
OX Security Becomes First Prompt-to-Runtime Security Platform, Leading the Evolving AINAPP Category
OX Security's platform is the first to govern the full agentic development lifecycle — from prompt to runtime — spanning AI governance, code security, cloud enforcement, and agentic pentesting in a single platform.
NEW YORK, July 28, 2026 /PRNewswire/ -- OX Security today announced that it has become the first security platform to govern the full agentic development lifecycle from prompt to runtime, establishing itself as first in the evolving AI-Native Application Protection Platform (AINAPP) category.
In the "Mythos Age," autonomous agents are increasingly writing code, provisioning infrastructure, and executing workflows with limited human review. Traditional security tools — including CNAPP (Cloud-Native Application Protection Platform), AppSec, and vulnerability assessment platforms — were built to secure infrastructure and human-written code, not to govern decisions made by an autonomous agent in real time.
OX Security's platform is built to close that gap. It governs AI users and agents at the point of the prompt, tracks every active agent and MCP (Model Context Protocol) connection, validates decisions before code is written, enforces policy at runtime, and traces each finding back to the decision that caused it.
"Since our last fundraising round, every decision our team has made has been about one thing: securing autonomous systems. We saw the shift coming — agents writing code without human gatekeeping — and built the entire platform around that reality," says Neatsun Ziv, CEO and co-founder of OX Security.
"Legacy tools ask how to find vulnerabilities faster, or better — questions that assume a human wrote the code and another human will have time to review and fix the findings. AINAPP asks the questions enterprises actually need answered now: Who's writing our code, with what tools? What is it doing? Can we trust it? Governance at the prompt, visibility across the stack, enforcement at runtime — that's what it takes to build security for the Mythos Age and beyond," says Ziv.
OX AINAPP Platform: Four Components, One System
OX Security's platform spans the full agentic development lifecycle through four connected components:
- VibeSec — AI governance and visibility for agentic development. Tracks which AI users and agents are active, what permissions they hold, and what data they can reach. Operates natively inside tools developers already use, including Copilot, Cursor, and Claude Code, preventing risky decisions before code is written.
- OX Code — Validates code security, provides supply chain security along with traditional code scanning before deployment.
- OX Cloud — Combines CNAPP, runtime security, and incident response, closing the loop between a development-time decision and its outcome in production.
- Agentic Pentester — White-box, code-aware automated penetration testing for agentic systems, providing continuous proof of exploitability rather than a point-in-time scan.
All four components share a single context lake, allowing every finding to be traced from the prompt that caused it to the runtime environment it threatens.
Customer Data
In benchmark testing, OX customers using VibeSec for AI governance have seen a 70–90% reduction in newly introduced vulnerabilities compared to environments without prompt-level governance.
OX Security's prompt-to-runtime platform is available now to customers. More information is available at ox.security.
About OX Security
OX Security is the platform for prompt-to-runtime security in the age of agentic AI. From governance at the prompt to enforcement at runtime, OX protects enterprises shipping autonomous systems at scale. A single context lake connects AI-user governance, code security, cloud enforcement, and agentic pentesting, so every finding carries its full lineage — from the decision that caused it to the risk it poses. OX Security has raised $94 million in total funding, including a $60 million Series B led by DTCP. Learn more at ox.security.
SOURCE OX Security
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