NetSPI, the global leader in offensive security, debuted its ML/AI Pentesting solution to bring a more holistic and proactive approach to safeguarding machine learning model implementations. The first-of-its-kind solution focuses on two core components: Identifying, analyzing, and remediating vulnerabilities on machine learning systems such as Large Language Models (LLMs) and providing grounded advice and real-world guidance to ensure security is considered from ideation to implementation.
As adoption of ML and AI accelerates, organizations must understand the unique threats that accompany this technology to better identify areas of weakness and build more secure models. NetSPI‘s testing methodology is rooted in adversarial machine learning – the study of adversarial attacks on ML and corresponding defenses. With this foundational research, the company’s offensive security experts have the knowledge to better understand and mitigate vulnerabilities within ML models by putting them to the test against real adversarial attack techniques.
“Securing technologies like ML/AI can be daunting, but our customers do not have to navigate the journey alone,” said Nick Landers, VP of Research at NetSPI. “Innovation in this space shows no signs of stopping – and we’re excited to bring our wealth of knowledge in machine learning, cybersecurity, and data science to help organizations navigate the emerging space with security top of mind. Our goal is not to slow innovation, but to help organizations innovate with confidence.”
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NetSPI‘s ML/AI Pentesting solution caters to organizations seeking to enhance the robustness, trustworthiness, and security of their ML systems, with a particular focus on LLMs. During an assessment, customers can expect:
- A dedicated partner through ideation, development, training, implementation, and real-world deployment
- Holistic and contextual security testing across their tech stack, leveraging NetSPI‘s application cloud, and network security testing expertise
- An evaluation of defenses against major attacks and tailored adversarial examples
- Guidance on how to build a robust pipeline for development and training
- Comprehensive vulnerability reports and remediation instructions delivered via NetSPI‘s PTaaS platform
“Every new paradigm shift brings along a new set of opportunities and challenges, and the widespread adoption of machine learning is no different,” said Vinay Anand, Chief Product Officer at NetSPI.“There is no silver bullet for ML/AI security, yet securing these systems is paramount.Our new pentesting solution equips businesses with the knowledge, tools, and best practices needed to protect their machine learning systems from adversarial threats and improve overall resiliency to attacks.”
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