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Gartner has named NeuralTrust a Sample Vendor in AI Runtime Defense in the inaugural Hype Cycle for AI-Driven Cybersecurity 2026, placing the category at the Peak of Inflated Expectations with a High benefit rating and 5-20% market penetration.
On 12 October 2026, Gartner published two landmark Hype Cycle reports simultaneously. One (the Hype Cycle for Secure AI) maps the technologies that protect AI systems. The other, the Gartner Hype Cycle for AI-Driven Cybersecurity, 2026, maps the inverse: the AI technologies and strategies that strengthen the cybersecurity program itself.
It is the first edition of this report, authored by Jeremy D'Hoinne, Craig Porter, Josh Murphy, and Christine Lee. And in it, Gartner named NeuralTrust a Sample Vendor in AI Runtime Defense, the same category the firm recognises in both reports, now appearing at the Peak of Inflated Expectations with a High benefit rating, 5-20% market penetration, and Emerging maturity.
The fact that AI Runtime Defense appears prominently in a Hype Cycle built around using AI for cybersecurity is not coincidental. It is the structural logic: before you can use AI to defend your organisation, you need controls in place to ensure that AI behaves as intended.
TL;DR: Key Takeaways
- Gartner published the first-ever Hype Cycle for AI-Driven Cybersecurity on 12 October 2026, authored by Jeremy D'Hoinne, Craig Porter, Josh Murphy, and Christine Lee (G00854093)
- 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
- 58% of cybersecurity leaders say generative AI delivers significant innovation; 72% say current AI security products are too expensive relative to the value they deliver
- 63% of organisations are evaluating in-house development of AI cybersecurity tools rather than buying from vendors
- Gartner projects that by 2030, 80% of large enterprises will rely on custom-built AI cybersecurity tools to improve their security posture
- TrustGuard provides the runtime enforcement and observability foundation that makes AI-driven security programs operationally trustworthy
The First Hype Cycle Built for AI-Driven Security Programs
The distinction between this report and its companion is important. The Hype Cycle for Secure AI asks: how do you protect AI systems? The Hype Cycle for AI-Driven Cybersecurity asks: how do you use AI to make your security program more effective?
This second question is the one most CISOs are actively wrestling with today. The promise of AI in cybersecurity: faster threat detection, better signal-to-noise in the SOC, more scalable vulnerability management... is real. So is the frustration. 58% of cybersecurity leaders say generative AI delivers significant innovation, but 72% say the products currently on the market are too expensive relative to the value they deliver.
That tension, the genuine capability, uncertain economics, and high expectations, is exactly what the Hype Cycle format was designed to represent. And AI Runtime Defense sits at the Peak of Inflated Expectations within it.
Why AI Runtime Defense Belongs in a Cybersecurity Hype Cycle
The placement of AI Runtime Defense in a Hype Cycle about using AI for security programs reveals something important about the current state of the market.
Organisations are deploying AI in their security operations: AI-assisted threat detection, AI in vulnerability prioritisation, AI-augmented analyst workflows. But these AI deployments are themselves software systems with a new attack surface. A threat detection model that can be manipulated through adversarial inputs, a security chatbot that can be prompted to reveal sensitive configuration data, an AI analyst assistant that responds to indirect prompt injection in email content: these are not theoretical risks. They are the direct consequence of deploying AI without the controls that AI Runtime Defense provides.
Gartner's decision to include AI Runtime Defense in this Hype Cycle reflects that reality. You cannot run a credible AI-driven cybersecurity program without protecting the AI that drives it.
What Is AI Runtime Defense?
AI Runtime Defense is the technology category covering security controls that monitor and protect AI applications and models during live operation.
Where traditional security tools inspect code, configuration, and network traffic, AI Runtime Defense operates at the semantic layer, the content flowing between users, models, tools, and data sources. It addresses threats that only materialise when a model is deployed: prompt injection through user-provided inputs, indirect injection through retrieved documents or tool outputs, sensitive data exfiltration through natural language, and agent behaviour that violates the intended scope of a deployment.
The core capabilities in this category include:
- Intent-based policy enforcement that defines what a model is permitted to do and blocks requests or outputs that fall outside that scope.
- Anomaly detection that identifies statistical deviations in model input or output patterns that may indicate active exploitation.
- Input and output filtering that inspects prompts and completions for policy violations, sensitive data, harmful content, or off-topic responses before they reach users or downstream systems.
- Observability and audit trails that provide the visibility required for compliance, forensics, and continuous security improvement.
Gartner rates AI Runtime Defense as High benefit with Emerging maturity and 5-20% market penetration in both the Hype Cycle for AI-Driven Cybersecurity and the companion Hype Cycle for Secure AI published on the same date.
What Being Named a Sample Vendor Means
NeuralTrust is one of ten Sample Vendors listed by Gartner in the AI Runtime Defense category across both reports. The full list includes Akto, Check Point Software Technologies, F5, HiddenLayer, Lasso, Noma Security, Pillar Security, SentinelOne, and TrojAI.
Sample Vendor listings in Gartner Hype Cycle reports result from analyst research, not vendor submissions. They indicate that Gartner's analysts have identified an organisation as an active, representative participant in the category at the time of publication. In the inaugural year of a new Hype Cycle, that recognition reflects the analyst team's assessment of the vendors shaping the category from the start.
How NeuralTrust Maps to AI-Driven Cybersecurity
NeuralTrust's platform provides the security infrastructure that organisations need to deploy AI in their security programs with confidence. Each product addresses a different layer of the lifecycle:
TrustGuard provides runtime enforcement at the point of inference. It monitors both inputs and outputs in real time, enforcing intent-based policies that define acceptable model behaviour. It detects prompt injection, sensitive data leakage, off-topic responses, and violations of custom guardrails, and takes automated action based on configured response policies.
