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Gartner has named NeuralTrust a Sample Vendor in AI Runtime Defense in the inaugural Hype Cycle for Secure AI, 2026, placing the category at the Peak of Inflated Expectations with a High benefit rating and 5-20% market penetration.
For the first time in Gartner's research history, there is now a dedicated Hype Cycle that answers a single question: how do you secure AI?
The Gartner Hype Cycle for Secure AI, 2026 separates the challenge of protecting AI systems from the broader cybersecurity landscape and treats it as a domain in its own right. Published on 12 October 2026 by analysts Jeremy D'Hoinne, Dionisio Zumerle, and Jeff Arias, it maps the maturity and business impact of the technologies organisations need to deploy AI responsibly.
Within that domain, Gartner named NeuralTrust a Sample Vendor in AI Runtime Defense, the category the firm positions at the Peak of Inflated Expectations.
TL;DR: Key Takeaways
- Gartner published the first-ever Hype Cycle for Secure AI on 12 October 2026, authored by Jeremy D'Hoinne, Dionisio Zumerle, and Jeff Arias (G00846820)
- NeuralTrust is listed as a Sample Vendor in AI Runtime Defense, one of ten vendors in a category rated High benefit, 5-20% market penetration, and Emerging maturity
- AI Runtime Defense is positioned at the Peak of Inflated Expectations, the point of maximum visibility and investment concentration
- 33% of organisations have already deployed customer-facing GenAI agents in production; 50% of CISOs say they are uncertain about the long-term costs of securing those deployments
- Gartner projects that by 2028, organisations skipping preproduction offensive testing will face twice as many cybersecurity incidents as proactive organisations
- TrustGuard provides the real-time policy enforcement, anomaly detection, and observability that AI Runtime Defense requires
The First Hype Cycle Built Entirely for Securing AI
The creation of a standalone Hype Cycle for Secure AI is itself a signal about how the market has changed. For years, AI security technologies were absorbed into broader application security or cloud security frameworks. Their emergence as a dedicated research category reflects how fundamentally the risk surface has shifted with the production deployment of generative AI.
Analysts D'Hoinne, Zumerle, and Arias studied how organisations are responding to the accelerating deployment of AI in live environments. What they found is a market simultaneously racing ahead and falling behind: 33% of organisations have already deployed customer-facing GenAI agents in production, yet 50% of CISOs say they are not confident about the long-term costs of securing those deployments. That gap between deployment velocity and security readiness is the territory this Hype Cycle maps.
47% of security organisations have already diverted budget toward AI runtime defense, which means this is not a speculative investment category. Security teams are spending money today. The question is whether that spending is producing the right outcomes.
What Is AI Runtime Defense?
AI Runtime Defense is Gartner's term for the security controls that operate after a model is deployed in the production environment, on live traffic, in real time.
Traditional application security focused on code vulnerabilities. AI introduces a different class of risk: adversarial inputs designed to manipulate model behaviour, sensitive data exfiltration through natural language interfaces, prompt injection embedded in user-provided content, and AI agent chains that behave unpredictably when given access to external tools or data sources.
AI Runtime Defense addresses these threats through a combination of capabilities:
Intent-based policy enforcement defines what a model is allowed to do and blocks deviations from that definition at the point of inference.
Anomaly detection identifies unusual patterns in model inputs and outputs that may indicate an active attack or unexpected behaviour.
Input and output filtering inspects prompts and completions for policy violations, sensitive data exposure, harmful content, and off-topic responses.
Observability and audit trails create the record needed for incident forensics, compliance reporting, and continuous security improvement.
Gartner rates this category as High benefit, with 5-20% market penetration and Emerging maturity. It sits at the Peak of Inflated Expectations in the 2026 report, the moment when investment is highest, expectations sometimes run ahead of delivery, and the vendors capable of consistent real-world performance begin to separate from those that cannot.
What Being Named a Sample Vendor Means
Gartner's Sample Vendor listings are the result of analyst research, not applications or submissions. They represent organisations that Gartner's analysts identify as active, representative participants in a technology category at the time of publication.
Being named a Sample Vendor in AI Runtime Defense confirms that Gartner considers NeuralTrust a relevant and active participant in this market segment. It is analyst-level recognition at a category moment that matters: Peak of Inflated Expectations is where the most important buying decisions happen, and where scrutiny of vendor capability is at its highest.
It is also worth noting the context: this is the inaugural edition of this Hype Cycle. Being listed in the first year of a category's dedicated research coverage means NeuralTrust has been tracked as a participant since the category's formal establishment.
How NeuralTrust Covers AI Runtime Defense
NeuralTrust's platform was built around the production AI use case. Where legacy security tools inspect code or network packets, NeuralTrust inspects the semantic layer: the content flowing between users, models, tools, and external data sources.
TrustGuard is NeuralTrust's AI Runtime Defense product. It enforces policies at the point of inference, analyzing both inputs and outputs in real time. It detects prompt injection attempts, sensitive data leakage, off-topic model behaviour, and violations of custom guardrails defined by the organisation's own policies. When a violation is detected, TrustGuard can block, redact, alert, or log depending on the configured response policy.
