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Vectra AI Blog

Securing AI Adoption Starts with Visibility by Aakash Gupta The Missing Data Layer Behind SIEM and SOAR Why Most SIEM/SOAR Integrations Break — and How to Fix Them Shai-Hulud Part 2: When the Worm Forged Its Own Security Certificate Improve SIEM and SOAR Workflows with Better Security Signal by Gearóid Ó Fearghaíl ShinyHunters isn’t a group. It’s a pattern. How Vectra AI Secures the AI Enterprise AI agents: the new workforce — and attack surface. by Tiffany Nip How Vectra AI Scoring Helps Security Teams Focus on What Matters First What’s Next for the Enterprise After Two GenAI Tidal Waves? If An Identity was Compromised, Would We Know? Help Over Hype: Claude Mythos, Project Glasswing and the Real Questions CISOs Want Answered Azure Logging just Changed - Your Detections May be Missing it by Alex Groyz When the Defender Becomes the Door: BlueHammer, RedSun, and UnDefend in the Wild by Justin Howe 4 Ways to Improve SOC Efficiency with AI by Jesse Kimbrel Why triage alerts - when AI can do it for you? Attackers Don’t Hack In — They Log In: The MFA Blind Spot The rise of supply chain-driven data theft in SaaS environments by Lucie Cardiet AI-Assisted Search: Clarity at the Speed of a Question What We Learned from Analyzing Millions of Alerts FortiClient EMS Zero-Day: When the Control Plane Becomes Initial Access by Lucie Cardiet Detecting Compromise After the Axios Supply Chain Attack. by Yusri Mohd Yusop Who’s Doing What on Your Network? by Mark Wojtasiak Breaking down the axios supply chain incident by Lucie Cardiet Detecting Sliver C2: When Advanced Beaconing Tries to Hide in Plain Sight Prompt Control: How Context Becomes the Command-and-Control Layer for AI Agents How Attackers Move Through Hybrid Networks After the Initial Breach How Attackers Establish Persistence in Hybrid Environments What the Stryker Incident Reveals About Handala’s Attack Playbook Why Cyber Resilience is Lagging in the AI Era 5-Minute Hunt: Six Queries to Detect Iranian APT Activity AI-Powered Attacks Are Here, But So Is AI-Powered NDR to Stop Them What is hiding in AI traffic AWS Compromised by AI Agents in Minutes The UX of Cybersecurity AI: Designing for Behavior at Machine Speed Molt Road and the Automation of Underground Marketplaces Moltbook and the Illusion of “Harmless” AI-Agent Communities From Network Detections to Understanding Risk: The Vectra AI Take on Gartner’s Redefinition of NDR From Clawdbot to OpenClaw: When Automation Becomes a Digital Backdoor by Lucie Cardiet Securing the AI Enterprise: How I’m Thinking About It as a CEO Cybersecurity Predictions 2026: AI, Agents, and SOC Defense OPSEC Failures: How Threat Actor Mistakes Help Defenders How Threat Actors Turned AI Into a Weapon CVE-2025-14847 MongoBleed in the Wild: Identifying MongoDB Exposure and Exploitation with Network Metadata by Fabien Guillot Pro-Russia Hacktivists Are Targeting Critical Infrastructure How Vectra AI Connects Network Detections to Endpoint Processes Automatically by Dale O’Grady How Vectra AI and CrowdStrike Deliver Complete Context Across Endpoint and Network by Tiffany Nip You are the Blackboard - AI Agent Assisted Bug Hunting by Kat Traxler TCP Reset Does Not Stop Modern Attacks – Here's Why Shai-Hulud: When a Supply-Chain Incident Turns Into a Worm How Typhoon APTs Infiltrate Infrastructure Without Leaving a Trace Think Your Microsoft Environment Is Resilient to Attacks? 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AI-Driven Network Detection and Response: Insights from a 2026 Gartner® Magic Quadrant™ Leader
2026-05-21 · via Vectra AI Blog

When I look at the 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR), what stands out to me isn’t just where vendors are placed, it’s how clearly the category itself is evolving.

Enterprises are changing fast. Environments are more connected, more distributed, and increasingly driven by automation and AI. Identities, human and non-human, are multiplying, activity is constant, and the network has become the place where all of it comes together. That’s the reality SOC teams are dealing with every day.

