On May 12, 2026, a specialized online event was held in the format of the Intecracy Expert Webinar, dedicated to one of the most discussed topics of today — the cybersecurity of corporate AI solutions. The event was organized by the Intecracy Group consortium, which regularly hosts analytical and practical meetings for business representatives. The main goal of this webinar was a detailed analysis of architectural approaches to protecting artificial intelligence in the corporate sector. The speakers focused on the protection of models, data, integrations, and user accounts.
The event was led by IQusion, an Intecracy Group member.
A New Security Perimeter: Challenges of Artificial Intelligence Integration
The rapid implementation of large language models (LLMs) and other artificial intelligence tools into daily business processes creates not only new opportunities for automation but also serious threats to corporate security. Traditional perimeter security tools prove ineffective when dealing with dynamic data flows within modern AI systems. Artificial intelligence security (AI Security) requires a complete rethinking of classical approaches and the introduction of new technological standards for effective protection against specific attack vectors, such as deliberate training data poisoning and prompt injection.
Corporate artificial intelligence cannot function as a fully isolated system. It integrates deeply into internal databases, CRM platforms, and various external services. Consequently, any undetected vulnerability in application programming interfaces (APIs) or outdated authentication systems can lead to rapid compromise of the entire corporate network. The webinar speakers emphasized that reliable protection must be built simultaneously on three levels: input data security, continuous control over the behavior of the model itself, and automatic monitoring of output results.
The Role of Zero Trust and IAM in Protecting Artificial Intelligence
One of the key tools for minimizing such risks is the concept of Zero Trust combined with strict identity and access management (IAM). In the context of modern AI solutions, this means that no user, internal process, or third-party integrated service should have an automatic trusted status. Every single request to the artificial intelligence model must undergo rigorous dynamic verification and multi-factor authorization.
"Modern corporate AI models operate with huge volumes of critical commercial information. If we do not provide strict access control at the IAM level, the system may accidentally disclose confidential data to users who do not have the appropriate rights. The Zero Trust concept requires that every request to the AI infrastructure be verified as if it comes from a fully open and unprotected network. This is the only reliable way to prevent unauthorized access to our company's intellectual property and personal data," Mykhailo Vihovskyi noted during his speech.
The implementation of IAM allows for a clear separation of access rights for different categories of employees interacting with artificial intelligence technologies. This reliably prevents situations where, through a standard user interface, an attacker or an unauthorized employee could gain access to deep system settings or confidential company information.
Data Loss Prevention (DLP) and Integration Security
Another critically important aspect of security is protection against information leakage using DLP systems. When using corporate AI solutions, employees often upload confidential documents to the system for quick analysis or report preparation. Without proper automated control, this data can enter the public domain or be used to train public models, which is a gross violation of corporate security policies.
"The use of modern next-generation DLP systems is a mandatory element of corporate AI protection. We must not just control standard data transmission channels, but also analyze the content of requests sent to artificial intelligence models in real-time. This allows us to block the transfer of commercial secrets or personal data outside the secure perimeter in a timely manner. Special attention should be paid to the security of integrations, as hidden data leaks most often occur through APIs," emphasized Serhii Balashuk.
Modern DLP solutions integrate directly into AI interaction interfaces, allowing sensitive data to be automatically masked or removed before it is sent for processing. This significantly minimizes the risks associated with the human factor and ensures full compliance with international regulatory requirements for personal data protection.
A Comprehensive Approach to Building a Reliable AI Infrastructure
Summarizing the webinar, the experts came to the joint conclusion that artificial intelligence security is not a one-time project or a simple software setup. It is an ongoing process that requires regular auditing, updating threat models, and adapting security systems to new types of cyberattacks. Only a comprehensive combination of Zero Trust, IAM, DLP, and specialized AI Security tools can guarantee the stable and secure operation of modern business.
The Intecracy Group consortium continues to actively develop its expertise in cybersecurity and help organizations build resilient IT infrastructure. The event demonstrated the extremely high interest of the professional community in artificial intelligence protection issues, confirming the importance of further discussion and implementation of best practices in this dynamic field.