Insider Brief
- InCountry raised $10M in funding led by Arbor Ventures, with participation from Caffeinated Ventures and Accelerator Ventures, to accelerate the global rollout of its new AI-driven data protection solution, AgentCloak.
- AgentCloak uses tuned AI and digital twin technology to cloak sensitive information like names, addresses, medical terms, and personal identifiers, ensuring compliance with regulations such as the EU AI Act, China’s PIPL, and Saudi Arabia’s PDPL.
- Designed for enterprises and governments, AgentCloak integrates into agentic workflows to enable secure, cross-border AI deployments, with funding supporting R&D, partnerships, and international market expansion.
PRESS RELEASE — InCountry, a provider of data protection products at the intersection of AI, identity, and sovereignty, announced it has raised $10 million in funding led by Arbor Ventures, with participation from Caffeinated Ventures and new investor Accelerator Ventures. The funding will accelerate global deployment of InCountry’s AgentCloak, a next generation AI agent data protection solution launched today.
AgentCloak deploys tuned AI to deliver a next-generation approach to data protection, generalizing sensitive data for data privacy and compliance. In addition to detecting and cloaking names, addresses, and account numbers, it can transform “headache” into “minor neurological condition,” “Berlin” into “EU,” and an age such as “32” into the “30–39” bracket.
AgentCloak’s AI can identify separate individuals within interactions, such as “I’m writing about my father’s test results” to provide robust data minimization. Within countries, AgentCloak uses Sovereign AI’s that are trained on local names, addresses, and local vernacular to detect sensitive data including medical terms. AgentCloak leverages InCountry’s proven digital twin technology to secure and cloak data.
AgentCloak enables AI agents to operate seamlessly within and across borders while adhering to stringent data privacy and AI regulations, including the EU AI Act, China’s PIPL, and Saudi Arabia’s PDPL. The technology integrates seamlessly with agentic workflows as an MCP Server, MCP Proxy, or A2A Proxy. AgentCloak helps train agents and incorporate RAG using cloaked digital twins and uncloak data with identity-based authorization
“As AI is posed to revolutionize industries, prioritizing data privacy, trust, and compliance is critical,” said Peter Yared, Founder and CEO of InCountry. “We’ve been collaborating on AgentCloak for over a year with our customers and partners, and we’re excited to launch general availability and supporting businesses with compliant AI agent deployments within countries and across borders.”
The funding will support expanded research and development, strategic partnerships, and global market expansion. InCountry’s unique position as a trusted partner to both large enterprises and government regulatory bodies will drive AgentCloak’s global reach.
“We’re thrilled to back InCountry’s vision for global AI data protection,” said Melissa Guzy, General Partner at Arbor Ventures. “AgentCloak’s AI-powered data generalization and secure digital twin technology will help enterprises protect their customer data within countries and across borders.”
For more information about InCountry and AgentCloak, visit InCountry.com amd AgentCloak.ai.
About InCountry
InCountry powers data protection at the nexus of AI, identity, and sovereignty. Our products deliver AI data minimization and cross-border AI sovereignty for AI agents, profile data residency for Identity and Access Management solutions, and end-user data residency for enterprise apps and SaaS, including Salesforce and ServiceNow. Trusted by Global 2000 companies in life sciences, semiconductors, and financial services, InCountry proudly enables compliance with leading regulatory bodies, including the Cyberspace Administration of China, the Saudi Arabian Monetary Authority, and European Data Protection Authorities.
Contacts
Media:
Nataliia Dolzhenko
Director of Marketing, InCountry
Email: press@incountry.com
