As companies integrate artificial intelligence into their business processes, the attack surface is also expanding. Traditional cybersecurity measures are not enough to recognize and stop AI-specific threats. So what has changed in this new threat environment and how should we take action?

AI security is an interdisciplinary field of security that encompasses the protection of artificial intelligence systems, their training data, and supporting infrastructure against unauthorized access, manipulation, and abuse. A departure from classic cybersecurity is this: Attacks target not only technical vulnerabilities, but also the learning and inference mechanisms of the model. Without getting into the system, an attacker can only manipulate the behavior of the model, causing critical damage.

Table of Contents

• Why AI Security Creates a Different Threat Environment
• What are the AI threats that stand out in 2026?
• What is Prompt Injection and Why Is It So Dangerous?
• How Does Data Poisoning Work?
• What measures should be taken to ensure the security of artificial intelligence?
• Which Industries Does AI Security Directly Affect?
• TL; DR
• consequence

Why AI Security Creates a Different Threat Environment

Short answer: Because AI systems operate probabilistically, unlike conventional software, which gives rise to entirely new attack vectors.

In a classic application, the vulnerability usually lies in a specific line of code; the patch is made, the problem closes. In AI models, the threat is much more diffuse and implicit. The model is a statistical structure formed by billions of parameters. Having discovered how to “convince” this structure, an attacker can redirect the system in the desired direction without bypassing the firewall.

According to Cisco's State of AI Security Report 2026, eighty-three percent of companies plan to integrate agentic AI capabilities into their business processes, while only twenty-nine percent say they are truly ready to use these technologies securely. This figure makes it clear how speed pushes safety into the background.

Moreover, as the integration of AI into workflows deepens, the distance between disrupting the behavior of a model and infiltrating a critical system is closing. From customer service bot to medical decision support system to supply chain optimization to financial risk assessment, reliance on AI is growing at every point. As this addiction grows, so does the goal.

What are the AI threats that stand out in 2026?

In the field of artificial intelligence security, several critical threat categories documented as of 2026 stand out.

Prompt injection remains the most common and most active threat faced by systems using major language models (LLM). Ranked number one on OWASP's 2025 LLM and Top 10 Risks for Productive AI Applications, this threat involves an attacker manipulating the behavior of an AI tool to disable the system's original purpose or security barriers.

Data poisoning is a form of threat in which attackers target the training process of the model and place harmful samples in the training data, enabling the model to produce erroneous or deliberately false output under certain conditions. This type of attack carries serious risk, especially in systems that are constantly learning and models based on open source datasets.

Model inversion is a technique for recovering sensitive information from training data by observing the inputs and outputs of a model. Models trained in privacy-critical areas such as health or finance are particularly vulnerable to this attack.

RAG poisoning targets Retrieval-Augmented Generation architectures to which the model generates responses by accessing external resources. The attacker places malicious content on the data sources that the model queries, enabling the model to treat that content as a trusted source.

Finally, supply chain attacks include compromising model weights, open source libraries, and model development tools. Instead of using a model, attackers poison the vehicles that build the model.

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What is Prompt Injection and Why Is It So Dangerous?

Prompt injection is an attack method in which an attacker sends a specially prepared input to the artificial intelligence system, allowing the model to ignore the system instructions and execute the attacker's commands. Although it sounds theoretical, recent documented cases show just how tangible this threat is.

In a direct prompt injection attack, the attacker tricks the system using the AI interface directly. In the indirect version, the attack is carried out through instructions hidden in a web page, email, or document processed by the model. By 2026, indirect prompt injection has become the dominant attack vector; in this method, malicious instructions are placed not in the user's direct input, but in data such as web pages, documents, emails, or database outputs received or processed by the model.

Google's researchers have identified a remarkable trend in this regard: Between November 2025 and February 2026, a thirty-two percent increase in malicious prompt injection attempts was observed. Although the level of complexity is still low, the scale and sophistication of the attacks is expected to increase.

What makes this threat even more alarming is the proliferation of agent-based artificial intelligence systems (agentic AI). If an AI agent is reading email, processing documents, and accessing the web, it is enough to intercept a malicious command agent placed on any of these resources. So prompt injection is no longer just a chatbot vulnerability, but a systemic risk that threatens enterprise infrastructure.

