AI Model Security Standards: A Critical Aspect of Machine Learning
Artificial intelligence (AI) is transforming the way humans interact with the world. It is a technological boon that has led to improved efficiency and productivity across various industries. The underlying machine learning (ML) algorithms drive the immense potential of AI, but at the same time, the security of these algorithms is also critical.
Organizations worldwide are racing towards AI adoption, but as ML algorithms become more complex, they become more vulnerable to security attacks. However, it is not just the security of the algorithms that need safeguarding, but also the data used to train these algorithms. Hence, there is an urgent need to define and implement robust AI model security standards.
In this article, we will discuss the need for AI model security standards, current industry best practices, and the future of AI security.
Why Are AI Model Security Standards Crucial?
As AI proliferates into our lives, the vast amounts of data being utilized to train these algorithms also increases proportionally. The quality and security of these data inputs are critical to the performance of the underlying ML algorithms. However, there are numerous examples that highlight AI model insecurity.
In 2018, a research paper highlighted the susceptibility of ML models to adversarial attacks. The study showed that attackers could manipulate images that the ML models relied on to make decisions. Similarly, transportation authorities worldwide utilize computer vision to monitor traffic in real-time. However, it was discovered that attackers could trick the vision algorithms by merely blurring out the license plate or altering street signs, leading to severe traffic accidents.
Besides, data privacy is another significant issue that organizations need to address. As AI relies on large amounts of data to train its algorithms, organizations must safeguard data privacy at all stages of ML model creation. In May 2021, a prominent data breach occurred with a US-based employment website where over 700 Million user records were stolen, exposing the confidential information of millions of people.
How Are Organizations Defining AI Model Security Standards?
Defining AI model security standards is a complex task, which requires collaboration between multiple stakeholders to create a holistic security framework. The framework should provide a set of guidelines to ensure secure, ethical, and reliable deployment of AI algorithms in various applications.
Several organizations worldwide have come together to define AI model security standards. The globally recognized ISO-IEC standard 62443-4-2 provides guidance on creating secure industrial automation control system design and development throughout the lifecycle. The framework applies to the Industrial Internet of Things (IIoT) and connected systems, including diverse applications such as smart grid systems, smart city infrastructures, and in-car technologies.
Similarly, the Institute of Electrical and Electronics Engineers, known as IEEE, released the first-ever ethical standard for autonomous and intelligent systems (A/IS), known as the IEEE Global Initiative for Ethical Considerations in A/IS. The framework is a collection of over 200 global stakeholders’ inputs and provides a set of guidelines on ethical issues related to AI security, privacy, and accountability.
Moreover, industry giants such as Google and Microsoft have implemented their AI ethics and security frameworks. For instance, Google has implemented its AI Principles, which provide guidelines on the ethical use of AI, data privacy, and transparency. Similarly, Microsoft’s AETHER committee assesses ethical issues related to AI within the organization.
What Is the Future of AI Model Security Standards?
As AI applications become more widespread and critical to industries, securing the underlying ML algorithms becomes paramount. There needs to be a shared understanding of AI security within the industry, with stakeholders collaborating to implement best practices.
However, AI model security standards are not static and require a constant evolution to keep up with the ever-changing security landscape. AI applications need to be assessed on an ongoing basis for potential vulnerabilities, with remedial actions taken when necessary.
Moreover, the future of AI security lies in the development of explainable AI, where the decision-making process of the AI algorithm can be transparently understandable to humans. Explainable AI would enable the traceability of AI decisions, leading to better accountability and transparency of AI applications.
Conclusion
In conclusion, AI model security standards are crucial to ensure the reliability and ethical deployment of AI algorithms in various applications. The security of data used to train these algorithms is equally crucial and should be safeguarded at all stages of ML model creation. Organizations worldwide are collaborating to define industry best practices, but there is a constant need to evolve these standards to keep up with the evolving security landscape. It is imperative that stakeholders collaborate to implement and abide by these standards to ensure long-term and sustainable AI integration into our daily lives.