Rig Security Raises $12M for AI Agent Identity Protection

A new cybersecurity venture, Rig Security, has officially launched from stealth mode, securing $12 million in seed funding to address a critical gap in enterprise security: the identity of AI agents. The funding round was co-led by prominent venture capital firms Ten Eleven Ventures and Brightmind Partners, with participation from the CrowdStrike Falcon Fund and a notable group of cybersecurity leaders, including Wiz co-founder and CTO Ami Luttwak.

The core challenge Rig Security aims to solve stems from the increasing reliance on AI agents within corporate environments. These agents often operate by leveraging existing human or service account credentials, creating a significant blind spot for traditional security systems. This ambiguity makes it difficult for security teams to differentiate between legitimate user activity and actions performed by AI agents, especially when those actions are potentially malicious, such as data deletion or unauthorized system access. Rig Security's platform is designed to disentangle these identities, providing clarity and control.

Founded by CEO Guy Kozliner, a former executive at Wiz, and supported by CTO Nokky Goren (formerly of Axis Security) and Head of Product Michal Haikov (ex-Unit 8200 and Flow Security), the company is building technology to specifically identify and manage AI agents operating under borrowed identities. The market for AI security is rapidly expanding, with projections indicating substantial growth as AI adoption accelerates across industries like financial services, insurance, healthcare, and technology, where Rig Security reports early traction with Fortune 200 companies.

At the heart of Rig Security's offering is its proprietary Rig Identity Correlation Engine (RICE). This system employs advanced machine learning and mathematical modeling to correlate identities across diverse enterprise systems, including identity providers, cloud infrastructure, networks, and endpoints. The company claims RICE achieves an accuracy rate exceeding 96% in resolving identities, a crucial metric for distinguishing AI-driven actions from human ones. This engine feeds into an Identity Dependencies Graph, mapping the complex relationships between users, machines, AI agents, permissions, and active sessions.

Complementing the RICE engine is an endpoint sensor that monitors activity at the device level. This allows Rig Security's software to differentiate an AI agent's operational session from an employee's genuine activity. This distinction is vital for enforcing security policies specifically against AI agents without disrupting legitimate human access. As AI agents are increasingly empowered to perform complex tasks like code generation and system analysis, the ability to isolate and govern their actions becomes paramount for maintaining operational integrity and security posture.

The influx of capital underscores the growing recognition of AI-specific security challenges within the broader cybersecurity market. With AI agents becoming more sophisticated and integrated into business workflows, the traditional paradigm of identity and access management is being fundamentally re-evaluated. Rig Security's approach, focusing on the granular identification and control of AI agents, positions it to address a pressing need for enterprises navigating the complexities of AI integration and seeking to prevent unauthorized or unintended consequences stemming from autonomous systems.