Real-time AI Security: What the Market is Learning from Google

A market in transition: the context no one can ignore

In May 2026, TechCrunch published a significant analysisEven Google, among the most equipped players in the world regarding digital infrastructure, is navigating the challenges of real-time AI security. There is no definitive map yet. Everyone is learning as they build.

This data changes the perspective with which Italian SMEs should view the adoption of artificial intelligence. In fact, the dominant narrative tends to present big tech companies as holders of mature solutions. In reality, the landscape is more fluid and, in some ways, more democratic than it seems.

However, the fluidity of the context does not equate to an absence of risk. On the contrary, it makes risk more difficult to anticipate. SMEs that integrate AI into their operational flows — from digital marketing document management systems — they need to develop a new awareness. It's not enough to choose a certified tool: its evolution over time must be monitored.

The numbers that redefine risk perception

According to Gartner AI Hype Cycle 2025, more than 40% of organizations that have adopted generative AI solutions have already experienced at least one related security incident. Among these, the majority were not attributable to vulnerabilities in the model itself, but rather to integration and configuration errors.

Furthermore, a report by McKinsey on the Global AI Survey points out that less than 30% of medium-sized companies currently have a formal policy in place for the safe use of AI. This figure is particularly relevant for the Italian market, where the fragmentation of SMEs makes distributed governance even more complex.

In summary, the problem isn't just about tech giants. It concerns every organization that uses third-party APIs, cloud-based models, and LLM-based automation tools. Therefore, the risk perimeter has expanded far beyond the traditional boundaries of corporate cybersecurity.

Why is AI safety structurally different from classic cybersecurity

Traditional cybersecurity operates on relatively stable surfaces. A firewall protects a defined perimeter. An antivirus recognizes known patterns. AI security, on the other hand, must contend with systems that change their behavior based on data and context.

So, the most relevant attack vectors are not always technical in the classic sense of the term. The prompt injection, for example, exploits the linguistic nature of models to alter their output. The data poisoning compromises the quality of the upstream training set. These attacks do not necessarily require privileged access to the systems.

For this reason, even a company with solid IT infrastructure can be vulnerable if it has not considered the specificities of the AI layers it has integrated. Similarly, a tool for artificial intelligence applied to marketing it can become a risk vector if not managed with adequate policies.

In addition to this, legal liability is still in the process of regulatory definition in Europe. The AI Act framework introduces gradual obligations, but their practical application to SMEs still requires operational clarification.

What no one says openly: even vendors are flying by the seat of their pants

The Google case, reported by TechCrunch, is emblematic. This is not an isolated failure, but a systemic condition. AI vendors - even the most robust ones - release updates that change the behavior of models in ways that are not always predictable. Consequently, the security of an AI system is not a fixed state: it is a continuous process.

This has direct implications for those purchasing or integrating third-party AI tools. SMEs tend to trust the vendor's reputation and not monitor release notes. However, a silent update can modify data handling methods, retention policies, or model behavior in critical edge cases.

We of SHM Studio We observe this dynamic also in the context of digital campaigns. Advertising automation tools—such as those used for Google Ads campaigns or for the LinkedIn campaign — they increasingly integrate AI components. Therefore, the secure management of these tools is now the responsibility of those who configure and monitor them.

Strategic reading: three levels of exposure for Italian SMEs

Not all SMEs have the same risk profile. It is useful to distinguish three levels of exposure based on the degree of AI integration into business processes.

• Low level: Using SaaS tools with built-in AI (e.g., CRM with automatic suggestions, copywriting tools). In this case, the risk is mainly related to the management of data entered into the system and the vendor's policies.
• Intermediate level Integration via API of LLM models into internal operational workflows (e.g., automated responses, document analysis). Here the risk increases: prompt management, data transmission, and system logs need to be monitored.
• High level development or fine-tuning of proprietary models, or use of AI in critical decision-making processes (e.g., customer scoring, dynamic pricing). In this scenario, structured governance is necessary and, ideally, a dedicated AI security role.

Therefore, the first step for any SME is to conduct an honest assessment of its level of exposure. Only then can resources be allocated appropriately.

Operational Implications: Building Minimal and Scalable AI Governance

In the absence of definitive standards, the most effective response is not to wait. It is to build minimal, documented, and revisable AI governance. This does not necessarily require substantial resources. It requires method.

First of all, it's important to inventory all AI tools in use within the company—including those informally adopted by individual teams. SMEs often discover they have a greater exposure than they thought. Subsequently, it is necessary to define acceptable use policies, with particular attention to the management of sensitive data and customer data.

In addition, it is advisable to establish a process for periodically reviewing the AI vendors you use. Terms of service change, and data policies evolve. A review every six months is the bare minimum. Finally, it is helpful to train internal teams on the specific risks associated with AI, which differ from those of traditional cybersecurity.

From the perspective of digital presence, even strategies SEO and of digital marketing they are increasingly incorporating AI components. The quality of content produced with AI support—for example, through copywriting services — it also depends on the robustness of the processes with which these tools are used. Unsupervised AI output can generate inaccurate, misleading, or non-compliant content with editorial guidelines.

To delve deeper into the technical implications, it is also useful to consult the research from MIT Technology Review on AI and emerging risks, which offers an updated perspective on the systemic vulnerabilities of generative models.

The construction site that's still open: what scenarios are we moving towards

Projections for 2027-2028 indicate a progressive consolidation of AI security standards, driven by the application of the EU AI Act and pressure from insurance markets, which are beginning to price AI risk into cyber policies. However, the path toward mature standards will take years.

In the short term, it's reasonable to expect an increase in AI-related incidents, simply because more organizations are integrating these tools without adequate preparation. Consequently, SMEs that invest in governance and training today will find themselves in a better competitive position – not only in terms of security but also customer trust.

La Web infrastructure design secure and responsible AI management are integral to a mature digital strategy today. Those who understand this now will have fewer problems to manage tomorrow. To discuss this with our team, you can Contact SHM Studio to explore the available resources in blog.