On May 12, 2026, a specialized online event was held in the Intecracy Expert Webinar format, focusing on cybersecurity for corporate AI solutions. The event was organized by the Intecracy Group consortium and led by IQusion, an Intecracy Group member. Mykhailo Vihovskyi and Serhii Balashuk addressed the protection of models, data, integrations and user accounts in environments where artificial intelligence is already connected to business workflows.
For organizations working with complex infrastructure, architecture audits, modernization and implementation projects, the subject was practical rather than abstract. The discussion treated AI not as a standalone application, but as a component of a wider enterprise architecture that exchanges data with internal systems, external services and business-critical repositories.
AI inside the enterprise architecture
The speakers described how large language models and other AI tools are being embedded into document processing, CRM platforms, internal databases and external services. In this setting, the traditional security perimeter becomes less reliable. Data moves dynamically between systems, while risks appear not only at the network edge but also in prompts, model responses and integration APIs.
The webinar also highlighted AI-specific threats, including training data poisoning, prompt manipulation, unintended disclosure of information and authorization failures. Reliable protection therefore has to cover input data, model behavior and output results. Network traffic inspection alone is not sufficient when AI services become part of day-to-day corporate operations.
Zero Trust and IAM for every AI request
Mykhailo Vihovskyi explained the role of Zero Trust and identity and access management (IAM) in corporate AI environments. No user, internal process or third-party service should automatically be treated as trusted. Each interaction with an AI model needs verification of rights, context and, where required, multi-factor authorization.
“Modern corporate AI models operate with huge volumes of critical commercial information,” Mykhailo Vihovskyi noted during his speech.
From an infrastructure perspective, IAM is essential for building clear role models, separating access between departments and reducing the risk that a regular user interface could become a path to system settings or confidential corporate data. This is especially important when AI tools are connected to multiple enterprise platforms.
DLP and the security of integrations
Serhii Balashuk focused on data loss prevention. In corporate use cases, employees may send confidential documents, personal data or fragments of internal databases to AI services for analysis or report preparation. Without automated DLP controls, this information may leave the protected environment or be processed in a way that violates corporate security policies.
“The use of modern next-generation DLP systems is a mandatory element of corporate AI protection,” emphasized Serhii Balashuk.
The practical implication is that DLP should operate directly at the points where people and systems interact with AI. Sensitive fragments can be masked, removed or blocked before they are sent for processing. Integration security also remains critical, because APIs are among the channels through which hidden data leaks can occur.
Lessons for infrastructure modernization
The webinar positioned AI security as an ongoing process, not a one-time configuration task. A resilient corporate AI environment requires regular architecture audits, updated threat models, controlled integrations and the combined use of Zero Trust, IAM, DLP and dedicated AI Security practices. This approach aligns with modernization programs where new services must be implemented without weakening governance, visibility or data protection.
Additional context on the event is available in the Intecracy Group article on 1.
