The term AI security does not have a single, unambiguous meaning. On the one hand, it refers to the use of artificial intelligence systems to strengthen an organization’s security posture; on the other hand, in a stricter sense, AI security encompasses the set of strategies, tactics, and tools aimed at protecting artificial intelligence systems, which are now at the heart of growth and innovation for all enterprises.
In this article, we focus on the latter meaning: we frame AI security within the broader context of risk management, analyze the main attack techniques, and outline the most effective strategies.
Key Points
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The central role of AI systems in business processes requires the development of dedicated AI security strategies and the adoption of appropriate tools.
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Threats are not theoretical: prompt injection, data poisoning, model extraction and attacks on the model supply chain are real and make AI systems high-value targets.
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AI security does not rely on a single control, but requires coordinated protection of all system components: data, models, interaction methods and infrastructure.
AI risk, governance and security: the new pillars of the 2.0 enterprise
With the integration of AI into organizations’ core processes, new risks have emerged that are structurally different from those associated with traditional systems. The specific characteristics of AI systems, from continuous learning to content generation, require a rethinking of risk management frameworks.
Reflections on AI risk management mainly focus on two major areas: AI governance and AI security in the strict sense. These are distinct yet closely interconnected dimensions.
AI Governance: what it is and why it is essential to trust AI
AI Governance includes a set of rules, processes, and controls designed to ensure that AI systems are reliable, compliant with corporate policies, and accountable. Any IT system enhanced by an AI model must ensure regulatory compliance, bias management, prevention of unintended harm, and control over how models generate outputs.
In the absence of a robust and effective AI Governance framework, organizations expose themselves to potentially critical consequences:
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a diagnostic support system trained on incomplete or unreliable data could produce incorrect clinical assessments;
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an online pricing or sales engine could apply indiscriminate discounts, eroding margins and value;
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an HR system based on historically biased data could crystallize and amplify pre-existing biases, systematically influencing recruitment processes.
In all these cases, the issue is not an external attack, but an “internal” malfunction of the model, linked to data quality, design choices or the absence of adequate control mechanisms.
AI security: what it is and why it is a priority for every company
Within the broader risk management perimeter lies AI security, understood as the set of practices and controls used to protect AI systems from intentional attacks or misuse — that is, from threats capable of altering model behavior, limiting functionality, compromising operational continuity or exposing sensitive data and information.
AI security focuses on prevention, detection, and response to threats, regardless of whether their origin is internal or external to the organization, and whether the event is deliberate or accidental. The goal is to preserve the overall reliability of the system, preventing technical or operational vulnerabilities from translating into concrete impacts on the business and/or corporate reputation.
AI security: what to protect and from which threats
An IT system that provides information or makes decisions based on an artificial intelligence model can be attacked just as a traditional, deterministic, or rule-based system can. The difference is that, in the case of AI, the attack surface expands and becomes more dynamic. Moreover, attackers can use AI itself to orchestrate more sophisticated and innovative operations.
Model, data, infrastructure: what to protect in practice
In AI security, the object of protection is not a single component, but a complex ecosystem composed of tightly interconnected models, data, interfaces, and infrastructures.
- The first level to protect is data. AI systems are data-driven by definition: the quality, reliability, and security of training, validation, and inference data directly determine the quality of outputs.
- The artificial intelligence model must also be protected through the measures discussed below. Models can be stolen, copied, manipulated, or induced to behave abnormally.
- The security of the underlying infrastructure must not be overlooked. Cloud environments, containers, storage systems, and networks form the foundation on which AI models and pipelines run.
How is an AI system attacked? Five typical cases
La disciplina della AI security è giovane, ma l’appetibilità economica dei sistemi ha accelerato ricerca e innovazione sul fronte offensivo, e inevitabilmente anche sul versante della difesa. Sono emerse alcune categorie di attacco ricorrenti.
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Prompt injection attack
This is a form of social engineering in the AI era, in which the attacker induces the model (usually a language model) to ignore rules or reveal sensitive information. According to OWASP reports, it is among the most frequent threats in the context of large language models (LLMs).
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Data poisoning
This technique involves altering the model’s training or update data. The principle is simple: given the scale of the datasets used for model training, even a small distortion can generate significant effects.
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Denial of Service (DoS)
These are attacks aimed at saturating resources and interfaces, compromising the availability of the AI system.
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Malware
AI systems can be infected with malware just like traditional systems (trojans, backdoors, malicious code).
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Extraction
So-called model extraction consists of techniques aimed at extracting, through repeated queries, information that the system should not share — or even the model itself.
All these threats, along with others that are less frequently employed, define a risk perimeter that makes AI security an essential component of modern enterprise IT strategies.
How to secure an AI system: an end-to-end approach
As mentioned, an artificial intelligence system can be viewed as the combination of several fundamental components, including data, the model, the infrastructure, and the way the system is queried and/or used. Securing an AI system, therefore, means developing a strategy that addresses all these dimensions in a coordinated manner.
How to secure data
The starting point is a robust data discovery and classification activity, essential to understand which data is used, where it resides, and what level of criticality it presents. On this basis, fundamental technical measures must be implemented, such as encryption, strict access controls, and continuous monitoring of information flows to detect anomalous behavior or signs of compromise.
Second priority: defending the model
Few companies develop their own AI models; in most cases, third-party models or their derivatives are used. This makes trust in the source a central issue — a complex one, considering that organizations often use multiple models simultaneously, sometimes modified or fine-tuned versions. It is therefore necessary to apply supply chain security principles to AI models as well, verifying and testing their provenance, versions, and updates.
Particular attention must be paid to API security, as interaction between applications and models almost always occurs through APIs. Careful privilege management and the adoption of RBAC (Role-Based Access Control) help reduce the risk of abuse or unexpected behavior.
Securing operational interaction
The third dimension of security concerns how AI is used and queried, as this is where phenomena such as prompt injection originate. Defense primarily relies on input monitoring to identify suspicious patterns or manipulation attempts, as well as on the introduction of controls and guardrails capable of limiting the model’s scope of action.
Never overlook infrastructure
Artificial intelligence systems almost always operate in cloud or hybrid environments, making extensive use of containers, distributed storage, and high-performance networks. Any vulnerability at this level risks propagating downstream, compromising the entire ecosystem.
It is essential to apply cloud security practices to AI workloads as well: network segmentation, system hardening, secure identity and privilege management, continuous patching and configuration control. Particular attention should be paid to containerized environments and MLOps pipelines, which introduce new points of exposure if not properly governed.
Kirey, your partner for effective and secure AI solutions
At Kirey, we address AI security starting from solid experience in software development, gained in enterprise and mission-critical environments, where the security-by-design paradigm is not optional.
We combine this with specialized expertise in artificial intelligence and a dedicated cybersecurity division, with competencies in governance, infrastructure protection, threat prevention and threat management. The synergy among these skills enables us to address AI security in an end-to-end manner, integrating technical protection, risk control, and operational continuity.
Contact us to find out how we can embark on this journey together.
