HiddenLayer startup emerges from stealth mode, raises $6m seed funding to protect AI-powered products
HiddenLayer, the developer of a unique security platform that safeguards the machine learning models enterprise organizations use behind their most important products, emerged from stealth mode to launch its flagship product, purpose-built to detect and prevent cyberattacks that target machine-learning-powered systems. The HiddenLayer Platform's primary product is the industry's first and only Machine Learning Detection & Response (MLDR) solution that protects enterprises and their customers from this emerging attack vector.
Organizations across virtually all industries are incorporating artificial intelligence (AI) technology into their hardware and software products that make machine learning (ML) inputs and outputs available to their customers, and bad actors have taken notice. Gartner predicts that 30 percent of all AI cyberattacks in 2022 will leverage training-data poisoning, model theft, or adversarial samples to attack machine learning-powered systems.
Chris Sestito, CEO, HiddenLayer: "We led the relief effort after our machine learning model was attacked directly through our product, and realized this would be an enormous problem for any organization deploying ML models in their products. We decided to found HiddenLayer to both educate enterprises about this significant threat and help them defend against it."
When ML inputs and decisions are publicly exposed, attackers can reverse-engineer the IP to steal trade secrets and tamper with production systems. These state-of-the-art attacks can destroy multi-million dollar investments, delay product releases, and leave victim organizations legally and financially liable.
According to HiddenLayer (founders Chris "Tito" Sestito, Tanner Burns, and James Ballard) companies unknowingly create vulnerabilities in their ML models for which there are no known commercially-available security controls. It's a lesson they learned first-hand while working together at Cylance, an endpoint security software developer that pioneered the application of machine learning in anti-virus.
HiddenLayer's MLDR solution uses a patent-pending ML-based approach to analyze billions of model interactions per minute to identify malicious activity without any access to or prior knowledge of the user's ML model(s) or sensitive training data. It detects and responds to attacks against ML models to protect intellectual property and trade secrets from theft or tampering and ensure users are not exposed to attacks.
Christina Liaghati, Operations Manager, AI & Autonomy Innovation Center, MITRE ATLAS: "AI assurance and security is critical for consequential uses of AI and broader adoption. Collaboration between the government, academia, and industry will lead to a healthy ecosystem and unique perspectives to address these crucial challenges."
HiddenLayer's MLDR is a non-invasive and easy-to-use security solution that does not require access to raw data or algorithms. It identifies patterns in ML model traffic through contextless vectorized data points to provide comprehensive defense from adversarial attacks targeting the deployed ML model in production. HiddenLayer is using the MITRE ATLAS framework to structure their platform to align with the industry's leading authority on adversarial threats targeting artificial-intelligence systems.
Todd Weber, Operating Partner and CTO, Ten Eleven Ventures: "Machine learning algorithms are rapidly becoming a vital and differentiating aspect of more and more of the technology products we depend on every day. As dedicated cybersecurity investors, we know that protecting the algorithms at the very center of a company's competitive advantage will become an essential part of a company's cyber defenses - these algorithms will become the new 'crown jewels.' Tito, Jim, and Tanner have the unique experience and skill set to solve this growing problem, making HiddenLayer the company best positioned to lead this important new category of security."