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.

Preparing for a ransomware attack is now a mission-critical priority for healthcare organizations. Ransomware incidents can disrupt clinical operations, delay patient care, expose sensitive health data, and create significant regulatory and financial consequences. As healthcare ecosystems become more digitally connected, building ransomware resilience requires more than reactive controls. It demands structured preparation, tested response plans, and validated assurance.

Learn about a practical, healthcare-specific roadmap to help organizations prepare for a ransomware attack, mitigate its impact, and recover effectively when prevention alone is not enough.

Understanding the ransomware threat landscape

What is ransomware and how does it work?

Ransomware is a type of malicious software that encrypts systems or data, making them inaccessible until a ransom is paid, often accompanied by threats to publicly release stolen data. In healthcare, ransomware attacks frequently target electronic health records (EHRs), imaging systems, scheduling platforms, billing applications, and connected medical devices.

Modern ransomware attacks often use double or triple extortion tactics, combining system encryption with data exfiltration and denial-of-service threats. This significantly raises the stakes for healthcare providers, where downtime and data exposure can directly impact patient safety.

Why ransomware attacks are on the rise

In 2025, 8.9 million health care records were compromised due to ransomware. Healthcare remains one of the most targeted sectors for ransomware due to the high value of protected health information (PHI), the complexity of clinical environments, and the limited tolerance for operational disruption. Many organizations rely on legacy systems, third-party vendors, and cloud platforms that expand the attack surface faster than security programs can mature.

For a deeper look at why this issue continues to escalate, explore the ransomware threat and its growing impact across regulated industries.

Common entry points and attack vectors

Most ransomware incidents begin with well-known weaknesses, including

• Third-party vendor or cloud service provider compromises
• Phishing emails targeting clinicians and administrative staff
• Compromised credentials and weak identity controls
• Unpatched systems and outdated software

Understanding these entry points is a foundational step in any effort to prepare for a ransomware attack.

Core strategies for ransomware preparedness

Conducting risk assessments

A ransomware risk assessment helps healthcare organizations identify critical systems, data flows, and dependencies most likely to be targeted or disrupted. This includes evaluating

• Availability and integrity of EHR systems
• Clinical workflow dependencies and downtime tolerance
• Third-party and cloud service risks
• Backup coverage for mission-critical assets

These assessments should be integrated into broader enterprise risk management programs and aligned with recognized cybersecurity frameworks for ransomware.

Building a robust incident response plan

A documented ransomware response plan is essential for minimizing confusion and downtime during an attack. Healthcare-specific plans should clearly define

• Decision-making authority during an incident
• Communication protocols with clinicians, leadership, regulators, and patients
• Coordination with legal counsel, cyber insurers, and incident response partners
• Criteria for system isolation, clinical workarounds, and recovery prioritization

Regular tabletop exercises ensure teams understand their roles before a real incident occurs.

Backup and recovery best practices

Reliable, tested backups remain one of the most effective ransomware mitigation controls. Healthcare organizations should

• Maintain offline or immutable backups
• Test restoration procedures for clinical and operational systems
• Ensure backups include EHRs, imaging systems, and connected devices

Without validated recovery capabilities, even well-designed response plans may fail under real-world conditions.

Ransomware risks in the healthcare sector

Unique threats facing healthcare organizations

Ransomware in healthcare presents risks that extend beyond financial loss. System outages can delay diagnoses, interrupt treatments, and force providers to divert patients or revert to manual processes. At the same time, PHI is highly valuable on the black market, making healthcare organizations prime targets for data extortion.

Third-party vendors and service providers compound these risks, as attackers increasingly exploit indirect access paths. Industry analysis shows growing concern around how ransomware has affected TPRM and vendor ecosystems.

Regulatory compliance and risk mitigation strategies

Ransomware incidents often trigger regulatory scrutiny under HIPAA, state privacy laws, and contractual obligations. Healthcare organizations must demonstrate not only that safeguards existed, but that risks were proactively assessed, mitigated, and governed.

This makes structured, auditable security programs essential not just for compliance, but for operational resilience.

