AI-based solutions are increasingly becoming part of business processes. Companies are implementing chatbots, AI assistants, RAG systems, copilots, document analysis tools and AI agents connected to corporate applications.<\/p>\n
These systems can speed up customer service, support employees, analyse documents, generate code, organise tickets and automate repetitive tasks. At the same time, they introduce new risks that cannot be assessed through classic application testing alone.<\/p>\n
A model may hallucinate, an application may handle context incorrectly, an agent may receive excessive permissions, and integration with corporate data may lead to information disclosure. That is why testing AI-based solutions should cover the entire system, not only the responses generated by the model.<\/p>\n
Testing AI-based solutions is the process of assessing systems that use artificial intelligence in applications, processes or corporate tools.<\/p>\n
In practice, we do not test only the AI model itself. We also verify:<\/p>\n
The purpose of testing is to check whether the AI solution works as intended, does not disclose data and does not perform actions that the organisation did not anticipate during implementation.<\/p>\n
In many projects, the focus is on whether the model provides correct answers. This is important, but not enough. An AI-based solution consists of more than just the model.<\/p>\n
Risk can appear in:<\/p>\n
An example from 2025 shows why a broader perspective is needed. CVE-2025-32711 described an AI command injection vulnerability in Microsoft 365 Copilot, which could allow an unauthorised attacker to disclose information over a network. This shows that the risk affects the entire environment in which AI uses organisational data.<\/p>\n
The first area is assessing the quality of responses generated by the AI system. The model should respond in line with the context, knowledge base and purpose of the application.<\/p>\n
During testing, we check:<\/p>\n
This is particularly important when an AI solution supports customers, employees, sales departments, helpdesks, HR, compliance or technical teams.<\/p>\n
AI-based solutions often work with company documents, knowledge bases, tickets, contracts, emails or repositories. In this model, it is necessary to check whether the system uses the right data and does not exceed access boundaries.<\/p>\n
Tests should answer questions such as:<\/p>\n
In RAG systems, response quality depends not only on the model. It also depends heavily on which documents were retrieved, how they were filtered and whether the user should actually have access to them.<\/p>\n
Security is one of the key elements of testing AI-based solutions. The system can be manipulated by a user, a malicious document, a crafted email or content retrieved from the internet.<\/p>\n
In security testing, we check:<\/p>\n
Prompt injection is particularly important in systems that retrieve data from external sources. An attacker does not need to enter a malicious command directly into the chat window. The instruction may be hidden in a file, webpage, comment or message that AI later processes.<\/p>\n
RAG systems connect a language model with an organisation\u2019s knowledge base. This allows AI to respond using current documents, procedures, reports or internal data.<\/p>\n
When testing RAG systems, it is worth checking:<\/p>\n
A good RAG system should be able to say that it does not know the answer. This is safer than generating a convincing but incorrect response.<\/p>\n
AI agents can perform actions, not only generate responses. They can send messages, create tickets, retrieve data, execute queries, run processes or use developer tools.<\/p>\n
This changes the way testing should be approached. An AI agent should be treated as an executable system component that requires control, limitations and oversight.<\/p>\n
When testing AI agents, we check:<\/p>\n
The OWASP Top 10 for Agentic Applications 2026 describes risks associated with autonomous and agentic AI systems that plan, act and make decisions in complex processes. This is an important direction for companies implementing AI solutions connected to business tools.<\/p>\n
An AI solution rarely works on its own. It is usually connected to an application, API, database, file system, CRM, ERP, email, helpdesk or developer tools.<\/p>\n
That is why testing should include integrations.<\/p>\n
We verify, among other things:<\/p>\n
In many cases, the problem does not come from the model itself. It appears only when AI is connected to data, tools and corporate processes.<\/p>\n
<\/p>\n
In 2025, CVE-2025-32711 was disclosed for Microsoft 365 Copilot. NVD described it as AI command injection that could allow information disclosure over a network.<\/p>\n
This example shows that AI solutions with access to organisational data require testing for prompt injection, context control, output filters and integration security.<\/p>\n
In August 2025, Anthropic described cases of Claude misuse, including the use of Claude Code to automate reconnaissance, obtain credentials and carry out actions in victims\u2019 networks. The company also indicated that AI was used to make tactical and strategic decisions during extortion operations.<\/p>\n
