HiddenLayer Secures $50 Million Funding To Strengthen AI-Defending Cybersecurity Tools


HiddenLayer, a cybersecurity startup focused on protecting AI systems from adversarial attacks, has announced that it raised $50 million in a recent funding round. The funding was co-led by M12 and Moore Strategic Ventures, with participation from Booz Allen Hamilton, IBM, Capital One, and TenEleven. With this new capital injection, HiddenLayer’s total funding now stands at $56 million.

Key Takeaway

HiddenLayer, a cybersecurity startup, has raised $50 million in funding to support the development and expansion of its AI-defending cybersecurity tools. The company offers a machine learning security platform that safeguards AI models against adversarial attacks and vulnerabilities. With the growing adoption of AI across industries, ensuring the security of AI systems has become paramount.

Expanding Market Presence and Enhancing Security Offerings

The newly secured funds will be utilized to support HiddenLayer’s go-to-market efforts, including expanding its workforce from 50 employees to 90 by the end of the year. Additionally, the company plans to invest further in research and development to enhance its AI-driven cybersecurity platform.

HiddenLayer’s machine learning security platform provides essential tools to protect AI models against adversarial attacks, vulnerabilities, and malicious code injections. It achieves this by monitoring the inputs and outputs of AI systems and conducting rigorous integrity tests to ensure models are secure before deployment.

One of the key strengths of HiddenLayer’s platform lies in its ability to protect against transfer learning attacks that can occur when organizations employ pre-trained, open-source models available for public use. By observing only the mathematical representations of inputs and outputs, HiddenLayer mitigates the risk of exposing proprietary models.

Contributing to Adversarial AI Knowledge Base

In addition to its core offerings, HiddenLayer also contributes to the MITRE ATLAS, a knowledge base of adversarial AI tactics and techniques maintained by the MITRE Corporation. HiddenLayer’s CEO, Chris Sestito, claims that their platform can safeguard against all 64 unique attack types listed in ATLAS, including IP theft, model extraction, inferencing attacks, model evasion, and data poisoning.

The Importance of AI Security

While real-world examples of large-scale attacks against AI systems are difficult to come by, government agencies and organizations are increasingly recognizing the need to address potential threats. The National Cyber Security Center in the UK and the US Government’s Office of Science and Technology Policy have both highlighted the importance of testing, risk identification, and ongoing monitoring to ensure the safety and effectiveness of AI systems.

Industry studies reflect the growing concern over AI security. In a Forrester study commissioned by HiddenLayer, a majority of companies surveyed expressed concerns about machine learning model security and the reliance on manual processes to address AI model threats. Additionally, Gartner reported that 2 in 5 organizations experienced an AI privacy breach or security incident in the past year, with 1 in 4 of those attacks deemed malicious.

Meeting the Growing Demand for AI Security

HiddenLayer recognizes that the demand for AI security solutions will continue to grow as the adoption of AI expands across industries. While other startups offer products designed to enhance the robustness of AI systems, HiddenLayer differentiates itself with its AI-driven detection and response approach.

The company has gained traction, with partnerships with Databricks and Intel, along with Fortune 100 customers in the financial, government, defense, and cybersecurity industries. The increasing pace of AI adoption underscores the need for organizations to prioritize the implementation of proper security measures, making solutions like HiddenLayer’s platform essential.

Leave a Reply

Your email address will not be published. Required fields are marked *