Aurascape launched from stealth in April 2025 to address one of the thorniest problems created by generative AI: enterprises have lost visibility into how their people and software actually use AI. Employees adopt chatbots, copilots, and AI-enabled SaaS faster than security teams can sanction them, creating 'shadow AI' that can quietly exfiltrate sensitive data. Aurascape positions itself as the security stack for the AI era, built natively for this new attack surface rather than retrofitted from older tooling.
The platform's AI Activity Control capabilities discover and monitor AI usage across apps, browsers, and copilots — including embedded AI features inside otherwise familiar SaaS products. It provides real-time visibility into what data is flowing into which AI tools, then applies policy enforcement and data protection to prevent sensitive information from leaking. Crucially, the goal is to enable safe AI adoption rather than block it outright, letting businesses 'innovate fearlessly' while staying within guardrails.
Aurascape extends beyond employee AI use into the agentic frontier. It secures AI agent development from code to runtime and protects agents operating in production, reflecting a bet that autonomous agents will become a major enterprise risk vector. This spans data leakage, unsafe agent behavior, and the broader governance gap around who can build and deploy AI inside an organization.
The company was founded in 2024 and is led by CEO Moinul Khan, a security-product veteran who spent years at Zscaler leading its SSE and data-protection business, with prior product leadership at Palo Alto Networks and Netskope. That pedigree shapes Aurascape's enterprise data-security orientation. Its launch funding of $50 million was co-led by Mayfield Fund and Menlo Ventures, with Celesta Capital and a roster of security luminaries including former Palo Alto Networks CEO Mark McLaughlin, former Symantec CEO Greg Clark, Walden International chairman Lip-Bu Tan, and former Zscaler strategy chief Manoj Apte.
Aurascape's thesis is that AI adoption and AI security must move together: as organizations race to deploy generative tools and agents, they need an AI-native control plane that provides discovery, governance, and runtime protection so that productivity gains don't come at the cost of leaked data or ungoverned autonomous systems.