Frontier Rounds— AI funding intelligence

HiddenLayer Funding History

HiddenLayer builds security software for AI models and systems. It scans models, detects threats at runtime and helps organizations monitor their AI security posture.

Company Overview

HiddenLayer logo

HiddenLayer

  • Cybersecurity

Founded 2022 · Austin, Texas, US

Official Website

HiddenLayer Funding Timeline

  1. Sep 2, 2026

    Series B

    $100M

    HiddenLayer said the financing would expand runtime protection for AI agents, including controls around autonomous coding tools.

    Participation included Ten Eleven Ventures, Morgan Stanley, M12.

    Source · hiddenlayer.com ↗

  2. Announced Sep 19, 2023

    Series A

    US$50 million

    HiddenLayer said the funds would expand its team and support development of its machine-learning security platform.

    Led by M12, Moore Strategic Ventures.

    Source · prnewswire.com ↗

  3. Announced Jul 19, 2022

    Seed

    US$6 million

    HiddenLayer came out of stealth alongside the announcement, introducing a security product for organizations deploying AI models.

    Led by Ten Eleven Ventures.

    Source · prnewswire.com ↗

HiddenLayer Founding Team

Christopher Sestito, Tanner Burns, and James Ballard started HiddenLayer after research into malware designed to target AI systems. Sestito leads the company, Burns leads scientific research, and Ballard leads operations.

HiddenLayer Products

  • HiddenLayer AI Security Platform

    Scan AI models for threats and monitor their behavior while they run.

HiddenLayer Company Development

Model Security Extends into Live Deployments

HiddenLayer launched in 2022 with protection for AI-powered products. Its later platform covers checks on models entering the environment, monitoring while systems run, and tracking security posture across AI deployments.

HiddenLayer Competitive Landscape

Where AI Threats Are Intercepted

HiddenLayer covers threats at the model and deployment level, including scanning and runtime detection. Lakera concentrates on application inputs and outputs, such as attempts to override instructions or extract data. The two overlap in deployment security but inspect different parts of an AI system.