Across 116 enterprises, agents are in production and so are the incidents: A majority have already had a confirmed agent security event or a near-miss. Two-thirds of enterprises enforce scoped permissions at runtime. Barely one in five isolates its highest-risk agents, making containment the weakest layer in the stack precisely as autonomy scales. Credential sharing persists across nearly two-thirds of agent fleets, and 53% have already had a confirmed agent security event or near-miss, contributing to a growing lack of confidence in agentic security. Security stacks remain overwhelmingly borrowed from model providers and hyperscalers, and confidence has slipped. Today, as many enterprises now believe AI-armed attackers are ahead of their defenses as believe the reverse.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.Only 18% of enterprises isolate their highest-risk AI agents, even as 65% of enterprises enforce scoped permissions at runtime and 56% monitor and log agent activity. The gap between what enterprises watch and what they contain is the central finding of this wave of VentureBeat Pulse Research. More than half of enterprises (53%) have agentic AI systems in production today, and another 27% are piloting or running a limited rollout. The agentic security incidents are arriving with them: 53% of organizations have already had an agent security event, with 19% confirming an incident and 38% having identified a near-miss that was caught before it caused harm.The central finding is a containment gap. Enterprises have built the controls that watch and permission agents but not the one that bounds the damage when those fail. Among enterprises describing their security posture, 65% enforce scoped identities and permissions at runtime and 56% observe and log agent activity, yet only 18% isolate high-risk agents in sandboxes. Even among enterprises running agents in production, isolation is enforced just 21% of the time, and just 8% pair enforcement with isolation. That ordering is backward from a defense-in-depth standpoint. From SOC teams to CISOs, security teams know that observation tells you what happened and enforcement tries to prevent it, but isolation is what limits the blast radius when prevention fails.Identity has improved without being solved. 49% of enterprises say each of their agents has its own scoped, managed identity, but 63% report credential sharing somewhere in the agent fleet, and only 29% describe a fleet with scoped identities and no sharing anywhere. The security stack doing this work remains overwhelmingly hyperscaler or model provider-native: OpenAI’s guardrails (44%), Microsoft Azure (42%), Anthropic’s managed-agent controls (37%), and Google Cloud (31%) lead, and 92% of enterprises naming a primary security layer name a hyperscaler/model provider-native one.Two things have shifted against the comfortable picture. Confidence has slipped, with 30% now saying AI-armed attackers are ahead of their defenses, exactly as many as say their defenses are ahead. And churn intent is the highest this series has recorded, with 74% planning to adopt, add, or replace agent security tooling within twelve months, despite satisfaction scores at a series high of 4.29 out of 5. Enterprises are more satisfied than ever with a stack they are more determined than ever to replace.MethodologyVentureBeat 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=116; the survey’s smallest size band, 1–100 employees, is excluded), drawn from a single July 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; all figures are drawn from the July fielding only. Several questions were multiple-select, so those shares can sum to more than 100%.By role the sample is senior and buyer-credible: 44% are final decision-makers for AI purchases and another 38% recommenders or influencers. Managers (36%), individual contributors (27%), VPs and directors (18%), and the C-suite (16%) make up the seniority mix. By organization size the sample is mid-market-weighted with a meaningful enterprise tail: 101–250 (34%) and 251–1,000 (23%) employees lead, with 1,001–5,000 (18%), 10,001+ (17%), and 5,001–10,000 (7%) above them. Technology/Software is the largest industry at 38%, followed by Healthcare/Life Sciences (11%) and Financial Services (10%).Three questions require a base note. Two questions were asked only of enterprises with agents live or piloting. Posture figures (observe / enforce / isolate) are reported on those 93 respondents, and primary-security-layer figures on the 92 of them who named a layer. The 23 respondents outside this base are those still evaluating, without plans, or unsure — organizations for which an agent security posture would not yet apply. And several multiple-select questions permitted overlapping answers where one was intended — identity (33 respondents selected more than one pattern), arms-race assessment (23), budget share (10), and incidents (9) — so those are computed at the respondent level and the overlap is described where it matters. Satisfaction ratings are computed on the respondents who answered each rating question; the overall satisfaction score reflects 76 of the 116 qualified respondents.At 116 respondents, the sample supports directional reads but not precise measurement; it is self-selected and is not a probability sample. It is best read as the view from organizations actively standing up agent security rather than from the largest operators.Finding 1: Agents are in production, and so are the incidentsA majority have already had an agent security eventWe asked whether organizations run agentic AI in production, and whether they had experienced an agent security incident — a confirmed breach, or a near-miss caught before harm. Finding 1 — Agents Are in Production, and So Are the Incidents 53% have agentic AI systems in production today; 27% are piloting or in limited rollout 38% have had a near-miss caught before harm 19% have had a confirmed incident 53% have had a