TrustGate operates at the AI gateway level, controlling which models, tools, and external services are accessible to AI agents, and enforcing usage policies before requests reach models. For security operations teams deploying AI in SOC workflows, this provides the network-level governance layer that enterprise policy requires.
TrustLens delivers continuous observability of AI agent behaviour: flagging anomalies, tracking policy compliance across sessions and deployments, and generating the audit records needed for security team oversight and regulatory reporting.
TrustTest provides the preproduction adversarial testing capability that Gartner's research identifies as an increasingly essential control: AI red teaming that validates model robustness before deployment reduces the incident risk that Gartner now quantifies through its strategic planning assumptions.
Strategic Planning Assumptions Worth Building Into Your Roadmap
Gartner's Hype Cycle for AI-Driven Cybersecurity includes three forward-looking planning assumptions that carry direct implications for how security leaders should invest and plan:
By 2029, one in five material cybersecurity incidents will result from C-level executives shifting funds from essential cybersecurity controls to AI-driven initiatives.
This is a caution about misallocation, not about AI investment in general. The risk is in treating AI as a replacement for foundational controls rather than an augmentation of them. Security leaders who allow AI enthusiasm at the board level to erode investment in basic controls (identity, endpoint, network segmentation) will accumulate incident risk that will materialise in the next three years.
By 2028, organisations that adopt six-month AI roadmap reviews will secure 25% more cybersecurity funding and double the project success rates of those using traditional three-year planning cycles.
This assumption has immediate operational implications. AI capabilities, threats, and vendor landscapes are evolving too quickly for annual planning cycles to track them adequately. The organisations that build in structured six-month reviews (reassessing tooling, threat models, and AI capabilities on a consistent cadence) will have a measurable funding and delivery advantage over those that do not.
By 2030, custom-built AI cybersecurity tools will play a critical role in helping 80% of large enterprises improve their security posture.
This is a long-term structural signal. The 63% of organisations currently evaluating in-house AI development are responding to the same tension the report quantifies: the tools on the market are expensive and their value delivery is uncertain. The organisations that begin building internal AI security capabilities now will be 3-4 years ahead of those who wait for the commercial market to mature.
What This Means for CISOs in 2026 and 2027
The Hype Cycle for AI-Driven Cybersecurity frames a practical decision that every security leader faces: how much of your security program's improvement should come from AI, and how do you govern the AI you deploy in pursuit of that improvement?
Several priorities emerge from this report:
- Protect the AI you are already running. Before expanding your AI-driven security program, audit the AI currently in your environment. Runtime defense coverage for AI already in production is a prerequisite for expanding AI use responsibly.
- Build six-month review cycles into your AI roadmap. The planning cadence assumption is actionable now. Restructuring how you review AI investments is an internal process change that does not require vendor selection, procurement, or budget approval.
- Separate essential controls from AI investment. The 2029 incident projection is a direct warning about a specific failure mode: C-level enthusiasm for AI shifting funds away from foundational security controls. Security leaders who can clearly articulate which controls are foundational and non-negotiable will be better positioned to protect them.
- Start building internal AI capability now. The 2030 projection suggests the advantage will accrue to organisations that begin developing internal AI expertise in 2025 and 2026, not those who wait for commercial tools to improve.
FAQs about the Gartner Hype Cycle for AI-Driven Cybersecurity
1. What is the Gartner Hype Cycle for AI-Driven Cybersecurity, 2026?
The Gartner Hype Cycle for AI-Driven Cybersecurity 2026, is the first edition of a new annual research report mapping the technologies and strategies that enable organisations to use AI to improve their cybersecurity programs. Published on 12 October 2026 and authored by Jeremy D'Hoinne, Craig Porter, Josh Murphy, and Christine Lee, it is a companion report to the Hype Cycle for Secure AI published on the same date.
2. How is this report different from the Hype Cycle for Secure AI?
The Hype Cycle for Secure AI addresses the question of how to protect AI systems. The Hype Cycle for AI-Driven Cybersecurity addresses how to use AI to improve security programs: AI-assisted threat detection, AI in SOC operations, AI-driven vulnerability management. AI Runtime Defense appears in both because it is a prerequisite for both: you cannot use AI reliably for security without also securing the AI itself.
3. What does it mean for NeuralTrust to be listed as a Sample Vendor?
Sample Vendor listings result from Gartner analyst research into active participants in a technology category. Being listed as a Sample Vendor in AI Runtime Defense in the inaugural edition of this Hype Cycle confirms that Gartner's analysts recognise NeuralTrust as a relevant and active participant in this market at the moment of highest category visibility.
4. Why are 72% of cybersecurity leaders dissatisfied with current AI security product value?
The 72% figure reflects a market dynamic that is common at the Peak of Inflated Expectations: significant investment has occurred, but real-world deployment experience has revealed gaps between marketed capabilities and actual operational value. It is a signal for buyers to evaluate vendors rigorously against their specific use cases rather than on feature lists alone.
5. How does NeuralTrust help organisations build AI-driven security programs?
NeuralTrust provides the runtime enforcement and observability infrastructure that makes AI deployments in security programs operationally trustworthy. TrustGuard enforces policies at the point of inference, TrustGate controls model access at the gateway level, TrustLens provides continuous agent observability, and TrustTest validates model robustness before deployment, covering the full lifecycle that a responsible AI-driven security program requires.
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 AI-Driven Cybersecurity, 2026, Jeremy D'Hoinne, Craig Porter, Josh Murphy, Christine Lee, 12 October 2026.
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