NeuralTrust named in the Gartner Emerging Tech Impact Radar 2026

TL;DR - Key Takeaways

• NeuralTrust has been named a Sample Vendor in the AI Application Security profile of the Gartner Emerging Tech Impact Radar: AI Cybersecurity Ecosystem (ID G00846953, published 5 October 2026).
• Gartner places AI application security in the 1 to 3 years ring with a High mass rating, meaning it expects the technology to reach early majority adoption within that window and to have a broad impact on the market.
• The radar maps 22 emerging technologies and trends across three themes: Securing AI (Engineering), Securing AI (Runtime) and Applied AI in Security.
• Gartner forecasts $16.4 billion in end-user spend on securing AI solutions by 2030.
• Gartner recommends evolving AI application security "beyond prompt filtering into active runtime execution protection that continuously validates agent reasoning, memory integrity, and tool calls", the architecture behind TrustGuard and TrustGate.
• This recognition builds on NeuralTrust's position as a Pioneer in the Gartner Emerging Market Quadrant for AI Application Security and four Gartner Hype Cycle 2026 recognitions. NeuralTrust raised a $20M seed round in June 2026.

AI Application Security Is a 1 to 3 Year Priority

The Gartner Emerging Tech Impact Radar asks two questions about every technology it covers: when will it cross from early adopters to the early majority (range), and how broad will its impact be on existing products and markets (mass)?

For AI application security, Gartner's answer is a range of 1 to 3 years and a mass of High.

Gartner attributes that range to three forces: "custom AI deployments and use cases are maturing, the attack surface and resource access are rapidly expanding, and new attack vectors continue to emerge with agent proliferation."

The demand signal is strong. Gartner expects enterprises deploying industry-specific AI agents in support of critical business objectives to grow to over 80% in 2030, up from less than 10% today. As worker agents and customer-facing agents gain access to more sensitive data, Gartner notes that preventing insider and external threats "will drive greater demand for in-line security solutions."

On mass, Gartner makes a point that matters to every CISO building an AI security stack: AI application security solutions "require new capabilities that are additive to the security market rather than displacing existing solutions." Firewalls, WAFs and DLP tools were not designed for this layer. Something new has to sit there.

NeuralTrust is listed as a Sample Vendor in this profile.

What Gartner Means by AI Application Security

Gartner defines AI application security solutions as those that "protect custom-built AI applications and agentic workflows by enforcing intent-based policies and continuously detecting anomalies."

According to Gartner, these solutions initially countered LLM threats such as prompt injection, data loss, toxicity and hallucinations, and have since expanded to control agent actions. Core capabilities include:

1. Asset discovery across AI applications, models and agents.
2. Security posture management for AI deployments.
3. Securing Model Context Protocol (MCP) integrations.
4. Detecting rogue agents.
5. Automated vulnerability and model testing throughout the life cycle.
6. Guardrails and risk scoring applied to runtime traffic.
7. Automated mitigations such as PII redaction, alerting and threat blocking.

Gartner also observes that buyers today tend to come from regulated, risk-averse industries such as finance, pharma, government, manufacturing and telcos, but that the need for AI application security spans every vertical.

NeuralTrust as a Sample Vendor

Gartner names Sample Vendors to illustrate representative providers in each technology profile. The Sample Vendors for AI application security in this Impact Radar are Cranium, Check Point (Lakera), DeepKeep, HiddenLayer, NeuralTrust, Noma Security, NVIDIA, Pillar Security, Onyx, PointGuard and Sun Security.

The list mixes AI-native security startups, established cybersecurity vendors and infrastructure providers, which reflects how many directions the market is being approached from.

For NeuralTrust, the recognition is consistent with its position as a Pioneer in the Gartner Emerging Market Quadrant for AI Application Security, the first Gartner framework dedicated to emerging vendors in this category.

How NeuralTrust Maps to Gartner's Definition

Gartner describes AI application security as a full life cycle discipline: discover, assess, test, then protect at runtime. NeuralTrust's Runtime Security Mesh covers each stage with a dedicated product.

1. TrustLens: Discovery, Inventory and Posture

TrustLens continuously discovers the AI applications and agents running across the enterprise, maps what data and tools they can access, and scores their risk. It answers the first question in Gartner's definition: what AI assets exist, and is their posture within policy?

2. TrustTest: Automated Vulnerability and Model Testing

TrustTest runs automated red teaming against AI applications before and after deployment, covering prompt injection, jailbreaks, data extraction and the OWASP LLM Top 10. Its findings feed directly into runtime policies, closing the testing and policy feedback loop that Gartner highlights as essential.

3. TrustGate: Runtime Inspection and MCP Security

The Agent Gateway, TrustGate, inspects every prompt and completion in line. It detects prompt injection, masks sensitive data, enforces token budgets and routing policies, and secures MCP connections with 200+ pre-built MCP servers. This is where Gartner's automated mitigations (PII redaction, alerting, threat blocking) happen.

4. TrustGuard: Agent Actions and Rogue Agent Detection

TrustGuard monitors what agents actually do: tool calls, MCP interactions and reasoning steps. It applies intent-based policies and behavioral anomaly detection to stop rogue or hijacked agents before an unauthorized action executes.

Three Themes Reshaping the AI Cybersecurity Ecosystem

The Impact Radar groups its 22 technologies and trends into three themes.

