In a landmark move, Australian Signals Directorate, along with other international cybersecurity partners, have released the 'Guidelines for Secure AI System Development', providing a comprehensive blueprint for the security of artificial intelligence (AI) systems and addressing their unique vulnerabilities and the challenges they present in cybersecurity.
A key feature of the guideline is its emphasis on embedding security practices at the very foundation of AI system development.
The guideline proposes a holistic approach to AI security, covering all phases of an AI system's lifecycle: from design and development to deployment and ongoing maintenance.
This approach is aimed at a wide range of stakeholders involved in AI systems, including developers, managers and policymakers.
Today we're pleased to announce the #AISecurityGuidelines - a landmark of guidance putting cybersecurity at the core of the AI systems of the future and making sure the benefits of AI are realised securely.Read the guidance here ???? https://t.co/vQwH7Jx20x pic.twitter.com/CljhjZE4Me
— Australian Cyber Security Centre (@CyberGovAU) November 27, 2023
Key features
The document outlines essential principles such as taking responsibility for security outcomes, promoting transparency and accountability, and elevating security as a primary concern in business operations.
It acknowledges the distinct threats to AI systems, such as adversarial machine learning, and offers strategies to mitigate these risks.
These include implementing comprehensive threat modelling, ensuring supply chain security, managing assets and technical debts effectively, and preparing for robust incident responses.
Aligning with established cybersecurity frameworks like those from the NCSC and NIST, these guidelines offer a unified and comprehensive approach to AI security.
They underscore the critical need for robust security measures in the face of the growing sophistication and prevalence of AI systems in various sectors.
Four key areas
The guidelines are broken down into four key areas within the AI system development life cycle, which are:
- Secure design - This section contains guidelines that apply to the design stage of the AI system development life cycle. It covers understanding risks and threat modelling, as well as specific topics and trade-offs to consider on system and model design.
- Secure development - This section contains guidelines that apply to the development stage of the AI system development life cycle, including supply chain security, documentation, and asset and technical debt management.
- Secure deployment - This section contains guidelines that apply to the deployment stage of the AI system development life cycle, including protecting infrastructure and models from compromise, threat or loss, developing incident management processes, and responsible release.
- Secure operation and maintenance - This section contains guidelines that apply to the secure operation and maintenance stage of the AI system development life cycle. It provides guidelines on actions particularly relevant once a system has been deployed, including logging and monitoring, update management and information sharing.
This release marks a significant step in international efforts to address the complex security challenges posed by AI technologies, providing a framework that is expected to guide the secure development and deployment of AI systems globally.