Safety and Security are fundamental, interdependent Trustworthiness Characteristics in the NIST AI RMF. Safety ensures that an AI system operates without causing undue physical, psychological, financial, or environmental harm under both normal and unexpected conditions. Security ensures the system is protected against malicious acts that seek to compromise its confidentiality, integrity, or availability.

This pairing addresses the spectrum of threats to an AI system's proper functioning, from unintentional failures and edge-case errors (safety) to deliberate attacks by adversaries (security). In the AI context, safety and security are deeply intertwined: a security breach (e.g., an adversarial attack on a vision system) can directly cause a safety failure (e.g., an autonomous vehicle misclassifying a stop sign). Therefore, a holistic risk management approach must consider both accidental and malicious pathways to harm, designing in resilience from the outset.

Achieving robust safety and security requires a multi-layered defense strategy:

Robustness & Reliability Engineering: Designing systems with fail-safes, redundancy, and the ability to handle edge cases and noisy data gracefully to prevent accidental malfunctions (Safety).

Adversarial Robustness: Hardening models against manipulation through techniques like adversarial training and input sanitization to resist attempts to cause malicious errors (Security supporting Safety).

Access Control & System Hardening: Implementing strict authentication, authorization, and network security measures to prevent unauthorized access or tampering with the AI system or its data (Security).

Incident Response & Recovery: Having prepared and tested plans to detect, contain, and recover from both safety-critical failures and security breaches to minimize harm.

Regulatory Context: The EU AI Act explicitly mandates both safety and security. Article 15 requires high-risk AI systems to achieve an "appropriate level of accuracy, robustness and cybersecurity," and to be "resilient against... attempts to alter their use or performance by exploiting system vulnerabilities." These requirements are core to the conformity assessment.

Non-Negotiable for High-Risk Deployment: For any AI system whose failure could cause significant harm, demonstrating rigorous safety and security is a legal and ethical imperative. It is the baseline for obtaining regulatory approval, insurability, and public acceptance for systems in domains like transportation, healthcare, and critical infrastructure.