Security-First AI: Foundations for Robust and Trustworthy Systems

Security is foundational to all other AI safeguards: The paper argues that AI security—protecting data, models, and pipelines from adversarial manipulation—must be treated as a prerequisite layer that underpins safety, transparency, accountability, and alignment efforts. Without security protections in place, other responsible AI measures cannot be fully effective.

Security and safety are distinct but interconnected challenges: The paper distinguishes AI security (defense against external attacks and manipulation) from AI safety (ensuring systems behave as intended), emphasizing that both must be addressed but through different approaches and threat models.

Multiple attack vectors threaten AI systems: The paper identifies core threat models and key attack vectors targeting AI systems, suggesting that trustworthy AI requires understanding and defending against diverse forms of adversarial manipulation across the entire AI pipeline—not just the final model.

Metrics-driven approach is essential: The paper concludes that a metric-driven approach to measuring and monitoring AI security is critical for achieving robust AI safety, transparency, and accountability, implying that organizations need concrete, measurable security standards rather than general principles alone.

Resilience requires a holistic security strategy: By presenting a hierarchical view of AI challenges, the paper suggests that building trustworthy and resilient AI systems requires comprehensive security planning that addresses threats at multiple levels, from data integrity through model deployment.