TrustGate extends the runtime layer to the AI gateway, giving security teams control over which models, tools, and external APIs are accessible to AI agents, and enforcing usage policies at the infrastructure level before requests reach models.
TrustLens provides the observability layer: continuous monitoring of AI agent behaviour, policy compliance tracking, anomaly signals, and the audit trails that enterprise security and compliance teams require.
TrustTest addresses the preproduction dimension: the AI red teaming and adversarial testing capability that Gartner identifies as an essential control before AI systems go live.
Together, these products cover the security lifecycle that AI Runtime Defense demands: from preproduction adversarial testing through live runtime enforcement to post-incident analysis.
Strategic Planning Assumptions Worth Acting On
Gartner's Hype Cycle reports include forward-looking planning assumptions that organisations should factor into their security roadmaps. Two from this report carry direct operational weight:
By 2028, organisations that skip preproduction offensive testing on generative AI deployments will face twice as many cybersecurity incidents as those that test proactively.
This is a concrete, time-bound projection. It frames AI red teaming not as a precautionary measure but as an operational risk management decision with a quantifiable cost differential. Organisations delaying investment in preproduction testing are accumulating a measurable incident risk that will materialise within the next two years.
Through 2027, the costs to enterprises from task-driven AI agent abuses will be at least four times higher than those from multi-agent systems.
This finding reframes where the near-term risk actually lies. The assumption is counterintuitive: it is not the most architecturally complex agentic deployments that will generate the highest costs in the near term, but the simpler task-driven agents already running in production today. The implication is that the threat is more immediate and more widespread than many security teams currently assume.
What This Means for CISOs in 2026 and 2027
The creation of a standalone Hype Cycle for Secure AI sends a clear structural message: AI is now a domain with its own risk model, its own threat actors, and its own required controls. Folding AI security into existing application security programs is no longer adequate as a strategic posture.
For CISOs building or updating their AI security roadmap, several priorities emerge from this report:
- Start with a deployment audit. With a third of organisations already running customer-facing GenAI agents, the probability that production AI exists in your environment without runtime defense coverage is high. Visibility is the prerequisite for everything else.
- Treat preproduction testing as a risk control, not a process step. The 2028 projection quantifies the incident differential. AI red teaming before deployment is the kind of investment whose value is only clear after an incident makes the comparison for you.
- Prioritise task-driven agents, not just frontier architectures. The 4x cost projection for task-driven abuses is a reminder that your near-term exposure is concentrated in simpler, more prevalent deployments, not in the sophisticated multi-agent architectures that attract the most attention.
- Build for lifecycle coverage. Runtime defense requires controls at every stage from development through production operation. A policy engine without observability, or testing without runtime enforcement, creates gaps that adversaries will find.
FAQs about the Gartner Hype Cycle for Secure AI
1. What is the Gartner Hype Cycle for Secure AI 2026?
The Gartner Hype Cycle for Secure AI, 2026 (G00846820) is the first edition of a new annual research report dedicated to the technologies that protect AI systems. Published on 12 October 2026 and authored by Jeremy D'Hoinne, Dionisio Zumerle, and Jeff Arias, it maps the maturity, adoption trajectory, and business benefit of categories ranging from AI Runtime Defense to agentic AI security tools.
2. What does it mean for NeuralTrust to be listed as a Sample Vendor?
Sample Vendor listings in Gartner Hype Cycle reports reflect analyst research into active participants in a technology category. Being listed as a Sample Vendor in AI Runtime Defense confirms that Gartner recognises NeuralTrust as a relevant, operating participant in this segment. It is not an endorsement or a competitive ranking -- it is analyst-level acknowledgment of market presence at a moment of significant category attention.
3. Why does being at the Peak of Inflated Expectations matter?
The Peak of Inflated Expectations is the point in a technology's lifecycle when visibility and investment are highest. For security teams evaluating vendors in this category, the Peak is the most critical moment to conduct rigorous evaluation -- because the field is at its largest, marketing noise is highest, and the capability differentiators between vendors have the most strategic consequence.
4. What is AI Runtime Defense?
AI Runtime Defense is the technology category covering security controls that protect AI applications and models during live operation. It includes intent-based policy enforcement, anomaly detection, input and output filtering, and the observability capabilities required for compliance and incident response. Gartner rates it as High benefit with Emerging maturity in this 2026 report.
5. How does TrustGuard address AI Runtime Defense?
TrustGuard enforces policies at the point of inference, inspecting both inputs and outputs in real time for prompt injection, data leakage, off-topic behaviour, and custom guardrail violations. It works alongside TrustGate for gateway-level control, TrustLens for agent observability, and TrustTest for preproduction adversarial testing, covering the full AI security lifecycle that Gartner's AI Runtime Defense category describes.
About NeuralTrust
NeuralTrust is an AI security company built to address the security challenges that emerge when organisations deploy large language models and AI agents in production. The platform covers runtime defense, gateway enforcement, agent posture management, and adversarial red teaming. NeuralTrust works with enterprise security teams across financial services, healthcare, retail, and technology sectors.
Gartner, Hype Cycle for Secure AI, 2026, Jeremy D'Hoinne, Dionisio Zumerle, Jeff Arias, 12 October 2026.
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