As artificial intelligence (AI) adoption continues to accelerate, with over 80% of enterprises expected to use generative AI APIs, models, and applications by 2026, it’s essential to address the growing concern of AI application security. Currently, 34% of organizations are already using AI application security tools, but this number is likely to increase as AI technology evolves rapidly. Ensuring the security of AI systems is critical, especially when it comes to machine learning (ML) concepts relevant to cybersecurity professionals. A thorough security review begins with understanding the components involved in the system, including ML models, which can introduce cybersecurity risks and weaknesses in the overall security architecture.
The article provides an introduction to ML concepts, explaining that ML is a subset of AI that enables computers to execute tasks without explicit programming. It’s built upon algorithms and statistical models designed to recognize patterns and relationships in data. The article also delves into large language models (LLMs), a specific type of ML that has led the AI industry due to its capacity to understand and generate human-like text. However, ML can open up many cybersecurity risks, and create weaknesses in the overall security architecture. Understanding the components and risks associated with ML frameworks and model formats is crucial to ensure vulnerabilities or unintended consequences are not incurred due to security flaws in ML deployment.
