Embracing Security in AI: A Crucial Frontier
ChatGPT gained 100M users 2 months after its release and has received over $10B in investments from Microsoft, according to Forbes. At the same time, the use of Software as a Service (SaaS) Large Language Model (LLM) Application Programming Interfaces (APIs) to access generative AI services, like ChatGPT, grew 1310% between November 2022 and May 2023 alone, as reported by the 2023 State of Data + AI by Databricks.
As the proliferation of Artificial Intelligence (AI) and particularly Generative AI (GenAI) reshapes our world, the imperative to safeguard AI systems against misuse and enhance their robustness cannot be overstated. The rapid advancement of AI promises unprecedented efficiency and capabilities, yet it also presents new, complex security challenges that must be addressed with innovative and robust solutions.
A survey of the global Machine Learning (ML) community reported that 69% of its members believe AI safety should be prioritised more, while governments and NGOs call for cooperation across borders and sectors to share information, develop standards, and work towards responsible stewardship of AI, according to sources such as the OECD’s International Cooperation for Trustworthy AI and the Centre for AI Safety (CAIS).
AI Security in context
AI technologies are increasingly embedded in various sectors, managing vast datasets and complex algorithms across industries and society. This omnipresence amplifies the potential impact of AI, urging a dual focus on both advancement and security by design. In other words, the safety and robustness of AI systems can no longer be seen an afterthought, but rather be incorporated in the development of AI since the principal design stages. The emerging European policy landscape reflects this need, balancing AI competitiveness with the protection of core values and fundamental rights.
The State of the Art in Security by Design
An emerging approach in the development of AI systems lies in the convergence of machine learning (ML) operations and ‘DevOps’ practices into what is known as ‘MLOps’, as outlined by Atlassian. This framework is essential for managing the lifecycle of ML models, emphasising efficiency, scalability, and reliability. However, there is a pressing need to extend this paradigm to ‘SecMLOps’, incorporating security at every phase from design to deployment.
SecMLOps isn’t just a methodology; it’s a necessity in today’s landscape where AI systems must be resilient to evolving threats. Zero-trust architectures that adapt to varying contexts, ensuring the security and robustness of AI systems across different operational environments are to be explored in this regard. Establishing the SecMLOps paradigm also involves a meticulous process of testing in various real-world scenarios to pre-certify security and robustness, ensuring that AI applications are safe before they even reach the market.
Moving Forward
The journey towards secure AI is complex and continuous. As AI systems become more integral to critical infrastructures and societal functions, the stakes will only get higher. The commitment to research, development, and the implementation of cutting-edge security measures in AI is not just about innovation; it’s about responsibility—to ensure that AI advancements enhance societal well-being without compromising safety or privacy.
The development of secure AI systems is a collaborative effort, which involves researchers, developers, policymakers, and the public, each playing a crucial role in shaping a future where AI supports and enhances human efforts securely and effectively. This collaboration should be fostered, ensuring AI not only advances but does so with security and robustness at its core.
