Artemis has emerged from stealth with $70m in combined seed and Series A funding, just six months after the company was founded.
The Series A round was led by Felicis, with continued participation from existing investors and a number of prominent figures from the cybersecurity industry. The seed round was co-led by Brightmind, whose general partner Gur Talpaz also doubled down in the Series A. The fresh capital will be used to grow Artemis’s engineering, research, and go-to-market functions as enterprise demand continues to build.
At the centre of the platform is a proprietary dynamic data model constructed from each customer’s own telemetry. This fuses behavioural log data across users, machines, cloud workloads, and applications with business context, allowing the system to determine whether a given action is appropriate for a specific organisation. Rather than inundating security teams with disconnected alerts, Artemis correlates signals and presents a single, coherent account of an attack. For instance, if unusual API activity in AWS coincides with a privilege escalation in Okta, the platform contextualises both events and surfaces one unified narrative rather than two separate notifications. Where necessary, Artemis can also trigger automated responses at machine speed, such as isolating a compromised identity before lateral movement can occur.
The company also distinguishes itself on cost efficiency. Unlike traditional security architectures that require all data to be ingested and stored in advance — a model where costs scale directly with data volume — Artemis retrieves data on demand from customers’ existing cloud storage and log sources via federated queries, offering full visibility at approximately a fifth of the cost.
In the months since launch, Artemis is already deployed in production environments and processing billions of events per hour for enterprise clients across technology, banking, and financial services. Early results include a technology firm where the platform’s first scan identified multimillion-dollar cloud spend savings alongside previously invisible shadow activity, including over-privileged accounts and undocumented integrations. A second early customer, a heavily regulated organisation with tens of thousands of employees, has reduced investigation times by 96%, now completing reviews in under five minutes.
Co-founder and CEO Shachar Hirshberg previously built and scaled security operations platforms at Palo Alto Networks and at Demisto — the company credited with creating the SOAR category, which was later acquired in one of the largest pure-play security operations deals on record. He subsequently completed an MBA at Harvard Business School before leading GuardDuty at AWS, described as the largest cloud attack detection product in the world. Co-founder and CTO Dan Shiebler spent the past decade building large-scale AI systems, most recently leading machine learning and AI at Abnormal AI, where he drew on research from his PhD in machine learning at the University of Oxford.
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.”
Felicis general partner Jake Storm said, “Artemis has built a truly world-class team with rare depth at the intersection of AI and cybersecurity, solving a problem that’s becoming increasingly urgent as attacks grow in frequency and complexity. Shachar’s track record in building enterprise-scale security products and Dan’s deep AI research background make them uniquely suited to tackle this problem together. Just six months after founding and while still in stealth, the team has seen enterprise inbound driven purely by word of mouth and early results. That level of demand at this stage is rare and signals the scale of the opportunity.”
Copyright © 2026 RegTech Analyst
Copyright © 2018 RegTech Analyst
