quadrillion tokens per month, up seven-fold from a year ago. The company expects capital expenditures of approximately $180 to $190 billion in 2026 — roughly six times the $31 billion it spent four years ago — largely to support the infrastructure required for this AI transformation. When asked about the future of traditional search, he was direct. "Search is the most used AI product in the world," he said. The blinking cursor in Google's search box still invites you to type. But after 25 years of teaching the world to speak in keywords, Google is now asking it to speak in sentences — and betting roughly $190 billion that it will.
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The agent security gap: 54% of enterprises have already had an AI agent incident, and most still let agents share credentials
Across 107 enterprises, AI agents are being given real access to systems and data while the controls meant to contain them lag behind. More than half have already had a confirmed agent security incident or a near-miss; only about a third give every agent its own scoped identity, and most agents still share credentials; and only three in ten isolate their highest-risk agents. The security stack is overwhelmingly borrowed from the model providers and hyperscalers rather than purpose-built for agents, spending remains a thin slice of the security budget, and enterprises are evenly split on whether their defenses are keeping pace with AI-enabled attackers. The result is an agent security gap — autonomous agents proliferating faster than the identity, isolation, and enforcement controls needed to hold them. This wave of VentureBeat Pulse Research examines how enterprises secure their AI agents: what tooling they run, how they manage agent identity and isolation, what has already gone wrong, how much they spend, and whether they believe their defenses are keeping pace with AI-enabled attackers. The central finding is an agent security gap — the distance between the autonomy enterprises are granting their agents and the controls in place to contain them. More than half of organizations (54%) have already experienced a confirmed agent security incident (18%) or a near-miss caught before harm (36%). The structural weakness beneath those numbers is identity: only about a third (32%) give every agent its own scoped, managed identity, while the rest report that some agents share credentials or that agents mostly run on shared API keys and human or service-account credentials. When agents share credentials, a single compromised or over-permissioned agent carries a wide blast radius — and only three in ten enterprises (30%) isolate their highest-risk agents in sandboxes to bound that radius. What makes the gap notable is how comfortable enterprises are inside it. The security stack is overwhelmingly provider-native — OpenAI’s guardrails (51%), Google’s and Microsoft’s cloud controls, and Anthropic’s managed-agent controls dominate, while the dedicated agent-security specialists barely register — and satisfaction with that borrowed stack is high, averaging 4.2 out of 5. Yet spending remains a thin slice of the security budget, only a third of enterprises believe their AI defenses are ahead of AI-enabled attackers, and a clear majority plan to change tooling within the year. Enterprises are satisfied with controls they are simultaneously preparing to replace. Methodology VentureBeat fielded this survey as part of its ongoing Pulse Research series, this instrument focused on enterprise agent security — the tooling, identity, isolation, and enforcement controls organizations use to secure autonomous AI agents. Responses are filtered to organizations with more than 100 employees (n=107; the survey’s smallest size band, 1–100 employees, is excluded), drawn from a single June 2026 wave. Because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends. Several questions were multiple-select, so those shares can sum to more than 100%. By role the sample is senior and buyer-credible: 45% are final decision-makers for AI purchases and another 30% recommenders or influencers. Managers (43%), individual contributors (24%), VPs and directors (15%), and the C-suite (11%) make up the seniority mix. By organization size the sample is mid-market-weighted: 251–1,000 (42%) and 101–250 (25%) employees lead, with 1,001–5,000 (19%), 5,001–10,000 (8%), and 10,001+ (7%) above them. Technology/Software is the largest industry at 23%, followed by Manufacturing (15%), Retail/E-commerce (14%), and Healthcare/Life Sciences (13%). At 107 respondents the sample is large enough to read directionally but should be treated as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It skews toward the mid-market, so it is best read as the view from organizations actively standing up agent security rather than from the largest operators. Satisfaction ratings are computed on the respondents who answered each rating question; the overall satisfaction score reflects 82 of the 107 qualified respondents. Finding 1: The incidents are already here More than half have had an agent security incident or near-miss We asked whether organizations had experienced an agent security incident — a confirmed breach, or a near-miss caught before harm. Most that run agents in production had. This is the report’s defining number. More than half of organizations (54%) have already had an agent security event — 18% a confirmed incident and 36% a near-miss caught before it caused harm. Only 42% report nothing, and a small remainder either run no agents in production or don’t track such events. That so many report near-misses rather than only confirmed incidents is telling: enterprises are catching problems, but they are catching them close to the edge. The controls examined in the rest of this report — identity, isolation, enforcement — are what determine whether the next near-miss stays a near-miss. Exposure scales with company size, but containment does not. The incident-or-near-miss rate rises from 49% in the mid-market (companies with 101-1,000 employees) to 63% at larger enterprises (above 1,000 employees), while sandbox isolation of high-risk agents falls from 35% to 20%, and satisfaction with security tooling drops from 4.36 to 