confirmed incident or near-miss — the report’s defining number 42% report no such event; 4% don’t track this and 4% run no agents in production Agents have moved into production for this cohort. More than half of enterprises (53%) run agentic AI systems live today, another 27% are piloting or running a limited rollout, and only 3% have no plans in the next twelve months. The security exposure has scaled with the deployment: 53% of organizations have already had an agent security event, 19% a confirmed incident and 38% a near-miss caught before it caused harm.That the near-misses outnumber confirmed incidents two to one is worth reading carefully. It means enterprises are catching problems, but catching them close to the edge — and a near-miss is a control that worked once, not a control that will work every time. The controls examined in the rest of this report, particularly the identity and isolation gaps in Findings 2 and 3, are what determine whether the next near-miss stays a near-miss.One pattern from earlier waves does not replicate here. Organization size makes no reliable difference to exposure: enterprises above 1,000 employees report an incident or near-miss at 47%, against 57% among those between 101 and 1,000 — a difference well inside sample noise, and pointing the opposite direction from the size gradient this series has previously recorded. In this wave, what separates the hit from the not hit is not headcount.Finding 2: Identity is improving — and still sharedHalf give agents scoped identities; two-thirds still share credentials somewhereWe asked how enterprises manage the identity of their AI agents — whether each agent has its own credentials, or agents share them. Respondents could describe more than one pattern across the fleet. Finding 2 — Identity Is Improving — and Still Shared 49% say each agent has its own scoped, managed identity 37% say agents mostly run on shared API keys or human / service-account credentials 34% say some agents have scoped identities, but many still share credentials 63% report credential sharing somewhere in the agent fleet — 73 of 116 enterprises 29% describe a fleet with scoped identities and no sharing anywhere — the fully governed minority Per-agent identity is now the most-cited pattern: 49% of enterprises say each agent carries its own scoped, managed identity, the precondition for least-privilege access and clean attribution. That is real progress on the control this series has repeatedly identified as the structural weakness beneath agent incidents.But the answers overlap, and the overlap is the finding. Thirty-three respondents described more than one identity pattern across their fleet, and rolled together at the respondent level, 63% of enterprises report credential sharing somewhere — either agents mostly running on shared API keys and borrowed human or service-account credentials (37%), or a mixed fleet where some agents are scoped and many are not (34%). Only 29% describe a fleet with scoped identities and no sharing anywhere at all. Among enterprises with agents in production, 60% report per-agent identity, so the improvement is concentrated where the agents actually are — but so is the residual sharing.The consequence is unchanged by the improvement. Where credentials are shared, an over-permissioned or compromised agent acts with far more reach than intended, and post-incident forensics cannot cleanly establish which agent did what. Half a fleet with scoped identities still has the blast radius of the half without. Non-human identity remains the largest unfinished piece of enterprise agent security, and as Finding 8 shows, it is still almost entirely absent from what enterprises are shopping for.Finding 3: Isolation is the control nobody buildsTwo-thirds enforce at runtime; fewer than one in five sandboxWe 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 by far the least common. Figures are reported on the 93 respondents who described a posture. Finding 3 — Isolation Is the Control Nobody Builds 65% enforce — agents have scoped identities and permissions, enforced at runtime 56% observe — they monitor and log agent activity, but runtime enforcement is limited 18% isolate — high-risk agents run sandboxed, with bounded blast radius if controls fail 8% pair enforcement with isolation — the full prevent-and-contain posture 21% isolate, among enterprises actually running agents in production This is the containment gap, and it is the widest structural gap in the report. Enforcement and observation are now common — 65% enforce scoped permissions at runtime and 56% monitor and log agent activity — while isolation sits at 18%. Only 8% of enterprises run both enforcement and isolation together, the posture that both prevents and contains.Deployment maturity is a better predictor than the aggregate figures suggest. Isolation reaches 21% among enterprises with agents fully in production, compared with 13% among those still piloting — a meaningful gap that tracks maturity rather than exposure. Among enterprises that report credential sharing in the fleet, the group with the widest potential blast radius per Finding 2, isolation reaches 15%. The organizations with the most exposure are not meaningfully more likely to have built the control that bounds it.The ordering is backwards from a defense-in-depth standpoint. Observation tells you what happened after the fact. Enforcement tries to stop it. Isolation is what limits the damage when enforcement fails — and enforcement will sometimes fail, which is the entire premise of the near-misses in Finding 1. An agent fleet that is watched and permissioned but not boxed in is precisely the configuration in which a single control failure propagates across systems. Enterprises have built the first two layers of the model and largely skipped the third.Finding 4: Security still runs on borrowed, provider-native controlsNine in 10 name a model provider or hyperscaler as their primary layerWe asked which agent security