1. Securing AI (Engineering): Governance, Data Protection and Posture

Gartner warns that many organizations want to skip straight to enforcement. But AI security "will not be effective" without three core pillars: AI governance and context, information governance and data privacy, and continuous AI security posture management supported by supply chain security and AI security testing. Because models, tools, plugins and prompts change constantly, point-in-time controls are insufficient once AI is in production.

2. Securing AI (Runtime): Unified Control Across Fragmented Environments

This is the theme closest to NeuralTrust's core. Gartner states that "prompt-and-response firewalls alone are insufficient, and enforcement has shifted toward agent-execution boundaries across AI deployments." Runtime protection is currently fragmented across cloud, desktop, mobile and on-premises environments, and across network, identity, API, browser and endpoint layers. CISOs need consistent visibility and protection, not disconnected controls. Gartner also calls identity "foundational to AI runtime security."

3. Applied AI in Security: Agents Remove the Boundaries Between Tools

Security teams are deploying AI agents, agent harnesses, synthetic data and intelligent simulation to match the speed of AI-augmented attackers. Gartner expects these capabilities to remove the boundaries between disparate security tools and drastically improve efficacy.

Gartner's Strategic Planning Assumptions

The report includes Strategic Planning Assumptions that put numbers on where AI security budgets and attacks are heading:

• "Through 2029, over 50% of successful cybersecurity attacks against AI agents will exploit access control issues, using direct or indirect prompt injection as an attack vector."
• "By 2029, at least 50% of enterprise AI security budgets will prioritize in-line interception and active runtime protection over general AI visibility, up from less than 10% in 2026."
• "By 2029, more than 25% of organizations that deployed AI agents at scale will leverage guardian agent capabilities deployed as independent and deterministic supervisory entities, up from less than 2% in 2026."

The second assumption is the clearest market signal. Budgets are moving from "see what AI is doing" to "stop what AI should not do", in line. Visibility alone will not be enough.

From Prompt Filtering to Runtime Execution Protection

Among its recommended actions for AI application security, Gartner advises:

"Evolve AI application security beyond prompt filtering into active runtime execution protection that continuously validates agent reasoning, memory integrity, and tool calls. Enforcing context-aware behavioral guardrails directly at the execution layer is essential to stop goal hijacking and exploitation before unauthorized actions occur."

Gartner also positions AI security platforms (AISP) as the consolidation of AI usage control and AI application security, with a range of 3 to 6 years and a mass of Very High. Organizations, Gartner notes, need "an efficient means of defining policy tied to governance requirements and enforced across multiple enforcement points across AI and agent deployments."

That is the design principle behind the Runtime Security Mesh: one policy layer, enforced at the gateway, at the agent and across MCP connections, informed by continuous discovery and testing.

What This Means for Security Leaders

The Impact Radar is written for technology product leaders, but its conclusions are just as useful for the CISOs who buy from them:

• The timing is near term. A 1 to 3 year range means AI application security is moving from early adopter projects to mainstream budgets now.
• In-line protection beats visibility. Gartner expects runtime interception to dominate AI security budgets by 2029.
• Fragmentation is the risk. Point solutions for chatbots, agents and MCP will create policy gaps. Look for platforms that cover multiple enforcement points with a single policy model.

When scoping an evaluation, start with four questions: which AI applications and agents are running today, which can take actions or access sensitive data, how are they tested before release, and what enforces policy at runtime? TrustLens answers the first, TrustTest the third, and TrustGate and TrustGuard the last.

See how NeuralTrust secures AI applications and agents at runtime

FAQs about the Gartner Emerging Tech Impact Radar

1. What is the Gartner Emerging Tech Impact Radar: AI Cybersecurity Ecosystem?

It is a Gartner research report, published on 5 October 2026 (ID G00846953), that maps 22 emerging technologies and trends in AI security. Each one is rated on range, the time until it reaches early majority adoption, and mass, the breadth of its impact on existing products and markets. It helps technology product leaders decide where and when to invest.

2. What is AI application security according to Gartner?

Gartner defines AI application security as solutions that protect custom-built AI applications and agentic workflows by enforcing intent-based policies and continuously detecting anomalies. Core capabilities include asset discovery, posture management, MCP security, rogue agent detection, automated vulnerability and model testing, and runtime mitigations such as PII redaction, alerting and threat blocking.

3. Where does AI application security sit on the Impact Radar?

AI application security sits in the 1 to 3 years ring with a mass of High. Gartner expects it to cross from early adopters to the early majority within one to three years, driven by maturing custom AI deployments, a rapidly expanding attack surface and new attack vectors from agent proliferation.

4. How is the Impact Radar different from a Gartner Hype Cycle?

A Hype Cycle shows how expectations and maturity of technologies evolve over time. The Impact Radar estimates when each technology will reach early majority adoption and how broadly it will affect existing products and markets. Read together, they give a fuller view of the AI security market.

5. What does it mean for NeuralTrust to be named a Sample Vendor?

Gartner names Sample Vendors to illustrate representative providers of a technology. The list is not exhaustive and is not a ranking. NeuralTrust's inclusion in AI application security reflects a platform that covers discovery and posture (TrustLens), automated testing (TrustTest), in-line runtime inspection (TrustGate) and agent action control (TrustGuard).