3.97. The organizations running the most agents across the most systems carry the most incidents and the least of the one control that bounds an incident's blast radius. Finding 2: The identity gap Only a third give every agent its own scoped identity We asked how enterprises manage the identity of their AI agents — whether each agent has its own credentials, or agents share them. Full per-agent identity is the exception. Rolled together, the overlapping answers show 69% of enterprises (74 of 107) with credential sharing somewhere in the agent fleet. Identity is the structural weakness beneath the incidents. Only about a third of enterprises (32%) give every agent its own scoped, managed identity — the precondition for least-privilege access and clean attribution. Nearly half (48%) say some agents have scoped identities but many still share credentials, and another 32% say agents mostly run on shared API keys or borrowed human and service-account credentials. (Respondents could describe more than one pattern across their agent fleet, so these overlap.) The consequence is direct: when agents share credentials, an over-permissioned or compromised agent can act with far more reach than intended, and forensics after an incident cannot cleanly tell which agent did what. The non-human identity problem — giving every agent its own governed identity — is the single largest unfinished piece of enterprise agent security. Moreover, a company’s agent credential posture is correlated with incidents. Organizations with credential sharing anywhere in the fleet were hit — with an incident or a near-miss in the past twelve months — at 63.5% (47 of 74). Organizations where every agent carries its own scoped identity were hit at 40.9% (9 of 22). The fully-scoped group is small, so for now the relationship is an association rather than proven causation, and the gap is concentrated in the mid-market — but within a single survey, a twenty-three point difference in incident rate suggests significance. Finding 3: Observe and enforce, but rarely isolate Only three in 10 sandbox their highest-risk agents We asked what an organization’s agent security posture looks like in practice — whether they observe, enforce, isolate, or some combination. The control that bounds damage is the least common. Monitoring and enforcement are reasonably common; containment is not. Roughly half of enterprises observe agent activity (47%) or enforce scoped permissions at runtime (49%), but only 30% isolate their highest-risk agents in sandboxes that bound the blast radius when the other controls fail. That ordering is backwards from a defense-in-depth standpoint: observation tells you what happened, enforcement tries to prevent it, but isolation is what limits the damage when prevention fails — and it is the control enterprises have adopted least. Combined with the identity gap in Finding 2, the picture is of agents that are watched and permissioned but rarely boxed in, which is precisely the configuration in which a single failure propagates. Finding 4: Security runs on borrowed, provider-native controls Guardrails from OpenAI, Google and Microsoft dominate; specialists barely register We asked which agent security tooling enterprises use, and which is their primary layer. The answer favors the model providers and hyperscalers over the dedicated security vendors. Enterprises are securing agents with tools that came bundled with their models and clouds. OpenAI’s guardrails lead at 51%, followed by Google’s and Microsoft’s cloud-native controls and Anthropic’s managed-agent controls — and when asked to name their single primary security layer, 82% name one of these provider-native offerings. The purpose-built agent-security category — Palo Alto’s Prisma AIRS, CrowdStrike, Cisco AI Defense, Zenity, HiddenLayer, Check Point’s Lakera, Okta for AI Agents, non-human identity platforms — barely registers, each in the low single digits, and only 5% run no dedicated tooling at all. As with retrieval and evaluation elsewhere in this series, the provider bundle is winning the default: enterprises reach first for the guardrails their platform ships, and the independent security layer that would address the identity and isolation gaps has not yet been adopted at scale. The provider-default pattern is consistent across both Q2 survey waves. In April–May (n=110), usage was led by the same names — OpenAI's controls at 26%, Azure at 15%, AWS at 14%, Google at 12% — with every dedicated agent-security specialist at 3% or below and one in ten using no dedicated tooling at all. The common finding from the two surveys: Enterprises are defaulting to the solutions provided by the platform they’re using, and the specialist category vendors have yet to become big players here. (A note on reading these shares. As described in the methodology section, the respondent sample is self-selected and skews mid-market, and the usage question counted every vendor or approach a respondent has in place — so the figures measure presence in the security stack rather than spending or exclusivity. Individual vendor percentages therefore carry all the usual sample caveats. The structural pattern, however, held across both Q2 waves on two differently worded questions: provider-native and hyperscaler controls lead, and dedicated agent-security specialists remain in low single digits. Read the individual shares loosely and the pattern with confidence.) Finding 5: And enterprises are comfortable with it Satisfaction is high, even as incidents mount and identity lags We asked how satisfied enterprises are with their current agent security tooling. The comfort is notably out of step with the exposure documented above. Satisfaction with agent security tooling is high — 4.2 out of 5 overall, and 4.1 for value for money — among the most positive readings in this series. That is the striking part: enterprises are highly satisfied with a stack that is mostly borrowed provider guardrails, even though more than half have already had an incident or near-miss and only a third give their agents scoped identities. The comfort appears to rest on the convenience and low friction of provider-native controls rather than on demonstrated containment. It is a false comfort in