For organisations, this means that testing AI-based solutions should also include misuse scenarios. It is worth checking how the system behaves when a user tries to use it in a way that goes beyond its intended purpose.<\/p>\n
In 2026, agentic systems became a particularly important topic. OWASP indicates that agentic applications require a separate approach because AI can plan, make decisions and perform multi-step actions.<\/p>\n
This means that testing AI-based solutions must include permissions, memory, tool access, activity logging and control over agent decisions.<\/p>\n
Companies often focus on launching a solution quickly. Testing appears only when the system is already being used by employees or customers.<\/p>\n
The most common mistakes include:<\/p>\n
An AI solution may look good during a demo, but behave differently when exposed to production data, an unusual user or a complex business process.<\/p>\n
Tests should be carried out before production deployment, after significant configuration changes and after connecting AI to new data sources.<\/p>\n
Testing is especially valuable when an organisation:<\/p>\n
The best time to test is before production. At this stage, it is possible to improve architecture, restrict permissions and implement monitoring without costly changes to a live environment.<\/p>\n
At Cyberforces, we test AI-based solutions from the perspective of quality, security and resistance to misuse. We verify not only the model, but the entire system in which AI operates: the application, data, integrations, user roles, permissions and business processes.<\/p>\n
As part of testing, we can verify:<\/p>\n
We combine experience in penetration testing, security audits, red teaming and risk analysis. This allows us to assess whether an AI-based solution is ready to operate securely in a production environment.<\/p>\n
Summary<\/strong><\/p>\n Testing AI-based solutions helps verify whether a system works as intended, uses the right data and does not create uncontrolled risks for the organisation.<\/p>\n In 2025 and 2026, testing RAG systems, copilots, AI agents, integrations with corporate tools and resistance to prompt injection became especially important. These are the areas where the AI model connects with data, processes and business decisions.<\/p>\n If an AI solution is meant to support an organisation, it should be tested before production deployment. It is not enough to check whether the model gives correct answers. The entire system needs to be tested to confirm that it works securely in conditions similar to everyday use.<\/p>\n <\/p>\n It is the process of assessing systems that use artificial intelligence. It covers response quality, security, data access, integration behaviour, AI agent behaviour and resistance to manipulation.<\/p>\n Yes. The model is only one part of the system. In practice, the application, data sources, prompts, integrations, user roles, permissions and the behaviour of the entire solution need to be tested.<\/p>\n It is worth testing chatbots, RAG systems, copilots, AI agents, LLM applications, code generation tools, predictive models and AI solutions connected to corporate data.<\/p>\n Prompt injection is an attempt to manipulate how an AI model behaves using a specially crafted instruction. It can be entered by a user or hidden in a document, email, webpage or another data source.<\/p>\n The best time is before production deployment, after changing the model, after connecting new data and after adding integrations with corporate systems.<\/p>\n <\/p>\n Cyberforces tests chatbots, RAG systems, AI agents, LLM applications and integrations using artificial intelligence. We help detect errors, reduce the risk of data leakage and prepare AI solutions for production use.<\/p>\n","protected":false},"excerpt":{"rendered":" AI-based solutions are increasingly becoming part of business processes. Companies are implementing chatbots, AI assistants, RAG systems, copilots, document analysis tools and AI agents connected to corporate applications. These systems can speed up customer service, support employees, analyse documents, generate code, organise tickets and automate repetitive tasks. At the same time, they introduce new risks […]<\/p>\n","protected":false},"author":25,"featured_media":255151,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","inline_featured_image":false,"footnotes":""},"categories":[3],"tags":[],"class_list":["post-255150","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"yoast_head":"\nFAQ<\/strong><\/h2>\n
What is testing AI-based solutions?<\/strong><\/h3>\n
Is testing an AI solution different from testing the model itself?<\/strong><\/h3>\n
What AI solutions should be tested?<\/strong><\/h3>\n
What is prompt injection?<\/strong><\/h3>\n
When is the best time to test an AI solution?<\/strong><\/h3>\n
Implementing an AI-based solution? Check whether it works securely and as intended.<\/strong><\/h3>\n