Understanding and Governing Shadow AI in Healthcare

Artificial intelligence (AI) is rapidly becoming integral to modern business operations, driving innovation and efficiency across industries. However, the rapid and unregulated adoption of AI has led to a growing challenge for many organizations—Shadow AI.  

Shadow AI

The unauthorized use of AI tools within an organization, implemented without proper oversight or approval. Similar to Shadow IT, it often arises when employees bypass formal processes to address immediate business needs.  

Imagine your organization as a high-speed train racing toward innovation and growth. Artificial intelligence is the engine driving it, helping you move faster, make smarter decisions, and reach new goals. But like any train, speed without control can lead to trouble. 

Let’s discuss shadow AI in the context of healthcare.   

Why is Shadow AI Dangerous for Healthcare?

The rise of AI tools has made it accessible for employees to start using unvetted apps to drive productivity in their business functions, but it has raised new concerns about shadow AI, and how advanced technology is properly governed within an organization.  

Shadow AI is like a rogue switch on the tracks—unmonitored and unauthorized. It can cause the train to veer off course, heading toward uncharted and potentially dangerous territory. 

Without proper governance, Shadow AI can disrupt operational systems, throw compliance out the window, and damage trust with customers and patients.  

The Risks Shadow AI to Healthcare Organizations

Organizations often end up juggling multiple AI platforms, each with its own security policies, compliance standards, and data integration needs.  

This creates unnecessary duplication, wastes resources, causes operational misalignment, and poses risks to your healthcare organization.  

1. Data Security Risks: Shadow AI often bypasses IT and cybersecurity oversight, creating vulnerabilities like cyberattacks and data breaches, which is especially risky in healthcare, where protecting PHI is crucial. 
2. Compliance Challenges: Compliance is critical for industries like life sciences and payers, requiring strict adherence to regulations like GDPR, HIPAA, and HITRUST. Shadow AI poses risks by operating outside regulated systems, potentially exposing organizations to legal and financial liabilities.
3. Operational Inefficiency: Shadow AI leads to fragmented efforts, higher costs, and conflicting insights across departments, reducing efficiency and hindering clear, actionable outcomes.
4. Reputational Risk: Failure to manage Shadow AI can prevent an organization from safeguarding critical data or adhering to industry standards, resulting in reputational damage which is difficult and costly to repair.  

Strategies to Mitigate Shadow AI Risks

Effective governance frameworks and processes reduce shadow AI risks and protect organizational stability. Focus on foundational steps to stay ahead of potential risks. 

Reinforce Governance with Centralized Oversight

Organizations should focus on establishing a unified AI governance structure within their IT teams. Assign the responsibility of overseeing AI tools to the IT department, treating AI oversight as you would for any other vendor or technology.   

This includes maintaining basic operational hygiene, such as cataloging AI deployments, ensuring compliance with company policies, and monitoring for redundancy.  

Establish Basic AI Hygiene Practices

Mitigating shadow AI starts with “basic hygiene.” Good hygiene, in this context, is about reinforcing the fundamentals:  

• Account Protection: Implement multi-factor authentication (MFA) to enhance identity security and avoid vulnerabilities around unauthorized access to AI tools.  
• Identity Protections: Ensure users accessing AI platforms use role-based access controls, preventing excess permissions for employees who may not need critical data.  
• Data Safeguarding: Regularly monitor AI systems to ensure that data is securely stored, encrypted, and compliant with relevant standards.  
• Secure Configurations: Ensure that all AI platforms used within the organization adhere to robust configuration best practices, guided by IT teams or governance boards. 

Implement Training and Awareness Programs

Shadow AI is often a byproduct of convenience, as employees turn to unsanctioned tools to solve immediate problems for their individual functions. Employees should be educated about shadow AI, its security implications, and the best practices for properly integrating AI into their workflows.  

Organizations should empower employees by offering secure AI tools and sandboxes for innovation. 

Leverage Multi-Cloud Strategies with Intentional Design

AI workloads often exist across multiple cloud providers—a reality that exacerbates shadow AI when left unmonitored. Organizations can’t just secure one environment; they need to build a proactive security perimeter across AWS, Azure, GCP, or any other cloud platforms.  

Implementing centralized multi-cloud management helps streamline oversight without restricting innovation. 

Engage with Trusted Partners

Most organizations don’t have the in-house expertise to govern AI effectively. Some are hiring Chief AI Officers or dedicated governance teams to oversee AI use internally. Many are still defining where these roles—whether a Chief AI Officer or AI governance leader—fit within their organizational structure. 

Working with external specialists in cloud security, compliance, and monitoring can save time and help establish best practices. These experts bring cross-industry insights to help your business avoid costly mistakes. 

Governing Shadow AI in Healthcare 

Governance may not always sound exciting, but it’s not about stifling innovation—it’s about enabling businesses to deploy AI responsibly and sustainably. Without cohesive governance, even the most advanced AI tools will become liabilities rather than assets.   

Governance strategies are the train’s control center, equipped with advanced monitoring systems and skilled operators. Governance ensures that every switch on the track is accounted for, every route is planned, and every passenger (or department) is aligned with the train’s ultimate destination.  

An effective governance program that reinforces fundamental digital hygiene practices addresses the core issues behind shadow AI.  

Solving Shadow AI Challenges for Long-Term Growth  

At its core, shadow AI is not a problem to be feared; it is a challenge to be managed. Addressing it transparently benefits an organization’s long-term resilience by safeguarding sensitive data, ensuring compliance, and maintaining operational coherence.   

For healthcare providers, this means focusing on patient privacy and regulatory compliance.  For life sciences, it’s streamlining research processes without compromising data integrity.  For payers, it’s ensuring that predictive analytics used in service offerings remain accurate and secure.  

Ultimately, it comes down to practicing the same basic hygiene we’ve always applied when adopting new technologies. Every organization can transform shadow AI from a lurking risk into an opportunity for streamlined growth. 

Jim Ducharme, ClearDATA Chief Technology Officer