the making — the same enterprises expressing satisfaction are, as Finding 8 shows, a clear majority planning to change tooling within the year, which suggests the confidence is thinner than the score implies. Finding 6: Budgets haven’t caught up Most spend under a tenth of the security budget on agents We asked what share of the security budget enterprises allocate to securing AI agents. For a fast-emerging risk, the allocation is modest. Spending on agent security is still a thin slice. The most common allocation is 6–10% of the security budget (46%), and a third of enterprises (34%) spend 5% or less; only a quarter (24%) devote more than a tenth. Given the incident rate in Finding 1 and the identity and isolation gaps in Findings 2 and 3, the budget looks like a lagging indicator — the risk has arrived faster than the funding to address it. The enterprises spending more than a tenth of their security budget on agents are a distinct minority, and they are likely the ones building the scoped-identity and isolation controls the rest have not. Finding 7: The arms race is even, at best Only a third think their AI defenses are ahead of AI-enabled attackers We asked how enterprises assess the balance between their AI-enabled defenses and AI-enabled attackers. Confidence is far from settled. Enterprises are split on whether they are winning. Only about a third (35%) believe their AI-enabled defenses are ahead of AI-enabled attackers; the rest are less sure — 32% call it roughly even, 21% think attackers are ahead, and another 21% say it is too early to tell. Taken together, a clear majority (53%) rate the balance as even or tilted toward the attacker. That uncertainty sits uneasily beside the high satisfaction of Finding 5: enterprises are content with their tooling yet unconvinced it is winning the contest it exists to win. In a domain where the offense is also compounding with AI, an even race is not a comfortable place to be. Finding 8: A security reshuffle is coming Nearly six in 10 plan to adopt or switch tooling within a year We asked whether enterprises plan to adopt a new, additional, or replacement agent security solution, and which they are considering. Few intend to stand pat. The security stack is not settled. While 41% have no plans to change, a clear majority (59%) intend to adopt a new, additional, or replacement agent security solution within twelve months, and 29% within the next quarter — a strong signal that, high satisfaction notwithstanding, enterprises know the current stack is provisional. Incidents are what start the buying cycle. Among organizations that have been hit, 42.1% plan to adopt, add, or replace agent security tooling within the next ninety days, against 14.0% of organizations with no incident — and after a confirmed incident it becomes majority behavior, at 52.6%. Getting hit also changes the threat assessment: 33.3% of hit organizations say AI-armed attackers are ahead of their defenses, against 8.0% of the unhit. Experience, in this data, is the strongest predictor of both urgency and pessimism. The consideration set still leans provider-native (OpenAI 34%, Google 30%, Anthropic 29%, Azure 25%), but the dedicated security vendors — Cloudflare, Cisco, Palo Alto, Okta, Check Point’s Lakera — draw early interest in the mid-to-high single digits, more than their current footprint. What the shopping does not yet include is the identity layer specifically. Twelve percent of the respondents include an agent-identity product — Okta for AI Agents, Microsoft Entra Agent ID, or a non-human identity platform — anywhere in their consideration set, and among the credential-sharing organizations that have already had an incident, identity consideration is essentially unchanged, at roughly one in ten. The control most directly implicated by the incident data is the one largely missing from the purchase plans. Whether this wave hardens the provider-native default or finally opens the door to purpose-built agent security — the identity and isolation controls the incidents call for — is the question this series will keep tracking. The bottom line: A security gap that autonomy will test first Organizations with more than 100 employees are giving AI agents real reach into systems and data while securing them with controls built for something else. More than half have already had an incident or near-miss; only a third give every agent its own scoped identity, and most still share credentials; only three in ten isolate their highest-risk agents; and the stack doing this work is overwhelmingly borrowed from the model providers and hyperscalers rather than purpose-built for agents. The uncomfortable pairing is confidence with exposure: satisfaction with the current tooling is among the highest in this series, yet spending is a thin slice of the security budget, only a third believe their defenses are ahead of AI-enabled attackers, and a clear majority are already planning to replace what they have. At 107 respondents in a single wave this is a directional read, skewed toward the mid-market — but the direction is clear: agent adoption is running ahead of agent security, and the controls that matter most when something fails — scoped identity and isolation — are the ones enterprises have built least. The agent security gap is not a coverage problem that a provider guardrail will close on its own; it is a problem of identity, isolation, and enforcement built for autonomous software. The open question for later waves is whether enterprises close it deliberately — or whether a confirmed incident closes it for them. Based on survey responses from 107 qualified enterprise respondents (100+ employees), drawn from a single June 2026 wave. This is a directional read, not a precise measurement — the sample is self-selected and skews mid-market, so it's best read as the view from organizations actively standing up agent security rather than from the largest operators. Respondents are senior and buyer-credible (45% final decision-makers, 30% recommenders/influencers), spanning managers through the C-suite, and drawn primarily from Technology/Software, Manufacturing, Retail/E-commerce, and Healthcare/Life Sciences.
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