tooling enterprises use, and which is their primary layer. The answer continues to favor the model providers and hyperscalers over the dedicated security vendors. Finding 4 — Security Still Runs on Borrowed, Provider-Native Controls 44% use OpenAI’s built-in guardrails; 42% Microsoft Azure (Purview / Copilot Studio DLP) 37% use Anthropic’s Claude Managed Agents controls; 31% Google Cloud controls; 17% AWS Bedrock Guardrails 11% use Cloudflare; 9% Cisco AI Defense; 8% custom in-house controls; 7% each CrowdStrike, Microsoft Entra Agent ID, and open-source guardrails 6% Palo Alto Networks; 4% each Zenity and Check Point’s Lakera; the remaining specialists sit at 3% or below 92% of enterprises naming a primary security layer name a provider-native or hyperscaler control (85 of 92) Enterprises secure agents with tools that came bundled with their models and clouds. OpenAI’s guardrails lead at 44%, followed closely by Microsoft Azure (42%), Anthropic’s managed-agent controls (37%), and Google Cloud (31%). Asked to name a single primary security layer, 92% of those who answered named one of these provider-native offerings, with Azure (27% of answerers) and Anthropic (26%) leading.The purpose-built agent-security category is no longer at zero, but it remains marginal. Cloudflare (11%) and Cisco (9%) lead the specialists, with CrowdStrike, Palo Alto, Zenity, Check Point’s Lakera, HiddenLayer, F5, and SentinelOne each between 1% and 7%. The identity specialists most directly relevant to Finding 2 are the smallest of all: Microsoft Entra Agent ID at 7%, Okta for AI Agents at 3%, and non-human identity platforms at 3%. Dedicated runtime sandboxing tooling — the control missing in Finding 3 — is in place at 3%.A note on reading these shares: As described in the methodology section, the respondent sample is self-selected, 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 is the durable part: provider-native and hyperscaler controls lead by a wide margin, and dedicated agent-security specialists remain in single digits. Read the individual shares loosely and the pattern with confidence.Finding 5: Satisfaction is at a series high — and so is churn intentEnterprises rate their tooling 4.29 of 5 and three-quarters plan to replace itWe asked how satisfied enterprises are with their current agent security tooling, and whether they plan to adopt a new, additional, or replacement solution within twelve months. The two answers do not sit comfortably together. Finding 5 — Satisfaction Is at a Series High — and So Is Churn Intent 4.29 average overall satisfaction with current agent security tooling, on a five-point scale 4.29 average ease of implementation; value for money trails at 4.11 74% nonetheless plan to adopt, add, or replace agent security tooling within twelve months 30% plan to move within the next 0–3 months; 33% within 3–6 months; 11% within 6–12 months 26% have no plans to change Satisfaction with agent security tooling is the highest this series has recorded — 4.29 out of 5 for both overall satisfaction and ease of implementation, with value for money close behind at 4.11. That is a striking set of scores for a stack that is mostly borrowed provider guardrails, given that a majority of the same enterprises have already had an incident or near-miss and fewer than one in five isolates high-risk agents.The purchase intentions tell the other half of the story. Three-quarters (74%) plan to adopt, add, or replace agent security tooling within 12 months, and 30% within the next quarter alone — higher churn intent than this series has previously seen in this category. Only 26% intend to stand pat. Enterprises are simultaneously more satisfied with their tooling and more determined to change it than at any prior reading, which suggests the satisfaction rests on the convenience and low friction of provider-native controls rather than on demonstrated containment. It is comfort with what is easy, not confidence in what is sufficient.Finding 6: Budgets are finally movingA third now spend more than a tenth of the security budget on agentsWe asked what share of the security budget enterprises allocate to securing AI agents. The allocation has grown, though it remains a modest slice. Finding 6 — Budgets Are Finally Moving 44% allocate 6–10% of their security budget to agent / AI security — the most common band 25% allocate 11–25%; a further 12% allocate more than 25% 22% allocate 1–5%; 6% under 1% 35% allocate more than a tenth of the security budget — the funded minority 28% allocate 5% or less Agent security spending is still a slice rather than a pillar, but it is a growing one. The most common allocation remains 6–10% of the security budget (44%), and roughly a third of enterprises (35%) now devote more than a tenth — a meaningful funded minority. Just over a quarter (28%) spend 5% or less.Read against Findings 1 through 3, the budget looks like a lagging but responsive indicator. A majority of enterprises have had an incident or near-miss, credential sharing persists across two-thirds of fleets, and fewer than one in five isolates high-risk agents — gaps that a 6–10% allocation is unlikely to close quickly. The enterprises spending above a tenth are the ones with the resources to build scoped identity and isolation controls rather than adopt whatever their model provider ships, and whether that minority grows is a reasonable leading indicator for whether the containment gap narrows.Finding 7: The arms race has tiltedAs many say attackers are ahead as say their defenses areWe asked how enterprises assess the balance between their AI-enabled defenses and AI-enabled attackers. Confidence has slipped into an even split. Finding 7 — The Arms Race Has Tilted 33% say the contest is roughly even 30% say their AI-enabled defenses are ahead 30% say attackers using AI are ahead of their defenses — level with the optimists 24% say it is too early to tell; 3% don’t know 63% rate the balance as even or tilted toward the attacker