The Shadow AI Threat Trifecta

When employees use Shadow AI—that is, unauthorized, unvetted tools that the company is not overseeing or even aware of—it introduces risks, including around privacy and cybersecurity compliance. These risks rarely occur in isolation. Together, they often converge into a “trifecta” of threats that can undermine an organization’s regulatory compliance, weaken its security posture, and damage its reputation. 

Privacy Risk

One of the biggest risk factors with Shadow AI is the inability of organizations to maintain the privacy and data rights of data subjects, which the EU’s General Data Protection Regulation (GDPR) defines as “an identifiable natural person” who “can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier, or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person.” When AI tools and models are not formally integrated into the organization’s infrastructure, governance, and data inventories, it is nearly impossible to fulfill data protection mandates such as Data Subject Access Requests (DSARs). Additionally, the AI model itself may retain personal data. Once personal or sensitive information is uploaded into the tool(s), it is difficult or impossible to remove, making erasure requests under laws like GDPR virtually unachievable.

The problem, though, goes deeper than complying with DSAR requests. Even before a data subject can exercise their rights, organizations are required to provide notice of and obtain consent to processing their personal information. With the use of Shadow AI, data subjects may never be informed that their personal information may be used in AI processing. This likely violates multiple regulatory notice and consent requirements. 

Along with the risk of being unable to comply with data subject rights, the unauthorized use of AI technologies can lead to the unintended disclosure of PII and/or sensitive information, such as PHI. Employees may upload PII, PHI, or confidential business data without knowing where the data is stored or processed. When Shadow AI is in play, organizations may inadvertently facilitate the processing of data in jurisdictions with weaker privacy protections, creating compounded compliance risk—high-risk personal information being processed in a location with inadequate data protection.

Another major privacy concern is that data entered into unauthorized AI tools can also resurface in outputs, creating re-identification risks and violating core privacy principles such as data minimization. These risks are compounded by parallel cybersecurity concerns.

Cybersecurity Risk

The use of Shadow AI also poses significant cybersecurity risks. When employees use unapproved AI tools, sensitive data such as internal communications, intellectual property, or credentials may be exposed to systems that lack robust security controls, secure APIs, or defined data residency. Shadow AI does not go through a rigorous Third-Party Risk Management (TPRM) assessment, leaving blind spots that an organization will be unaware of. This increases the risk of data exfiltration and unauthorized access. This also expands organizations’ attack surface by enabling interactions with unmonitored external systems, opening new initial attack vectors that are unknown to existing security monitoring. 

Additionally, AI-specific zero-day vulnerabilities (that is, exploits that attackers use that are unreported), such as prompt injection or system jailbreaks, can trick users into inadvertently exposing enterprise data or systems. By its nature, Shadow AI usage is unauthorized, so organizations cannot know the specific software and technologies that must be in scope of their vulnerability management frameworks, meaning critical patches and exploit disclosures can’t be addressed. This unmonitored activity creates blind spots for incident detection and response, heightening the risk of breaches, regulatory penalties, and reputational damage.

Another concern is the upstream risk created by the AI supply chain. Many AI tools rely on third-party components, open-source models, and training datasets that may be altered or compromised without detection. Model poisoning, for example, occurs when an attacker deliberately manipulates the data used to train or fine-tune an AI model, embedding malicious patterns or backdoors that change how the model behaves. This can cause the AI to make incorrect or biased decisions, produce harmful outputs, or even exfiltrate sensitive information when triggered by certain inputs. Without formal oversight, organizations have no visibility into these dependencies or the controls in place to protect them.

The Trifecta

Shadow AI-related privacy and cybersecurity risks are pressing for organizations because they bring together three powerful risk vectors: regulatory exposure from noncompliance with privacy and security requirements, heightened security threats from unvetted and unmonitored tools, and reputational harm from the inevitable fallout of incidents that could have been prevented. This combination, fueled by the rapid and often invisible spread of AI tools within organizations, creates a complex and volatile risk environment.   

In our next post, we’ll talk about Shadow AI’s growing impact on contractual and IP risks, areas where we have significant experience supporting clients. Click here to read more about QuisLex Advisory.