Security-First AI: Foundations for Robust and Trustworthy Systems

TL;DR

The paper argues that AI security should be the foundational layer of trustworthy AI systems, outlining a security-first, hierarchical framework that treats security as distinct from safety while highlighting their interdependence. It surveys threat models (white-box, gray-box, black-box) and attack vectors (data poisoning, model inversion, adversarial examples, model extraction, membership inference), and reviews defensive techniques (adversarial training, differential privacy, robust architectures) and secure pipelines. A metric-driven approach—covering anomaly detection, vulnerability scoring, and resilience metrics—is proposed to guide continuous monitoring and adaptive defense across the AI lifecycle. The authors advocate a three-phase model (Security First, Safety Next, Continuous Improvement) and emphasize integrating security with safety to enable transparent governance and trustworthy deployment, while outlining future directions such as standardized benchmarks and zero-knowledge proofs for verifiable security without exposing proprietary details.

Abstract

The conversation around artificial intelligence (AI) often focuses on safety, transparency, accountability, alignment, and responsibility. However, AI security (i.e., the safeguarding of data, models, and pipelines from adversarial manipulation) underpins all of these efforts. This manuscript posits that AI security must be prioritized as a foundational layer. We present a hierarchical view of AI challenges, distinguishing security from safety, and argue for a security-first approach to enable trustworthy and resilient AI systems. We discuss core threat models, key attack vectors, and emerging defense mechanisms, concluding that a metric-driven approach to AI security is essential for robust AI safety, transparency, and accountability.