*By André Dias
Sensitive data leakage is one of the most critical issues in the current AI landscape. As AI becomes increasingly integrated into business processes, the lack of adequate controls can result in unwanted exposures of confidential information. According to consulting firm IDC, spending on AI and generative AI-related services is expected to exceed US$459 million this year, highlighting the accelerated growth of this technology. However, for 37% of companies, the application of AI is limited by uncertainty regarding the security and protection of data used in these models.
In this context, Shadow AI emerges as an even more alarming concern. These are artificial intelligence systems developed and operated independently, outside of corporate governance structures or formal oversight. Although often created to solve specific problems and optimize decisions, these systems pose serious risks due to the lack of control and transparency, compromising the security and reliability of organizations, which are often unprepared to deal with the implications of this unregulated technology.
Because they are created outside the scope of organizational governance, these systems do not undergo formal audits, validations or regulations, making it difficult to understand how these systems work, how they make decisions and how the data used and generated is processed. This can result in errors that go unnoticed, leading to decisions based on distorted information. The problem is amplified by the lack of adequate documentation, which creates dependence on specific developers and makes it impossible for the organization to maintain effective control over the technology.
Shadow AI also carries the risk of algorithmic bias. AIs are trained on historical data, which often reflect biases and inconsistencies. In a scenario where supervision is absent, as is the case with Shadow AI, these biases can be amplified, leading to discriminatory or harmful decisions.
Amplified risks in cybersecurity
The cybersecurity context adds an extra layer of complexity to the issue of Shadow AI, making it an even more critical threat to organizations. A lack of governance and oversight in Shadow AI systems can result in the exposure of sensitive data, putting confidential information at risk and violating strict regulations such as LGPD and GDPR. Without proper monitoring, these systems can not only cause direct harm to individuals’ privacy, but also open up vulnerabilities for cyberattacks, such as data breaches or exfiltration, which can be exploited by cybercriminals, exposing the organization to significant financial and reputational risks.
Governance becomes even more urgent as Shadow AI expands beyond generative models. While often associated exclusively with generative AI, which creates new concepts from historical data, Shadow AI can also emerge in traditional systems that mimic and replicate existing patterns. The point of concern is not the model itself, but the lack of clear processes to ensure the security, confidentiality, and integrity of information.
We can draw an interesting parallel between the current Shadow AI challenge and the Shadow IT phenomenon that marked the last decade. Just as IT departments had to deal with systems and solutions deployed outside their supervision, the emergence of Shadow AI requires the implementation of robust and adaptive governance. This includes the creation of clear regulations, system approval, and investment in training and staff development.
If not managed proactively, Shadow AI can compromise not only cybersecurity but also the reputation and sustainability of a company’s operations. Balancing technological innovation and responsible governance is key to navigating this new paradigm without risk. Organizational leadership must prioritize transparent and ethical practices, ensuring that the use of artificial intelligence, even in its most independent forms, is aligned with institutional values and objectives.
*André Dias is an Engineering Sales Specialist at Adistec Brasil, an IT distributor specializing in infrastructure solutions for Data Centers and Information Security.
Notice: The opinion presented in this article is the responsibility of its author and not of ABES - Brazilian Association of Software Companies
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