Project Glasswing: Securing critical software for the AI era
Project Glasswing highlights the urgent need to secure critical software in the AI ecosystem. This Action Pack guides AI practitioners to integrate robust security practices throughout the AI development lifecycle, focusing on mitigating novel vulnerabilities and supply chain risks for trustworthy and resilient AI systems.
5 Steps
- 1
Integrate Security into AI SDLC: Embed security practices across the entire AI development lifecycle, from initial data ingestion and model training to deployment and ongoing maintenance.
- 2
Mitigate AI-Specific Threats: Understand and implement defenses against unique AI attack vectors, including model poisoning, adversarial attacks, and vulnerabilities in open-source AI components.
- 3
Practice Secure AI Coding: Apply secure coding principles specifically adapted for AI models, algorithms, and underlying infrastructure components to prevent common and AI-specific exploits.
- 4
Scan AI Dependencies for Vulnerabilities: Regularly audit all third-party libraries, frameworks, and tools used in your AI projects for known security vulnerabilities and outdated versions.
- 5
Establish Continuous AI System Monitoring: Implement continuous security monitoring for deployed AI systems to detect and respond promptly to anomalies, security incidents, and potential attacks.
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