AI’s Growing Footprint in Healthcare: Managing Third-Party and Compliance Risks

Healthcare organizations (HCOs) of all sizes are integrating AI models into their workflows to augment provider capabilities. From speeding up administrative tasks like clinical documentation to leveraging advanced models for diagnosis support and patient engagement, AI holds immense potential to alleviate certain burdens in an industry plagued by burnout. However, the implementation of this technology carries multiple risks that must be considered and promptly addressed.

As AI adoption accelerates, so do compliance, security and governance challenges. HCOs managing HIPAA-protected patient data, third-party AI integrations and regulatory requirements must take a proactive approach to risk management. Without a strong governance framework, the promise of AI-driven efficiencies can quickly turn into new vulnerabilities that threaten an organization’s security posture.

The Growing Risk of AI in Healthcare

According to the Deloitte Center for Health Solutions, more than 75% of leading healthcare organizations plan to scale AI across their enterprise. With every new AI-driven tool introduced, the risk network expands, especially when third- and fourth-party vendors get involved with data handling, decision-making and patient interactions.

Without structured governance, health systems face three critical risks:

• Data Security – AI systems process massive amounts of sensitive patient data, making them a prime target for cyberattacks and unauthorized access. If AI-powered automation lacks proper security controls, health systems risk exposing electronic protected health information (ePHI) and violating HIPAA regulations.
• Regulatory Misalignment – With federal AI regulations currently in flux, states are advancing a piecemeal set of laws, each aiming to promote responsible AI use within their jurisdiction. This fragmented approach creates compliance challenges for healthcare AI developers and deployers, who need an agile system to track evolving requirements in real time and maintain a feedback loop for leadership to monitor where and how AI models are being leveraged.
• Operational Disruptions & Erosion of Trust – Poor risk management can have severe downstream consequences, from potential cyberattacks that cripple hospital operations to compliance failures that spark legal action and reputational fallout. When technology systems lack proper oversight, patient safety and care are compromised and public trust erodes, leading to a loss of business as patients and partners seek more secure alternatives.

AI Governance Challenges: Managing Business Associates & Compliance Risks

A major challenge for health systems is their reliance on third parties for business services, creating multiple vectors for cyber intrusion. The integration of AI-driven tools through external providers adds another layer of risk and compounds the preexisting difficulties with tracking and managing risks across the third-party ecosystem.

For example:

• A health system using AI-driven revenue cycle management software must ensure that patient billing and claims processing remain HIPAA-compliant.
• AI-powered patient monitoring tools must comply with data privacy laws while ensuring that automated decision-making does not introduce bias or errors in patient care.
• Third- and fourth-party AI vendors handling PHI are business associates and must be continuously assessed for compliance, security posture and contractual obligations.

These AI-driven processes can quickly become a compliance and risk liability if not monitored and held to a strict governance policy.

Proactive AI Governance: Three Strategies for Risk Management

HCOs need a structured governance, risk and compliance (GRC) approach to successfully scale AI adoption without compromising security and compliance.

1. Centralized AI Risk & Third-Party Oversight

We can expect AI to continue its momentum in becoming more embedded in clinical, operational and administrative workflows. In preparation for the inertia, health systems need a unified view of risks across departments and third-party networks. A centralized GRC platform enables organizations to:

• Track AI-related risks and regulatory obligations across all AI-enabled applications and third-party integrations.
• Automate third-party assessments and contract compliance, ensuring third- and fourth-party AI providers meet security and regulatory standards and a business associate agreement is in place when required.
• Maintain real-time visibility into AI-driven data handling, reducing gaps in HIPAA, GDPR and state-specific AI compliance requirements.

2. Real-Time Compliance Tracking & Workflow Automation

AI regulations are constantly evolving, making manual compliance tracking inefficient and error-prone. A risk register embedded within a GRC system enables organizations to:

• Automate compliance workflows, ensuring AI and third-party integrations are regularly evaluated against changing regulations.
• Provide dashboards and reporting, giving compliance teams a live view of risk levels as well as security and compliance gaps.
• Reduce administrative burden by maintaining a living repository of risk data, helping teams quickly generate audit-ready compliance reports.

3. Continuous AI Risk Auditing & Third-Party Assessments

A one-time third-party review and checklist mentality is no longer enough. Health systems must continuously monitor AI applications and external partners to ensure long-term security and compliance. This includes:

• Routine audits of AI-driven processes to validate accuracy, security measures and regulatory compliance.
• Ongoing third- and fourth-party risk assessments, ensuring external AI providers uphold security, data privacy and contractual obligations.
• Integrating AI governance within broader compliance frameworks to prevent operational disruptions, reputational harm and legal exposure.

The Future of AI in Healthcare: Why GRC Matters

AI is fundamentally changing the way HCOs operate, creating new opportunities to reduce administrative strain and improve patient care. But without the right safeguards, it can introduce more risks than rewards.

Healthcare leaders must think through strategies that support AI integration that enhances operations without creating new vulnerabilities. Those who leverage advanced GRC systems into their compliance and risk management strategy will be better prepared to scale AI responsibly, adapt to evolving regulations and protect long-term stability. By centralizing risk oversight, automating compliance and continuously evaluating AI-driven tools, organizations can adopt AI with confidence while safeguarding security, compliance and public trust.