How AI-native intelligence removes friction from detection, investigation, and response, turning security insight into decisive action.
Snehal, as Chief Product Officer at Vectra AI, what drew you to focus your career on advancing cybersecurity innovation, and how has that journey led to this latest breakthrough?
My career has consistently sat at the intersection of large-scale data systems, AI-driven insights, and real-world operational decision-making. Early on, I worked on building platforms where reliability, performance, and trust were non-negotiable—systems that had to operate at massive scale while still delivering clear, actionable outcomes to users.
As I moved deeper into security and data platforms, one pattern became increasingly clear: organizations were drowning in telemetry but starving for intelligence. Security teams weren’t failing because they lacked tools—they were failing because those tools didn’t translate complexity into clarity. Too much noise, too many interfaces, and too much manual effort stood between detection and action.
What ultimately drew me to Vectra AI was its uncompromising focus on signal quality and attacker behavior. Vectra wasn’t trying to bolt AI onto existing workflows—it was rethinking how defenders gain advantage by creating native Attack Signal Intelligence across network, identity, cloud, and SaaS environments. The opportunity to help scale that vision—while making it more accessible through natural language and agentic interfaces like the MCP Server—felt like a natural continuation of everything I’ve worked on throughout my career.
The launch of the Vectra AI MCP Server is a significant step. What inspired its development, and what core problem does it solve for security teams today?
We built the Vectra AI MCP Server because security teams should never be slowed down by the interface—they should be empowered by the insight. There’s a universal challenge: translating deep, contextual threat intelligence – or what we call Attack Signal Intelligence – into timely action. Traditional platforms demand toggling between tools, dashboards, and formats.
MCP Server disrupts that by delivering AI‑native security—putting the power of our platform right into the tools analysts either already use, or will be using in the not so distant future, like Claude, Cursor, VS Code, or a direct agentic interface. In a single conversational flow, they can move from detection to investigation to action—no custom connectors, no friction. Customers don’t need to ingest all of our data into a SIEM to access it as part of their core workflow. It solves the core problem of complexity, unlocking speed, accuracy, and accessibility.
Natural language access to threat intelligence is a game-changer. How do you see this shift reshaping the day-to-day role of analysts and threat hunters?
When threat hunters can ask for insights conversationally, in their native language—‘research the latest TTPs from APTs targeting our industry and search for those TTPs in our environment’—it transforms how they work. It’s no longer about learning how the tool works, but about instantaneously extracting the intelligence. That means faster triage, less context switching, and more time spent on proactive detection and advanced analysis. Analysts become empowered collaborators—fueling more strategic high value, high impact work.
Speed is everything in security. How does MCP Server accelerate the process of investigating and neutralizing threats compared to traditional methods?
With traditional methods, going from alert to resolution can take hours—or even days—spent navigating dashboards and translating data. MCP Server collapses that into minutes; you’re talking to it, it interprets the context, surfaces the timeline, visualizes the attack, and helps you take action—all in one flow. That kind of speed is not incremental—it’s transformative.
Many organizations struggle with integrating new tools into their existing environments. How does MCP Server’s “no custom integration” approach remove those barriers?
Our philosophy is simple: make advanced security usable by bringing it into your flow—not making you adapt to a vendor’s tool or workflow. MCP Server leverages the Model Context Protocol to connect AI assistants directly to the Vectra AI Platform. It works with the tools you already use, without needing new connectors, APIs, or deployment labor. It dramatically reduces friction and accelerates adoption.
AI in cybersecurity often sparks concern about complexity or lack of transparency. How do you ensure the platform delivers clarity and trust while still being powerful?
We’re committed to transparency—every prompt and every response is backed by rich contextual insight. MCP Server doesn’t just tell you something is suspicious—it shows you the visual story, the timeline, the evidentiary data as to why we prioritized it. Analysts stay in control and can verify insights at every step. That combination of explainability, visual clarity, and natural language is what makes powerful AI trustworthy.
Breaking down silos between AI tools and real-world threat intelligence is a recurring challenge. How does MCP Server specifically address this gap?
MCP Server bridges the gap by bringing Vectra AI’s Attack Signal Intelligence directly into your AI assistant—not the other way around. It collapses boundaries: detection sets, contextual insights, and dynamic investigations—all flow through a single interface. Analysts don’t grapple with multiple dashboards—they speak their query, and security intelligence responds.
From a product perspective, what does it take to design an AI system that not only detects threats but also communicates insights in a way teams can act on immediately?
Designing AI that’s not only accurate but also actionable starts with understanding analyst workflows deeply. It means harnessing conversational interfaces, dynamic visualizations, investigative timelines, and well-structured contextual metadata. Fast, intuitive, and insightful—and that’s what the MCP Server delivers by translating complex insights into fluent, interactive conversations.
Beyond faster investigations, what broader business or operational benefits do you see organizations gaining from adopting MCP Server?
Faster investigations are just the start. But I think that as MCP is broadly adopted it will change both the flow and cost of data within a Security Operations Center (SOC). It will reduce the need to pull everything into a SIEM since the SOC team is able to easily access data directly from the source via MCP. This is a ways out, but I think there will be significant opportunities to optimize ingest and storage costs.
Looking ahead, what role do you see natural language and conversational AI playing in the future of cybersecurity, and how is Vectra AI preparing for that evolution?
The future is conversational security—where AI agents become intuitive partners, plugged into our data, responding in context, and guiding actions. At Vectra AI, we’re committed to leading that evolution, building deeper integrations, expanding conversational use cases, and enhancing visual and contextual intelligence—so that security isn’t just faster, it’s smarter.
A quote or advice from the author: The future of cybersecurity is proactive protection—where accurate signal, deep context, and decisive action come together to stop attacks before they escalate. At Vectra AI, our goal is to deliver high-fidelity Attack Signal Intelligence that not only accelerates detection, investigation, and response, but also empowers security teams to proactively protect hybrid and multi-cloud environments with insight that is actionable and embedded directly into their workflow.
Snehal Patel
Chief Product Officer, Vectra AI
Snehal Patel is Chief Product Officer of Vectra AI, bringing deep technical and AI-product expertise shaped by leadership roles at Google, Cisco, McKinsey, and Boeing. He leads Product Management, Engineering, Data Science, Managed Detection and Response (MDR), and Technical Marketing Engineering, with a focus on scaling Vectra’s AI-powered protection and threat detection and response platform a and cohesive product experiences across cloud, identity, and network. Prior to Vectra AI, Snehal led Product Management for Google Kubernetes Engine (GKE), helping enterprises securely run mission-critical workloads across hybrid cloud and accelerating developer productivity. While at Cisco, he served as Vice President of Product for Security Platform & Response, where he advanced one of the industry’s first XDR platforms and drove sustained growth across the endpoint and security analytics portfolio. Earlier, Snehal was an Associate Principal at McKinsey & Company advising high-tech and aerospace leaders on product strategy and operational performance, and he began his career at Boeing leading engineering programs supporting secure communications for the U.S. Department of Defense. Snehal holds an MBA from UCLA Anderson, an MS in Electrical Engineering from UC Irvine, and a BS in Electrical Engineering from the Georgia Institute of Technology