エンタープライズ、エージェント権限管理の2/3が実行中だが高リスク隔離は5%未満

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.

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=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 incidents

A majority have already had an agent security event

We 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.

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 shared

Half give agents scoped identities; two-thirds still share credentials somewhere

We 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.

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 builds

Two-thirds enforce at runtime; fewer than one in five sandbox

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 by far the least common. Figures are reported on the 93 respondents who described a posture.

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 controls

Nine in 10 name a model provider or hyperscaler as their primary layer

We 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.

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 intent

Enterprises rate their tooling 4.29 of 5 and three-quarters plan to replace it

We 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.

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 moving

A third now spend more than a tenth of the security budget on agents

We asked what share of the security budget enterprises allocate to securing AI agents. The allocation has grown, though it remains a modest slice.

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 tilted

As many say attackers are ahead as say their defenses are

We asked how enterprises assess the balance between their AI-enabled defenses and AI-enabled attackers. Confidence has slipped into an even split.

Enterprises are no longer net-optimistic about the contest. Exactly as many say AI-armed attackers are ahead of their defenses (30%) as say their defenses are ahead (30%), with another 33% calling it roughly even and 24% saying it is too early to tell. Taken together, 63% rate the balance as even or worse.

Experience is what drives the pessimism, and the relationship is statistically clear. Among enterprises that have had a confirmed incident or near-miss, 39% say attackers are ahead; among those that have not, 20% do — a gap large enough to be unlikely to arise by chance in a sample this size. Getting hit does not just change what enterprises buy; it changes how they read the contest. The organizations closest to the actual threat are the least confident about it.

That assessment sits uneasily beside the series-high satisfaction of Finding 5. Enterprises rate their tooling 4.29 out of 5 while a clear majority believe it is, at best, holding even against an adversary that is also compounding with AI. An even race is not a comfortable place to be, and the group that has actually been tested rates it worse than even.

Finding 8: A reshuffle is coming — but identity still isn’t on the list

Incidents drive urgency; the control they implicate draws 10% interest

We asked which agent security solutions enterprises are considering. The consideration set has broadened, but not in the direction the incident data points.

Incidents start the buying cycle. Among organizations that have had a confirmed incident or near-miss, 38% plan to adopt, add, or replace agent security tooling within the next ninety days, against 22% of organizations with no incident; after a confirmed incident specifically the figure reaches 41%. Experience remains the strongest predictor of urgency in this data, as it is of pessimism in Finding 7.

The consideration set still leans provider-native — OpenAI (38%), Microsoft Azure (37%), Anthropic (35%), and Google Cloud (28%) lead — though the dedicated security vendors now draw meaningful early interest: Cisco (10%), Cloudflare (9%), Zenity and CrowdStrike (8% each), and Palo Alto, Check Point’s Lakera, and open-source guardrails (6% each). For most of the specialists that is more forward interest than current footprint.

What the shopping still does not include is the identity layer. Just 10% of enterprises include an agent-identity product — Okta for AI Agents, Microsoft Entra Agent ID, or a non-human identity platform — anywhere in their consideration set. Among the enterprises that both share credentials and have already been hit, the group with the most direct evidence that the control matters, identity consideration is no higher: roughly one in ten. Runtime sandboxing tooling draws 6%. The two controls most directly implicated by the incident data, identity and isolation, are the two least present in the purchase plans — the same blind spot this series recorded in the prior wave, unchanged despite a year of incidents.

The bottom line: A security gap that prevention alone won’t close

Organizations with more than 100 employees have put agents into production — 53% run them live today — and the incidents have arrived alongside them, with a majority already reporting a confirmed event or near-miss. On the controls, the picture is genuinely mixed rather than uniformly poor: nearly half now give each agent its own scoped identity, two-thirds enforce permissions at runtime, and a third devote more than a tenth of the security budget to agents. Enterprises are building agent security in earnest.

What they are not building is containment. Fewer than one in five isolates high-risk agents, only 8% pair enforcement with isolation, and among enterprises running agents in production isolation reaches just 21%. Credential sharing persists across 63% of fleets, so the blast radius that isolation would bound remains wide. The stac