Leveraging cybersecurity assessments for defense

How HITRUST supports ransomware readiness

The HITRUST framework provides a prescriptive, scalable approach to preparing for ransomware attacks in healthcare. By harmonizing regulatory requirements, security controls, and risk-based assurance, HITRUST enables organizations to assess their vendors and

• Identify and remediate ransomware-related control gaps
• Align security practices with healthcare regulatory expectations
• Strengthen risk management programs

Rather than relying on fragmented controls, HITRUST supports a unified and measurable approach to ransomware resilience.

Integrating assessments into your security strategy

Healthcare organizations that integrate assessments like HITRUST into their security programs benefit from

• Consistent control implementation across systems and vendors
• Benchmarking and maturity measurement
• Clear evidence of due diligence for regulators, partners, and patients

This improves preparedness across the full incident lifecycle, from prevention to response and recovery.

Certification and assurance benefits

For healthcare organizations assessing their vendors, HITRUST certification provides independent validation that security and risk controls are both designed and operating effectively. Rather than relying on self-attestations or fragmented questionnaires, healthcare organizations can use HITRUST certification to gain confidence that vendor environments are prepared to withstand ransomware threats.

HITRUST certification

• Demonstrates that vendors have proactively implemented controls to reduce ransomware risk
• Builds trust and transparency across the healthcare ecosystem, including regulators and business partners
• Reduces assessment fatigue by replacing duplicative vendor reviews with a standardized, validated approach

This assurance helps healthcare organizations ensure that ransomware resilience is embedded into vendor governance and operations.

Conclusion: Building long-term resilience

Continuous monitoring and improvement

Preparing for a ransomware attack is not a one-time initiative. Healthcare organizations must continuously monitor threats, test controls, assess vendors, and incorporate lessons learned from incidents and exercises into program improvements.

Staying ahead of emerging threats

As ransomware actors increasingly target third-party vendors, cloud platforms, and interconnected healthcare systems, organizations need adaptable and validated security strategies. Those that invest in threat-adaptive frameworks, ongoing risk assessments, and independent assurance will be best positioned to protect patient care and sustain trust over time.

Protect your organization from ransomware threats. Explore how HITRUST can help you build a resilient cybersecurity strategy today.

AI security certification has become a critical requirement for organizations assessing the risk posture of AI-enabled vendors, especially as AI becomes deeply embedded in sensitive and regulated business processes. For third-party risk management (TPRM) teams, the question is no longer whether vendors use AI, but whether those AI systems are secure, governed, and independently validated against recognized security standards.

As AI adoption accelerates across industries, traditional vendor risk assessments are struggling to keep pace. Questionnaires and attestations alone cannot adequately address the unique risks introduced by AI models, training data, and automated decision-making. This is where AI security certification plays a pivotal role, providing structured, testable assurance that AI systems meet defined security and risk management expectations.

Why AI security certification is critical today

The rise of AI in sensitive and regulated environments

Third-party vendors increasingly rely on AI to process protected health information (PHI), financial data, intellectual property, and other sensitive assets. From clinical decision support tools to fraud detection engines, AI systems now sit directly in the flow of regulated data.

For organizations managing third-party risk, this creates a new exposure layer. A vendor’s AI model may introduce risks that extend far beyond traditional infrastructure or application security, making AI security assessment an essential component of modern vendor due diligence.

Security challenges unique to AI systems

AI systems present distinct security challenges that are often overlooked in standard risk assessments. These include risks related to training data integrity, model drift, prompt injection, adversarial attacks, and unintended data leakage through model outputs. Vendors may also struggle to demonstrate consistent governance over how AI systems are developed, deployed, and monitored over time.

Without a recognized AI assurance framework, organizations are left to interpret vendor claims without objective validation, an approach that increases uncertainty and risk.

The cost of uncertified AI deployment

Uncertified AI deployments can expose organizations to regulatory scrutiny, operational disruptions, reputational damage, and downstream third-party failures. For TPRM leaders, the absence of AI security certification complicates vendor onboarding, slows procurement, and increases residual risk across the supply chain.

Independent assessment helps reduce these challenges by offering a standardized, repeatable way to evaluate AI security controls at scale.

What is AI security certification?

Defining AI assurance through a security lens

AI security certification is a formal, independent proof of evaluation of an AI system’s security, governance, and risk management controls. Unlike high-level ethical AI principles or self-attested compliance checklists, certification focuses on whether AI systems are implemented and operated securely in real-world environments.

