Secure AI System Development

Guidelines for 2024

In this post, take a look at a document published by the UK National Cyber Security Centre (NCSC), the US Cybersecurity and Infrastructure Security Agency (CISA) along with the National Security Agency (NSA), the Federal Bureau of Investigation (FBI), and cybersecurity agencies from Australia, New Zealand, Chile, the Czech Republic, Estonia, France, Germany, Israel, Italy, Japan, Nigeria, Norway, Poland, Korea and Singapore.

As an international organization, Get Network Visibility partners with security service providers worldwide, and these guidelines prove critical when implementing best practices, especially in the context of new AI-based challenges and opportunities.

Secure AI system development is paramount in our increasingly interconnected world. As artificial intelligence advances, its applications span critical domains, including finance, healthcare, and national security. Ensuring the security of AI systems is crucial for safeguarding sensitive data and mitigating potential risks such as cyberattacks, adversarial manipulations, and ethical concerns. The global nature of these challenges necessitates international collaboration in AI system development.

International collaboration in secure AI system development brings several key benefits. Firstly, it allows for the pooling of diverse expertise, fostering the exchange of best practices and promoting standardized security protocols. This collaborative approach helps create robust frameworks that transcend geographical boundaries, ensuring a more unified and effective defense against evolving cyber threats. Additionally, as AI technologies often transcend national borders, collaborative efforts facilitate the establishment of ethical guidelines and norms that reflect a broad spectrum of cultural, legal, and societal perspectives. 

This inclusive approach is essential for building trust in AI systems and promoting their responsible and ethical deployment on a global scale. In summary, secure AI system development with international collaboration is fundamental to addressing the complex challenges inherent in advancing artificial intelligence and fostering a secure, trustworthy, and globally accessible AI landscape.

Guidelines for Secure AI System Development

So, what does this look like in practice? This is where the Guidelines for Secure AI System Development come into play. These guidelines aim to offer comprehensive recommendations for providers involved in the development and deployment of AI systems, emphasizing the necessity of incorporating secure-by-design principles. The guidelines address AI systems’ unique challenges, including novel security vulnerabilities and the potential exploitation of machine learning components. 

The scope encompasses the entire lifecycle of AI systems, emphasizing the continual importance of security considerations from development through deployment. By adhering to these guidelines, AI system providers are encouraged to prioritize customer security outcomes, embrace transparency and accountability, and make secure design a top business priority. 

The document underscores the global nature of the challenges and advocates for international collaboration, urging providers to follow established ‘secure by design’ principles developed by leading cybersecurity entities. Overall, these guidelines serve as a crucial resource for creating AI systems that function as intended and uphold the highest standards of security, reliability, and ethical conduct.

Understanding the AI System Development Life Cycle

The Guidelines lay out some best practices for the AI system development life cycle, encompassing design, development, deployment and secure operation and maintenance. Throughout the AI system development lifecycle, securing the supply chain remains a paramount focus, with continuous assessment and monitoring of security standards applied to suppliers. Whether produced in-house or externally sourced, acquiring and maintaining well-secured hardware and software components is emphasized, incorporating commercial, open-source, and third-party developers.  

Failover mechanisms for mission-critical systems are prepared if security criteria are not met, using resources like NCSC’s Supply Chain Guidance and the Supply Chain Levels for Software Artifacts (SLSA) framework. Asset identification, tracking, and protection are integral, recognizing the value of AI-related assets and implementing controls to safeguard confidentiality, integrity, and availability, including managing data access and content generated by AI. Thorough documentation of data, models, and prompts is emphasized, incorporating security-relevant information to ensure transparency and accountability.

Technical debt management is addressed throughout the AI system’s life cycle, recognizing the unique challenges posed by rapid development cycles and evolving protocols. Lifecycle plans encompass risk assessment, acknowledgment, and mitigation, ensuring the responsible management of technical debt and facilitating the decommissioning of AI systems.

Guidelines for Secure AI System Development and NPBs

Network Packet Brokers (NPBs) are crucial in the AI system development lifecycle, particularly in securing the supply chain and managing technical debt. In securing the supply chain, NPBs contribute by ensuring the visibility and accessibility of network traffic data. By aggregating, filtering, and distributing network packets to security tools, including those involved in AI system development, NPBs enhance the monitoring and assessment of security standards. This visibility aids in identifying potential vulnerabilities and assessing the adherence of suppliers to security protocols, crucial aspects of securing the supply chain.

Moreover, in managing technical debt, NPBs assist in tracking and managing the “technical debt” associated with network traffic data throughout an AI system’s life cycle. The comprehensive visibility NPBs provide allows for identifying potential engineering decisions that might fall short of best practices, contributing to technical debt. By continuously monitoring and optimizing network packet flows, NPBs facilitate the identification and resolution of bottlenecks, inefficiencies, or potential vulnerabilities, ultimately supporting the effective management of technical debt in AI systems.

the guidelines underscore the critical importance of secure AI system development, emphasizing principles such as securing the supply chain, asset identification, documentation, and managing technical debt throughout the system’s life cycle. A noteworthy aspect involves the call for international cooperation, advocating collaboration to address the global nature of AI-powered threats. By adhering to established ‘secure by design’ principles and leveraging resources like Network Packet Brokers (NPBs), organizations can enhance their ability to combat AI-powered threats.

NPB technology, by providing comprehensive visibility into network traffic data, plays a pivotal role in monitoring, assessing, and optimizing security standards. This technology contributes to securing the supply chain, identifying potential vulnerabilities, and managing technical debt effectively. As the global community works collaboratively, sharing expertise, best practices, and threat intelligence, organizations can harness the benefits of AI while establishing a robust defense against emerging threats, ultimately ensuring the responsible and secure advancement of artificial intelligence on a global scale.