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When we talk about artificial intelligence, the conversation usually revolves around models: GPT, Claude, Gemini. But after years working in technological infrastructure security, we have learned that the real risk is not in the model, but in everything that surrounds it. The orchestration layer, gateways, connection protocols with external tools, and credential storage systems have become the main target for attackers. And the worst part is that many companies still think their model is the most valuable thing, when in reality the infrastructure that supports it holds the keys to the kingdom.
In recent months, we have seen how vulnerabilities in components such as AI gateways allowed an attacker with a low-privilege role to escalate to control the entire system, access cloud provider keys, read conversations with sensitive data, and even modify the model's responses to execute malicious commands on developers' machines. These incidents were not due to flaws in the AI model, but to architecture problems: layers communicating with each other with incorrect assumptions, fields disappearing when passing from one middleware to another, and design decisions that prioritize flexibility over security.
The lesson is clear: AI security is not solved by fine-tuning the model, but by reviewing how all components connect. At Q2BSTUDIO we offer cybersecurity and pentesting services specialized in this new scenario, where we analyze not only the applications, but the relationships between them. Because a failure is not in a line of code, but in the mismatch between two systems that should trust each other and do not do so correctly.
The industry has started to talk about orchestration security, but we are still in the early phase. Many organizations deploy AI agents, gateways, and MCP servers without an adequate threat model, assuming that model security is sufficient. Nothing could be further from the truth. An AI gateway typically has visibility over all API keys, routing policies, conversation logs, and credentials of connected systems. It is a single point of failure that, if compromised, exposes the entire infrastructure.
For companies adopting artificial intelligence, the first step is to recognize that AI infrastructure is as critical as the business itself. We recommend treating gateways and orchestrators as if they were identity or IAM systems. This implies applying security patches with the same urgency as in internet-exposed systems, auditing permissions between layers, and ensuring that no component can escalate privileges without control.
At Q2BSTUDIO we develop AI solutions for companies that integrate these good practices from the design phase. We create custom applications and custom software that not only meet functional requirements but also incorporate a secure-by-default architecture. We work with AWS and Azure cloud services to deploy scalable infrastructures, and use business intelligence tools like Power BI to provide visibility into system performance and security. Additionally, we develop AI agents that operate within defined security boundaries, preventing a failure in communication between components from becoming a breach.
The paradigm shift is profound: before we protected data, now we protect execution. An attacker no longer just wants to read information; they want to influence what the system does. They can modify a call to a tool, reroute a request, or promote a user to administrator. The only way to defend is to understand that AI infrastructure is the new center of gravity for enterprise security. Let us ask ourselves, as a good security professional does: what could go wrong? And let us act accordingly before attackers do it for us.