For third-party risk teams, AI security certification serves as objective evidence that a vendor’s AI system has been assessed against defined security requirements.

Key components of a certifiable AI security program

A certifiable AI security program typically includes controls for data protection, secure model development, access management, monitoring, incident response, and governance oversight. It also requires documented policies, repeatable processes, and demonstrable implementation — elements that are critical for scalable vendor risk management.

HITRUST brings these components together through a structured, security-first approach to AI assurance.

How certification helps mitigate AI risks

Addressing data integrity and model vulnerabilities

AI security assessment helps organizations validate that vendors have safeguards in place to protect training data, prevent unauthorized model access, and detect tampering or degradation over time. This is particularly important when vendors rely on large datasets sourced from multiple environments.

TPRM teams gain confidence through certification that AI systems are not only functional but resilient against integrity and availability risks.

Preventing adversarial exploits and bias

Adversarial attacks, model manipulation, and unintended bias pose significant risks to organizations relying on third-party AI. Certification frameworks designed for AI security evaluate whether vendors have controls to identify, mitigate, and respond to these threats.

By requiring certified assurance, organizations can reduce the likelihood that vendor AI systems introduce compliance, ethical, or operational failures into their ecosystems.

HITRUST’s role in AI security certification

Overview of the HITRUST AI Security Assessment

The HITRUST AI Security Assessment and Certification was designed to solve a specific and increasingly urgent problem for organizations and third-party risk management teams: proving that deployed AI systems are secure.

Rather than focusing on high-level governance maturity or policy intent, HITRUST evaluates AI-specific security risks in real, operational environments. The assessment applies prescriptive, threat-mapped controls tailored to how and where AI is deployed, ensuring that security requirements align directly to practical risk scenarios.

Independent testing, centralized quality assurance, and formal HITRUST certification together deliver defensible, evidence-based AI security assurance that TPRM teams can rely on across vendor ecosystems.

How HITRUST aligns with global AI security standards

HITRUST’s AI security assessment aligns with and maps to leading global standards and guidance, including NIST publications, ISO/IEC standards, and OWASP resources. However, HITRUST differs materially from governance-first approaches such as ISO/IEC 42001. HITRUST provides prescriptive security requirements and standardized assurance outcomes.

Use cases: HITRUST in healthcare, finance, and beyond

Developed through extensive industry collaboration, HITRUST AI Security Certification enables scalable trust across regulated industries where AI risk is embedded in third-party products and services. The assessment includes 44 harmonized, AI-specific security controls with explicit mappings between threats and required safeguards, and it is regularly updated to address emerging AI risks.

In healthcare, HITRUST-certified AI systems support the protection of PHI and regulatory compliance. In financial services, they help organizations validate the security of AI-driven analytics, automation, and fraud detection. Across industries, standardized reporting supports executives, regulators, and TPRM teams alike.

By certifying systems and environments, HITRUST delivers clear proof that AI systems are protected, enabling organizations to make confident, defensible third-party risk decisions at scale.

Steps to achieve AI security certification

Conducting a readiness assessment

Vendors typically begin by evaluating their AI systems against HITRUST requirements to identify gaps. Additional insights on building trust in AI highlight how structured assurance accelerates confidence across stakeholders.

Working with HITRUST external assessors

Certification requires validation by an authorized HITRUST Assessor, ensuring independence and consistency. This third-party validation is a key differentiator for risk teams seeking defensible assurance outcomes.

Organizations exploring broader assurance options can also review HITRUST’s full portfolio of assessments and certifications to support holistic risk management strategies.

Maintaining certification through continuous monitoring

AI risk does not remain static. HITRUST emphasizes ongoing monitoring and reassessment to ensure certified AI systems continue to meet security expectations as models evolve and threats change. This approach supports efficient AI risk management across the vendor lifecycle.

Future-proofing AI with HITRUST

Building a culture of certified AI innovation

For organizations managing third-party risk, AI security assessment is foundational to maintaining trust, resilience, and compliance in an AI-driven ecosystem. By leveraging HITRUST’s structured, scalable pathways, organizations can gain defensible, repeatable AI assurance.

Secure AI systems with confidence and explore HITRUST’s proven path to AI security certification and risk reduction.