What is the AI trilemma, and why does it matter for healthcare vendors?

In a recent Foreign Affairs article, “The AI Trilemma,” the author argues that governments are struggling to balance three competing priorities at once: accelerate AI innovation, manage its risks, and build effective assessment programs. While geopolitical in framing, the same dynamic is unfolding inside healthcare vendor ecosystems. Optimizing all three requires moving beyond traditional questionnaires toward independently validated, decision-grade assurance that keeps innovation aligned with regulatory and security expectations.

Healthcare leaders are under pressure to adopt AI-enabled capabilities quickly. Clinical documentation support, revenue cycle automation, patient engagement tools, cybersecurity monitoring — AI functionality is increasingly embedded across the vendor stack. Yet every new AI-enabled product introduces additional data flows, algorithmic decision influence, and third-party dependencies.

The result is a localized version of the AI trilemma

• Move quickly to capture innovation.
• Reduce risk to protect patients and data.
• Evaluate vendors and technologies effectively.

Trying to optimize all three at once exposes the limits of traditional third-party risk management (TPRM).

Why do traditional TPRM approaches break down with AI-enabled vendors?

Historically, many organizations relied on vendor questionnaires, point-in-time assessments, or self-attestation to evaluate risk. That approach was already strained before AI. With AI, it breaks down more quickly.

AI-enabled vendors may rely on

• Cloud infrastructure providers
• Foundation model developers
• Data labeling subcontractors
• External APIs and integrations
• Continuous model updates

This creates not just third-party risk, but fourth- and fifth-party risk — often invisible to the relying organization. Traditional TPRM models were not designed to account for continuously evolving AI systems or layered model dependencies.

As AI systems update dynamically and rely on interconnected services, risk posture can change between assessment cycles. Static documentation cannot keep pace with dynamic model behavior.

How are regulatory expectations increasing around AI and vendor risk?

At the same time, regulatory expectations in healthcare are tightening. HHS has emphasized the importance of “recognized security practices” and proposed updates to strengthen cybersecurity safeguards under the HIPAA Security Rule. Organizations are expected not merely to claim controls exist, but to demonstrate that they operate effectively.

Meanwhile, NIST’s AI Risk Management Framework (AI RMF) provides structured guidance for identifying and managing AI-specific risks, including governance, data management, monitoring, and accountability.

Together, these developments signal a shift from policy-based compliance to demonstrable, operational effectiveness. The tension is clear: AI innovation accelerates, but the evidence required to support trust must accelerate with it.

What does decision-grade assurance look like in AI-driven TPRM?

The answer to the AI trilemma inside TPRM is not slowing innovation. It is elevating assurance.

Healthcare organizations need

• Clearly defined AI security expectations for vendors
• Risk-based scoping of AI-enabled services
• Independently validated evidence that security and privacy controls are implemented and operating effectively
• Repeatable, comparable assurance artifacts that reduce duplicative reviews

In other words, they need decision-grade assurance — not marketing claims.

When assurance is standardized and independently validated, organizations can move more confidently. They reduce duplicative assessments, shorten procurement cycles, and maintain alignment with rising regulatory expectations.

For organizations seeking a structured way to demonstrate AI security and risk management effectiveness, the HITRUST AI assessment provides a certifiable, independently validated approach aligned to recognized security practices and emerging AI risks. It enables organizations to evaluate vendors’ AI-specific controls within the broader assurance framework already trusted across healthcare, helping bridge innovation and risk management without creating parallel compliance tracks.

How can healthcare organizations innovate without losing assurance?

The geopolitical AI trilemma is about balancing speed, safety, and oversight. Inside healthcare vendor ecosystems, the same challenge exists — but the operational solution is clearer:

Innovation without validated assurance is risk acceleration.
Innovation with validated assurance is resilience.

Organizations that embed validated, standardized assurance into their TPRM and AI strategies do not have to choose between innovation and risk management. They create a structured path to adopt AI technologies responsibly, protect sensitive data, and sustain regulatory alignment — all while maintaining operational velocity.