Neural
The 2025 State of AI Security Report synthesizes findings from NeuralVault's analysis of over 3,200 model deployments across enterprise customers in financial services, healthcare, technology, and government sectors. It documents the adversarial attack landscape as it actually exists in production environments — not as it appears in academic research — and maps the gap between the security practices organizations report having and the security outcomes they are actually achieving.
The headline finding is not surprising to anyone who has worked in this field: the gap between AI security investment and AI security outcomes is widening. More organizations are deploying AI to production. Fewer of them have the monitoring infrastructure to know when something goes wrong. The result is an expanding population of production AI systems operating with uncharacterized security properties, serving users who have no visibility into that uncertainty.
Key Findings: The Detection Gap
The most significant finding of the 2025 report concerns detection latency. Across the organizations in our dataset, the median time from model degradation onset to engineering team awareness was 17 days for organizations without continuous monitoring. For organizations with continuous behavioral monitoring in place, the same metric was under 90 seconds.
That is not a marginal difference. It is a 14,000x difference in the time window during which degraded, manipulated, or adversarially influenced model outputs reach users without intervention. The business consequences of that gap — user trust erosion, compliance exposure, and in regulated industries potential regulatory action — compound over every day the detection window stays open.
Seventeen days is the median. The distribution has a long tail. In 12% of cases in our dataset, behavioral anomalies in production models went undetected for more than 60 days. In several cases involving fine-tuned models deployed for internal enterprise applications, the anomaly was identified only when a downstream system failure prompted a retrospective investigation.
The Attack Landscape in 2025
The adversarial attack landscape against production AI systems evolved significantly in 2025 in ways that have operational implications for security teams.
Prompt injection industrialization. Prompt injection attacks moved from researcher-demonstrated proof-of-concepts to systematized attack toolkits in 2025. Automated scanning tools that test deployed LLM endpoints against hundreds of injection variants are now publicly available. The barrier to entry for running a systematic injection campaign against an externally deployed model is no longer expert research knowledge — it is the ability to run a command-line tool.
Indirect injection through the RAG pipeline. Retrieval-augmented generation architectures became the dominant deployment pattern for enterprise LLMs in 2025. The attack surface that comes with them — adversarial content injected into retrieved documents — became correspondingly more significant. In 2025, indirect injection through RAG pipelines overtook direct prompt injection as the most common attack vector in our dataset.
Multimodal attack surface expansion. As organizations deployed multimodal models capable of processing images, audio, and documents alongside text, the adversarial attack surface expanded accordingly. Visual adversarial examples — images modified to elicit specific model behaviors — emerged as a practical attack vector against deployed vision-language models.
Model inversion professionalization. Membership inference and model inversion techniques, previously requiring significant machine learning expertise to execute, became accessible through packaged tools. Organizations with models fine-tuned on proprietary or regulated datasets faced a materially higher data extraction risk in 2025 than in prior years.
Security Practice Gaps
The report documents significant gaps between the security controls organizations report having and the controls that are actually functioning as intended.
68% of organizations in our dataset reported having behavioral monitoring for their production AI systems. Of those, only 31% had monitoring configured with detection thresholds calibrated to their specific model's baseline behavior. The remaining 69% had monitoring infrastructure installed but not operationalized — equivalent to having a smoke detector with no battery.
The compliance documentation gap is equally pronounced. 74% of organizations deploying high-risk AI systems under the EU AI Act classification cited maintaining compliant technical documentation as a significant operational challenge. The most common failure mode was not the absence of documentation at deployment time but the absence of processes for keeping documentation current as models and their operational contexts evolved after deployment.
Access control misconfiguration was the third major gap category. In organizations with more than five deployed model endpoints, 44% had at least one endpoint accessible to API consumers with broader permissions than the model's intended use required. These over-privileged consumers represent latent risk — not an active threat in most cases, but a vulnerability that an attacker with API access could exploit.
What High-Performing Organizations Do Differently
The report identifies a distinct cluster of high-performing organizations — defined as those achieving detection latency under five minutes and maintaining compliance documentation in current state — and analyzes what distinguishes their practices from the median.
Three factors consistently differentiate high performers. First, they treat model security as a continuous operational function, not a deployment gate. Security assessment does not end at deployment; it runs continuously through the model's operational lifetime. Second, they have invested in automated evidence generation that produces compliance documentation as a byproduct of normal operations, rather than treating documentation as a separate work product. Third, they have assigned named ownership for AI security outcomes at the model level — someone who owns the risk register, the monitoring configuration, and the compliance evidence package for each deployed system.
None of these practices require exotic technology. They require organizational commitment to treating AI security as a first-class operational concern rather than a compliance checkbox exercised periodically.
The 2026 Outlook
Several trends visible in 2025 data will intensify in 2026. Regulatory enforcement of the EU AI Act will begin in earnest for high-risk AI systems, creating concrete compliance consequences for organizations that have delayed building compliant governance processes. The professionalization of adversarial attack toolkits will continue, raising the baseline threat level for all externally deployed model endpoints. And the continued expansion of AI deployments in regulated industries will make the combination of high attack surface and inadequate monitoring infrastructure increasingly difficult to sustain without incident.
The organizations that close the detection gap now — before a significant incident forces the investment — will be in a fundamentally different position than those that are reactive. The data in this report is intended to make that case concrete: the security gap is measurable, the consequences are quantifiable, and the remediation is available. The question for 2026 is which side of the 17-day median your organization is on.
