Neo Raises $100M Series A to Secure Enterprise AI Agents
Boston-based Neo emerged from stealth on July 20, 2026, with $100M in total Seed and Series A funding to build what it calls an Agentic Software Control platform. The company is tackling a problem many enterprises are only beginning to recognize: AI agents are gaining the ability to act across business systems, while most organizations still lack the visibility and policy controls to govern those actions.
The round was backed by Andreessen Horowitz, Bessemer Venture Partners, Craft Ventures, and Merlin Ventures. Neo says the capital will support engineering, product development, go-to-market initiatives, and broader operating growth as enterprise adoption of agentic software accelerates.
This matters because the security model is changing underneath the enterprise stack. When software shifts from executing commands to initiating workflows, invoking tools, calling APIs, and moving data, the question is no longer only whether a device or identity is compromised. The question becomes whether an organization can understand which human, agent, application, or identity performed an action in the first place.
What Happened
Neo was founded in 2025 by cybersecurity veterans Nicholas "Nick" Warner, CEO and Co-Founder, Shlomi Salem, Co-Founder and Chief Product Officer, and Eran Shirazi, Co-Founder and CTO. The team previously helped build and scale SentinelOne before focusing on what it sees as the next major security challenge: autonomous software operating inside enterprise environments.
The company disclosed $100M in total funding across Seed and Series A financing. Andreessen Horowitz led the Seed round, Bessemer Venture Partners led the Series A, and Craft Ventures and Merlin Ventures participated. Public information also indicates a team of roughly 50 employees, including a significant development presence in Israel, giving Neo a footprint split between its Boston headquarters and technical operations abroad.
Why Neo's Technology Matters
Traditional endpoint security was designed around people interacting with software. AI agents change that assumption because they can take actions, use tools, and move through workflows with less direct human involvement. That creates a new control problem for SecOps teams that already have enough alerts, dashboards, and vendor promises to wallpaper a small airport.
Neo's platform is built around inventory, capability and risk intelligence, real-time attribution, granular policy controls, and native enforcement for AI agents, AI-enabled applications, browsers, identities, plugins, MCP servers, and traditional software gaining agentic capabilities. Instead of relying solely on API-based visibility, Neo says it uses an endpoint sensor to observe and enforce behavior in real time.
That distinction matters because autonomous software does not behave like ordinary SaaS. It can chain actions together, operate across systems, and create security exposure before a human notices the pattern. Neo is betting enterprises will need a control layer that can see the agent, understand its capabilities, attribute its actions, and stop risky behavior before the blast radius becomes a board meeting.
Why Investors Are Paying Attention
Investors are not just funding another cybersecurity company. They are funding an emerging infrastructure layer for enterprise AI, and the timing is doing much of the work. Gartner projections cited by Neo indicate enterprise applications with agentic capabilities could increase from 5% in 2025 to 40% by the end of 2026.
Bessemer also cited CyberArk research indicating that 68% of enterprises deploying AI agents have no identity controls governing those systems. Taken together, those numbers help explain why this category is becoming valuable so quickly. Enterprises are adopting agents because the productivity upside is obvious, while security teams are trying to build the governance model after the adoption curve has already started moving.
Markets rarely wait for organizations to become comfortable before moving forward. AI adoption is accelerating because the short-term gains are immediate, while the controls are still catching up. Neo's positioning lands in that uncomfortable gap between what enterprise teams are already deploying and what their security architecture can actually explain.
What This Signals for Enterprise Cybersecurity
Every major technology shift creates new infrastructure categories. Cloud computing produced cloud security. Mobile computing created mobile device management. The rise of autonomous AI agents is beginning to create a market for Agentic Software Control.
Neo's leadership provides a strong founder-market fit signal. Nicholas "Nick" Warner helped scale SentinelOne through major commercial growth and its IPO, Shlomi Salem spent more than a decade building advanced threat research capabilities, and Eran Shirazi brings product and engineering experience from EasySend and Unit 8200. Founders who have lived through previous platform transitions often recognize emerging market gaps before the rest of the industry turns them into conference panels.
The competitive landscape will not stay quiet. Endpoint security vendors, identity platforms, cloud security companies, and AI governance startups all have reasons to move toward agent control. Neo's advantage is that it is starting with the agentic software problem at the center of the product, not as a feature bolted onto an older category.
The Bigger Industry Shift
The broader lesson extends beyond Neo. Enterprise AI is moving from a productivity enhancement to an operational workforce. Organizations are increasingly deploying agents that can make decisions, initiate workflows, and interact with systems autonomously, changing cybersecurity from protecting endpoints to governing digital actors inside business environments.
Neo's funding reflects more than confidence in one startup. It reflects growing recognition that autonomous software requires a different security model. The companies building those control layers today may become foundational infrastructure providers for tomorrow's AI-powered enterprises.
The conversation is no longer whether AI agents will become part of enterprise operations. That decision has largely been made. The real question is whether security will evolve quickly enough to manage a workforce that never sleeps, never stops processing information, and increasingly acts without waiting for human instruction.
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Frequently Asked Questions
What problem is Neo trying to solve for enterprises adopting AI agents?
Neo is focused on the visibility and control gap created when AI agents can invoke tools, call APIs, move data, and execute workflows across enterprise systems. Its platform is designed to help security teams identify agentic software, understand what it can access, attribute actions, and enforce policies in real time.
Why are investors treating agentic software control as a cybersecurity category?
AI agents are moving from experimental assistants into operational software that can act inside business environments. Neo's $100M financing signals investor belief that enterprises will need a dedicated control layer for autonomous software, similar to how cloud and mobile computing created new security categories.
How does Neo's approach differ from API-only visibility tools?
Neo says its platform uses an endpoint sensor to observe and govern agent behavior in real time, rather than relying only on API-based visibility. That matters because agentic software can chain actions across browsers, applications, identities, and tools before a traditional monitoring workflow catches up.
What does Neo's funding suggest about enterprise AI security timing?
The funding suggests enterprise AI security is moving from a future planning topic into an immediate infrastructure priority. Neo and its investors points to fast growth in agentic application adoption and a gap in identity controls for deployed AI agents.
What should security teams watch as AI agent adoption grows?
Security teams should watch whether they can inventory AI agents, assign actions to the right human or software identity, understand tool and data access, and enforce policies before risky behavior spreads. The harder question is not whether agents will enter the enterprise, but whether controls can keep pace with their autonomy.