Key Takeaways
- • Artemis, an AI-native cybersecurity startup, emerged from stealth with $70 million in combined seed and Series A funding, positioning itself to address the growing threat of AI-powered attacks in enterprise environments.
- • The platform leverages behavioral analytics and dynamic data models to detect and autonomously respond to threats across applications, users, machines, and cloud workloads, operating alongside legacy security infrastructure without requiring full data migration.
- • Founding team includes veterans from Abnormal AI, AWS, and Palo Alto Networks, bringing deep expertise in AI-driven security and enterprise threat detection to a market facing rapid evolution of attack methodologies.
- • Backers include prominent venture firms—Brightmind, Felicis, First Round Capital, Lockstep, Theory VC, and Two Sigma Ventures—reflecting strong institutional confidence in the AI security infrastructure category.
- • Early customer traction spans technology, banking, and financial services sectors, suggesting product-market fit in security-critical industries where autonomous threat response capabilities command premium valuations.
Summary
Artemis, a New York-based cybersecurity startup founded in 2025, has exited stealth mode with $70 million in combined seed and Series A funding to address the emerging category of AI-powered cyber threats. The company has developed an AI-native platform designed to detect and contain threats across applications, users, machines, and cloud workloads—moving beyond static, rule-based detection models toward adaptive, autonomous defense mechanisms.
The platform's core innovation centers on a dynamic data model that combines behavioral log data with business context to identify anomalies specific to each organization's operational patterns. Rather than storing comprehensive datasets upfront, Artemis retrieves telemetry on demand from cloud sources and logs, reducing infrastructure costs while maintaining full visibility. The system autonomously investigates alerts, identifies attack paths, and applies remediations—capabilities that differentiate it from traditional security information and event management (SIEM) systems. Critically, the solution operates alongside existing legacy security tools, reducing implementation friction and capital expenditure for enterprises with mature security stacks.
The founding team brings substantial pedigree to the venture. Co-founder and CTO Dan Shiebler emphasized the company's core thesis: "The real breakthrough isn't just using better AI models, but in giving those models deep, structured understanding of how an organization functions, making reliable detection and automated response possible." The team includes executives previously at Abnormal AI, Amazon Web Services, and Palo Alto Networks—three organizations at the forefront of AI-driven security architecture.
Artemis's investor syndicate reflects confidence in the AI security infrastructure market. Backers include Brightmind, Felicis Ventures, First Round Capital, Lockstep, Theory VC, Two Sigma Ventures, and multiple angel investors—a mix of generalist venture firms and specialized security/fintech investors. Early customer adoption has extended into technology, banking, and financial services verticals, suggesting enterprise demand for autonomous threat response capabilities in regulated and security-sensitive industries.
The $70 million raise will fund expansion of engineering and go-to-market operations as the company scales its platform and drives deeper enterprise penetration. The funding reflects both market recognition of AI-powered attack methodologies as a distinct threat category and institutional appetite for infrastructure plays in enterprise cybersecurity.
The platform's core innovation centers on a dynamic data model that combines behavioral log data with business context to identify anomalies specific to each organization's operational patterns. Rather than storing comprehensive datasets upfront, Artemis retrieves telemetry on demand from cloud sources and logs, reducing infrastructure costs while maintaining full visibility. The system autonomously investigates alerts, identifies attack paths, and applies remediations—capabilities that differentiate it from traditional security information and event management (SIEM) systems. Critically, the solution operates alongside existing legacy security tools, reducing implementation friction and capital expenditure for enterprises with mature security stacks.
The founding team brings substantial pedigree to the venture. Co-founder and CTO Dan Shiebler emphasized the company's core thesis: "The real breakthrough isn't just using better AI models, but in giving those models deep, structured understanding of how an organization functions, making reliable detection and automated response possible." The team includes executives previously at Abnormal AI, Amazon Web Services, and Palo Alto Networks—three organizations at the forefront of AI-driven security architecture.
Artemis's investor syndicate reflects confidence in the AI security infrastructure market. Backers include Brightmind, Felicis Ventures, First Round Capital, Lockstep, Theory VC, Two Sigma Ventures, and multiple angel investors—a mix of generalist venture firms and specialized security/fintech investors. Early customer adoption has extended into technology, banking, and financial services verticals, suggesting enterprise demand for autonomous threat response capabilities in regulated and security-sensitive industries.
The $70 million raise will fund expansion of engineering and go-to-market operations as the company scales its platform and drives deeper enterprise penetration. The funding reflects both market recognition of AI-powered attack methodologies as a distinct threat category and institutional appetite for infrastructure plays in enterprise cybersecurity.
Go deeper than the headline
Research every firm in this story, track the ones that matter and get the daily briefing — free for 7 days.