Technological Characteristics of AI Systems

Introduction

Artificial intelligence is not merely another software tool, but a fundamentally different computational paradigm. Unlike classical algorithm-based systems, the behavior of AI systems does not arise solely from explicitly defined rules, but from statistical learning processes and data-driven representations. As a result, AI security cannot be considered a simple extension of traditional cybersecurity. Understanding the following technological characteristics is essential to recognize why new types of risks emerge and why specialized security approaches are required.

Non-deterministic and probabilistic behavior

Traditional software systems are deterministic: given the same input and system state, they always produce the same output. This property enables precise testing, reproducibility, and formal verification. In contrast, AI models exhibit probabilistic behavior, differing from the classical paradigm on multiple levels. Their operation is based on statistical estimation. For example, a large language model does not “know” an answer through explicit rules, but generates the output that is most likely according to the learned distribution given the input context. This inherently introduces uncertainty. Reproducibility is therefore limited. While deterministic execution is possible under certain configurations, commonly used parameters (e.g., sampling , temperature ) often result in different yet statistically consistent outputs. From a security perspective, this means that classical boundary testing cannot be directly applied.

A vulnerability may not be reproducible in every execution, making deterministic identification and validation more difficult.

Learning-based behavior and data dependency

In traditional systems, behavior is explicitly defined in code. In AI systems, behavior is implicitly derived from training data. This distinction introduces entirely new security dimensions.

In AI, data effectively acts as “source code.” The quality, representativeness, and integrity of training data directly shape the model’s decision patterns. Biased or manipulated datasets can distort system behavior at a fundamental level.

Decision-making is often non-transparent (black-box). Modern neural networks operate with billions of parameters, and their decision logic emerges from these internal representations, limiting explainability and causal analysis.

As a consequence, the attack surface expands significantly. Attackers may target not only the deployed system, but also the training and fine-tuning processes. This includes attacks such as data poisoning or the insertion of hidden behavioral patterns (e.g., backdoors).

Linguistic and abstract interfaces

One of the most important characteristics of modern AI systems is that the user interface is not based on structured data formats (e.g. SQL, JSON), but on natural language and multimodal inputs.

This shift fundamentally transforms the nature of the attack surface.

Some attacks operate at the semantic level. The input is not an executable file or a piece of code, but a natural language construct that exploits the model’s interpretation mechanism. This phenomenon can be understood as analogous to classical social engineering, with the key difference that the target is the model itself.

Control mechanisms are limited. While syntactic and type validation can be applied to structured inputs, in the case of natural language there is no comprehensive, deterministic validation mechanism capable of filtering out all malicious intent.

A direct consequence of this is the emergence of prompt injection, where instructions embedded in the input can influence or override the system’s original operational constraints. This is not “code execution” in the classical sense, but rather the manipulation of the model’s interpretation process.

Dynamic behavior and concept drift

Traditional software behaves statically: it changes only through explicit version updates. AI systems, however, may exhibit time-varying behavior driven by multiple factors. Some systems incorporate continuous learning mechanisms, such as feedback-based fine-tuning, making them adaptive but also introducing new risks.

While vulnerabilities in traditional systems are static programming errors, AI vulnerabilities are dynamic and context-dependent. Concept drift means that the distribution learned by the model gradually diverges from real-world conditions, causing previously safe outputs to become irrelevant or even harmful.

AI security is therefore a moving target. Due to the model’s internal associative structure, new semantic input combinations may succeed where previous attempts failed. The challenge is not finding a “buggy line of code,” but manipulating statistical probabilities to push the system beyond its intended logical boundaries.

This necessitates continuous monitoring, periodic reassessment, and ongoing validation as core elements of AI system security.