AI Security vs. AI Safety: The Duality of Intentionality and Risk Management

Introduction

The risk management of artificial intelligence systems can be interpreted along two closely related but distinct dimensions: AI Security and AI Safety.

The analytical separation of these approaches enables a structured examination of different risk sources and control mechanisms, while it is important to emphasize that in practice they are not independent but rather overlapping and mutually reinforcing domains.

One of the fundamental differentiating aspects is the intentionality behind risk sources.

AI Security primarily focuses on threats arising from intentional, malicious interventions, while AI Safety addresses risks stemming from unintended, emergent, or system-originated behaviors.

However, this distinction is not absolute: in many cases, the same system characteristics may be relevant in both dimensions depending on the context.

1. AI Security: Defense in an Adversarial Environment

AI Security can be interpreted as an extension of classical information security, where the system is analyzed within an adversarial environment. The model implicitly assumes the presence of a rational and goal-oriented attacker who actively seeks to manipulate system behavior, compromise services, or extract valuable information.

Within this framework, the objective of defense is to ensure system security along the principles of confidentiality, integrity, and availability. However, in AI systems, these are extended with additional dimensions such as model integrity, output consistency, and the assurance of data provenance and reliability. The threat landscape also expands beyond traditional technical exploits to include methods targeting the statistical and semantic behavior of models, including input manipulation, data poisoning, and attacks aimed at reconstructing model behavior.

It is important to emphasize that AI Security is not limited to protecting traditional infrastructure but also extends to the behavioral attack surface of models. As a result, security mechanisms must address both technical and semantic attack vectors.

2. AI Safety: Managing Unintended and Emergent Risks

AI Safety focuses on ensuring reliable, predictable, and controlled system behavior even in the absence of an external attacker. In this context, risks originate from the statistical nature of models, the quality and biases of training data, as well as variability in environmental and usage contexts.

The central question of the safety dimension is to what extent system behavior remains aligned with defined objectives and normative expectations, particularly in situations that differ from the patterns observed during training. These include factual inconsistencies (hallucinations), statistical biases, and unexpected behaviors in rare or complex scenarios.

AI Safety is not limited to the concept of alignment, although it is a key component. Safety considerations also include robustness, reliability, and mechanisms for limiting and recovering from system failures. Furthermore, the safety dimension is often interpreted within a socio-technical context, taking into account user interactions and the operational environment.

3. Overlaps and Interactions: The Central Role of Robustness

Despite the differences between AI Security and AI Safety, the two domains are deeply interconnected in practice. A given system characteristic may simultaneously be relevant in both dimensions, and safety issues can transform into security vulnerabilities when intentionally exploited by an attacker.

A key concept linking the two is robustness, defined as the system’s ability to maintain stable operation under various disturbances. These disturbances may include both intentional adversarial interventions and unintended environmental or statistical variations. Robustness therefore represents a shared requirement across both security and safety domains.