We’re proud to be recognized as a Leader in this year’s report. But what matters more to me is what Gartner is pointing toward, where this market is going, and what customers need in order to be resilient to attacks.

What I see is a shift toward platforms that can do three things well: provide continuous observability across the environment, detect, correlate, and prioritize real threats with accuracy, and help teams respond early and rapidly without adding complexity.

At Vectra AI, that’s where we’ve been focused.

We’ve spent years building an AI platform that looks at how systems, identities, and workloads behave as they move across the network. Not just collecting data but using behavioral analytics to understand it. That’s where AI comes in, not as a feature, but as the foundation. The goal is simple: reduce the noise, surface what matters, and give teams answers, actions, and outcomes they can trust. This means delivering:

  • Unified observability: Vectra AI delivers unified observability of threats and exposure risks across one networked attack surface, spanning on-premises data center, multi-cloud, identity, SaaS, IoT/OT, edge and AI infrastructure. This gives defenders a continuous, real-time view of what’s happening everywhere on the network and where risk exists.
  • Threat Detection: Vectra AI detects attacker behavior across every stage of the cyber kill chain by analyzing how activity unfolds across the network, identity and cloud. Instead of waiting for known indicators or isolated alerts, behavioral AI identifies reconnaissance, lateral movement, command-and-control, privilege abuse, and data access as they happen. This allows teams to spot attacks early by recognizing malicious behavior patterns that would otherwise remain hidden.
  • Exposure reduction: Vectra AI identifies where exposure exists and guides teams to close gaps before attackers exploit them. This includes highlighting risky access paths, excessive permissions, weak identity hygiene, and unsafe network connections allowing teams to tighten controls, reduce blast radius, and strengthen posture continuously.

These aren’t ideas we’re chasing. They’re challenges we’ve been addressing and innovating on for a long time.

The challenge: too much data, not enough clarity

One of the biggest challenges we hear from customers is not a lack of data; it’s the opposite. Too many alerts. Too many tools. Not enough clarity. Teams are forced to piece things together manually, while dealing with alert fatigue, often under time constraints, trying to understand what’s truly happening. That’s not sustainable, especially as attackers continuously evolve to outthink and evade traditional defenses at AI speed.

Our focus is to change that experience. To move from fragmented signals to a clear picture of what is going on. To help teams see how activity connects across domains. And to do it in a way that reduces time and effort.

Observability plays a big role here. Not just seeing more but seeing in a way that makes sense. Knowing what’s on the network, how it’s behaving, and where risk is building. That’s the foundation for everything else: detection, investigation, response, and even compliance.

At the same time, we’re continuing to expand how the platform supports real-world environments. That means flexible deployment options, deep integrations with the tools customers already use, and support for hybrid architectures that aren’t going away.

It also means being clear about what we do and what we don’t try to do.

We focus on detection, prioritization, and visibility. When customers need integration with their tools, we will gladly do that because we don't believe in vendor lock-in. When they need deployment support, we work with a security ecosystem that many of them already rely on. That’s how most organizations operate today using the right tools together rather than forcing everything into one system.

For the second year in a row, we have been recognized as a leader in this space. We're thrilled about that, but there's still work to do on our side as well. As we grow, we need to keep improving how we support customers, how we co-defend. How we make deployments smoother, and how we ensure teams get value quickly. That’s something we’re actively investing in across our people, our processes, and our partner network.

Looking ahead, our vision remains simple.

We will continue to invest in AI to better understand behavior across the network. We will continue to improve how we correlate activity across on-premises, multi-cloud, identity, SaaS, edge, and IoT/OT infrastructure. And we will continue to focus on reducing the gap between identification and action, so teams can take control earlier, faster, and with more confidence.

If you’re evaluating this space, I’d encourage you to look beyond categories and charts and focus on what matters day to day. Can you see what is happening across your environment? Can you tell what’s real and what’s not? Can your team act quickly when it counts? That’s what we’re here to solve.

We’re grateful for the recognition. More importantly, we’re committed to continuing the work helping security teams keep up with environments that are only getting more complex and giving them the clarity they need to do their job well. That’s Vectra AI’s promise to you.

Source: Gartner, Magic Quadrant for Network Detection and Response, Thomas Lintemuth, Charanpal Bhogal, Nahim Fazal, 18 May 2026.

Gartner and Magic Quadrant are trademarks of Gartner, Inc. and/or its affiliates.

Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner’s business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose.