AI security startup Artemis exits stealth with $70m

Artemis has emerged from stealth mode with $70m in combined seed and Series A funding — just six months after its founding.

The Series A round was led by venture capital firm Felicis, with continued backing from existing investors and notable figures from across the cybersecurity industry. The total raise combines the seed and Series A tranches, though individual round sizes were not disclosed.

The funding comes as AI-driven cyberattacks become increasingly difficult to counter with conventional tools. Such attacks can be executed within minutes, adapt continuously, and never repeat the same pattern — making traditional, rule-based detection systems ill-suited to the challenge. Those systems were designed for human-speed threats, meaning the rules written to counter attacks can become obsolete before they are even deployed. Security teams are frequently left piecing together context from dozens of disconnected tools spanning cloud, identity, endpoint, and network environments, often only after damage has already occurred.

Artemis was designed to address this gap, giving defenders the same speed and adaptability now available to attackers. At the heart of the platform is a proprietary dynamic data model built from each customer’s own telemetry. This fuses behavioural log data across users, machines, cloud workloads, and applications with business context, enabling the platform to assess whether any given action is consistent with normal activity for that specific organisation. The company works closely with leading frontier AI labs and models to remain at the forefront of how both attacks and defences are evolving.

Rather than overwhelming security teams with individual alerts, Artemis correlates signals and surfaces coherent attack narratives. For instance, if a privilege escalation in Okta occurs simultaneously with unusual API activity in AWS, the platform links both signals into a single correlated account rather than generating two separate, unconnected alerts. The platform can also execute automated responses — such as isolating a compromised identity before lateral movement can occur — when circumstances demand it. Artemis also offers an alternative cost model to traditional security information and event management (SIEM) architectures: rather than ingesting and storing all data upfront, it retrieves data on demand from customers’ existing cloud storage and log sources via federated queries, delivering full visibility at approximately a fifth of the cost of conventional approaches.

Since entering production fewer than six months after formation, Artemis is processing billions of events per hour for enterprise customers across technology, banking, and financial services. Early results include a technology company where Artemis’s first scan identified multimillion-dollar cloud spend savings and previously invisible shadow activity — including over-privileged accounts, undocumented integrations, and API calls with elevated privileges. At a heavily regulated enterprise with tens of thousands of employees, investigation times have fallen by 96%, with cases now completing in under five minutes. Customers are also able to build custom detections within minutes and query their environments in natural language, without the need for complex query construction.

The new capital will be used to grow Artemis’s engineering, research, and go-to-market teams, and to deepen platform capabilities as enterprise demand increases.

Artemis co-founder and CEO Shachar Hirshberg said, “We built Artemis as an AI-native defense system from the ground up. The question isn’t whether this model wins, but who builds it best. Some of the largest and fastest-growing companies in the world are among our first customers, and we’re able to deliver value to them on day one. That trust matters, and we intend to earn it every day.”

Artemis co-founder and CTO Dan Shiebler said, “At the core of Artemis is a data model I’ve been iterating on for years. 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.”

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