Shadow IT 2.0: Shadow AI and the Risks of Artificial Intelligence in Companies

The advancement of AI in companies is inevitable and, in many cases, uncontrolled. Tools like ChatGPT, Copilot, or Midjourney have become so accessible that today any employee can use them without going through IT, without legal review, and without anyone knowing how these tools are processing the data that is being entered.

This phenomenon is what we call Shadow AI. It is a direct evolution of the well-known shadow IT, but with exponentially more critical impacts. Because now we are not just talking about parallel SaaS platforms or undocumented scripts. We are talking about algorithms that learn from your data, without permission and, even worse, without control.

For technology leaders in mid-sized and large companies, this is not exactly new. But the urgency is real: the risks of artificial intelligence in companies are not theoretical. They are operational, daily, and already costing organizations dearly.

Shadow AI and the new data leakage landscape

If shadow IT was already a headache, Shadow AI takes risk to an entirely new level. The unauthorized use of generative AI can lead to sensitive data leaks, and this is happening more often than many executives realize.

Imagine this scenario: a marketing analyst pastes customer data into an AI tool to speed up campaign creation. Or a developer copies proprietary code into a tool like ChatGPT to seek optimization. Once entered, this data is processed on external servers, and it is not always possible to guarantee where, how, or for how long it will be stored.

Passwords, customer lists, confidential contracts, and even business strategies are slipping through the cracks of Shadow AI.

Risks already impacting large enterprises

At Zallpy, we closely monitor how artificial intelligence is being used outside official channels, and the risks this creates:

• Data leakage: transferring information to third-party servers exposes data that should be under strict control.
• Non-compliance with LGPD and other frameworks: the lack of control over what is sent to AI tools violates compliance requirements.
• Fragmented environments: each team uses whatever tools they want, however they want, creating an ecosystem that is nearly impossible to audit.
• Shadow IT out of control: parallel technology use was already an issue, but with AI, the impact multiplies, because the machine learns from mistakes.

The lack of AI governance opens gaps that compromise the core of the business: intellectual property, reputation, and security.

The false sense of security of total prohibition

Here is an uncomfortable truth: trying to block AI usage across business areas would be like banning internet access in the 2000s. It will not work, and it will only slow your company down.

The question is no longer whether your employees are using AI. They are.

The real question is: how do you ensure this happens with security, traceability, and strategic intelligence?

Three pillars for structuring AI usage in companies

AI must be treated as a continuous digital transformation program, built on three essential pillars:

• Clear and realistic guidelines
  Usage policies that balance freedom and responsibility without stifling innovation.
• Secure and authorized tools
  AI solutions with traceability, permission controls, and integration with the company’s core systems.
• Continuous enablement of leaders and squads
  Internal programs to educate, raise awareness, and promote the intelligent use of technology.

Because the risks of artificial intelligence in companies are not caused by AI itself, but by the absence of governance.

Shadow AI is an undeniable reality. How will you respond?

Shadow AI is not a future threat. It is a present phenomenon that has been silently expanding the reach of shadow IT and exposing companies to data leaks, password leaks, and serious legal risks.

But it is also a signal. It shows that teams want to be more productive, that they are seeking autonomy and innovation.

In this context, more than building barriers, the real challenge for IT leadership is to foster a culture where AI is used with responsibility, purpose, and governance. This requires clear policies, mature processes, and, above all, continuous awareness of the boundaries between operational efficiency and the exposure of critical assets such as sensitive data and intellectual property.

In times of accelerated transformation, structuring the use of AI is no longer an optional initiative, it is part of the strategic core of any digitally mature company.