Resistant AI raises funding to protect AI systems from adversarial attacks
By FF Newsroom · 30 April 2020

Resistant AI, a security company that helps to protect AI systems from targeted manipulation, adversarial machine learning attacks and advanced fraud, has raised $2.75 million in funding. The company will use the funding for product development and hiring, including the expansion of its sales and support function for financial services and e-commerce customers.
The round was co-led by Index Ventures (Jan Hammer) and Credo Ventures (Ondrej Bartos and Vladislav Jez) with participation of Seedcamp. Daniel Dines, CEO of UiPath, Michal Pechoucek, CTO of Avast and other angel investors have also invested. Ondrej Bartos joins the board of directors on behalf of the investors.
With the increased reliance on AI, malicious actors are actively exploring vulnerabilities which, because of the very nature of AI, are much harder to detect. Working with customers in financial services, fintech and e-commerce, Resistant AI ensures that the AI-driven online and automated processes that these companies rely on, have not been compromised.
Martin Rehak, Founder & CEO, Resistant AI: "Historically, all systems that make high-value financial decisions become targeted. This is already happening with the automated systems deployed by our fintech and financial customers and we are here to protect them."
Resistant AI is deployed as an additional protection on top of AI systems, and
- protects credit risk scoring, payment systems, fraud detection, anti-money laundering and customer onboarding systems;
- detects forged documents submitted to mislead or manipulate automated processes;
- discovers relationships between seemingly unrelated transactions to identify advanced fraud designed specifically to evade the current fraud prevention systems, such as synthetic identities, bust-out fraud, approval boundary probing and many others;
- defends against strategic attacks where the attackers aim to copy the underlying model or the sensitive data used to train the model.