How Does Data Poisoning Work?

Data poisoning is a particularly dangerous category of attacks that take place during the training phase of the model and due to its long-term consequences.

The basic logic works as follows: The attacker intentionally places erroneous, misleading, or malicious examples in the dataset to be used in the training of the model. When the model is trained with this poisoned data, it begins to misbehave as predicted when faced with certain trigger inputs; but in other cases it appears completely normal. This feature makes it extremely difficult to detect the attack.

Backdoor attacks are the most sophisticated form of this category. The attacker teaches the model a hidden “trigger”. For example, by adding a specific pixel pattern to training images, the model can be conditioned to respond with incorrect classification to inputs containing this pattern in the real environment.

The open source model ecosystem represents a particularly critical dimension of this threat. Teams using datasets to which anyone can contribute, or model weights shared by the community, are at serious risk without source verification.

What measures should be taken to ensure the security of artificial intelligence?

AI security cannot be achieved with a single tool or the responsibility of a single team. An effective approach should cover more than one layer.

Security in the model development phase begins with data validation. Recording the provenance of training datasets (which source they came from), performing threat modeling for open source components, and implementing anomaly detection in the training process are key requirements. The principle of secure-by-design provides for placing security at the beginning of the development cycle.

Input validation and sanitization are critical during the deployment phase. Especially in systems that access external sources, each input must be passed through security filters before reaching the model. The principle of least privilege should also apply to AI agents: an agent should not have access to more systems or data than is necessary for his task.

Continuous monitoring constitutes the most frequently neglected dimension of AI security. Model behavior can shift over time or go quietly before an attack is detected. Monitoring model outputs in the production environment, identifying abnormal patterns, and conducting regular red team exercises ensure the continuity of defense.

Finally, without a governance structure, the impact of technical measures remains limited. Risk classification, model inventory management and attack response plans of AI systems must be defined at the enterprise level. McKinsey's research shows that companies with AI governance frameworks can respond to cyber incidents, on average, forty percent faster.

Which Industries Does AI Security Directly Affect?

AI security vulnerabilities are not an abstract risk; they are a reality with tangible operational and financial consequences in specific industries.

AI-powered customer service and fraud detection systems have become a primary target in the financial sector. The interception of a customer service bot via prompt injection can open the door for an attacker to access sensitive account information. In one documented case, attackers managed to access customer account information by bypassing security filters through documents containing secret instructions that they uploaded to a financial institution's AI-powered customer service system.

In the healthcare sector, AI diagnostic tools and clinical decision support systems are high-value targets at risk of manipulation. Distorting the output of a diagnostic model can directly affect treatment decisions.

Attacks on artificial intelligence systems in the public and defense fields have the potential to have consequences at the national security level. This is why AI deployments in this industry are subject to the strictest security protocols.

In the retail and e-commerce sector, manipulation of recommendation engines and pricing algorithms can cause serious damage on both the corporate and consumer dimensions.

TL; DR

AI security has become an independent and critical sub-area of cybersecurity by 2026. Prompt injection, data poisoning, model inversion, and RAG poisoning are the major threat categories. Conventional security measures alone are not sufficient to meet these threats. Effective defense requires a multi-layered approach, ranging from model development to production, monitoring to governance. For organizations, the question is now “do I need AI security?” not, “how do I set this up correctly?” must be.

consequence

As AI systems become central to the business world, protecting these systems is becoming a strategic imperative. The scale of prompt injection attacks is growing, threat actors are actively exploring AI-specific vulnerabilities, and many organizations are not yet ready for this transformation. Leaving security to the end of the process of improving security or relying solely on traditional tools is no longer an acceptable approach in this environment.

Delaying investment in AI security means not only a technical risk, but also widening a gap that can have critical consequences for reputation, operational continuity and regulatory compliance.

Do you want to assess the security maturity of your AI systems and build your threat model? Schedule an exploratory meeting with our experts.

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Sources

IBM, “What Is AI Security?”