“AI, Regulation, and the New Risk Stack: A CEO’s Guide”

Healthcare CEOs face a growing challenge: managing the "new risk stack" - a combination of AI, cybersecurity, and evolving regulations. These interconnected risks demand immediate, organization-wide strategies instead of outdated, siloed approaches. The stakes are high, with 31 million Americans affected by healthcare breaches in early 2024, AI adoption surging past 75% of organizations, and regulatory updates arriving faster than ever.

Key Takeaways:

• AI Risks: Generative AI and predictive AI adoption are growing, but 58% of organizations lack governance, increasing vulnerabilities.
• Cyber Threats: AI-driven attacks like deepfake scams and autonomous malware are bypassing traditional defenses, threatening patient safety and operations.
• Regulations: Federal and state AI rules are expanding, with the FDA, HIPAA, and FTC focusing on compliance for AI tools handling patient data.
• Leadership Role: CEOs must lead cross-functional AI oversight, integrate AI into threat detection, and adopt proactive risk management frameworks.

Quick Insights:

• AI-Powered Cyber Threats: Deepfake voice scams, data poisoning, and adaptive malware are redefining cybersecurity challenges.
• Compliance Needs: Organizations must align AI systems with HIPAA, FDA, and state-specific rules while addressing algorithmic bias and data security.
• Vendor Risks: Third-party vendors account for 59% of breaches, requiring stronger evaluations and monitoring.

This guide highlights actionable steps for CEOs to manage these risks, emphasizing cross-functional collaboration, AI governance, and advanced security tools like Censinet RiskOps™. By addressing the new risk stack head-on, healthcare leaders can protect patient safety, ensure compliance, and strengthen organizational resilience.

Healthcare Cybersecurity: From Digital Risk to AI Governance with Ed Gaudet

AI-Powered Cyber Threats in Healthcare

Healthcare organizations are now grappling with a new breed of cyberattacks powered by artificial intelligence. These threats are bypassing traditional defenses and causing damage on a scale never seen before.

New AI-Powered Cyber Threats

AI has armed cybercriminals with tools that make their attacks more convincing, scalable, and dangerous than ever.

Deepfake Voice and Video Attacks are no longer a distant possibility - they’re happening now. Early in 2024, healthcare settings reported incidents where AI-generated voice deepfakes were used to authorize fraudulent prescriptions or extract sensitive data remotely ^[3]. These realistic impersonations can trick staff into granting unauthorized access to patient records or medication systems, creating serious vulnerabilities.

AI-Enhanced Phishing Campaigns have taken email scams to the next level. Cybercriminals now use machine learning to analyze massive datasets, crafting emails that mimic the tone, style, and context of specific healthcare providers. These messages often reference recent patient interactions or create urgent scenarios, making them much harder for employees to detect ^[4].

Data Poisoning Attacks target the very core of healthcare AI systems by corrupting their training data. This type of attack can cause diagnostic tools to deliver incorrect results, putting patient safety at serious risk while remaining undetected ^[3].

Autonomous Malware has emerged as another alarming development. This AI-driven malware doesn’t need human oversight - it mutates its code in real time to evade detection and spreads rapidly through connected systems ^[4][5]. Traditional signature-based security measures simply can’t keep up with these self-adapting threats.

These AI-powered attacks are pushing beyond the limits of human response capabilities. By automating malicious activities, cybercriminals are increasing both the scale and sophistication of their attacks, exploiting the healthcare sector’s growing reliance on digital systems ^[4].

Standard vs. AI-Powered Cyber Risks

To effectively defend against these advanced threats, healthcare leaders must understand how they differ from traditional cyber risks.

The defining factor here is adaptability. While traditional threats rely on predictable patterns, AI-powered attacks refine their methods in real time, making them far harder to detect ^[4]. Organizations leveraging AI-based security tools report detecting threats up to 60% faster than those relying on older methods ^[6]. Alarmingly, 78% of Chief Information Security Officers (CISOs) acknowledge that AI-driven threats are significantly impacting their operations ^[6].

This sets the stage for understanding how these advanced threats directly endanger patient safety and disrupt healthcare operations.

How AI Threats Affect Patient Safety and Operations

AI-powered cyberattacks don’t just challenge IT departments - they pose a direct risk to patient care and the functionality of healthcare systems.

Operational Disruption can bring healthcare systems to a standstill. In February 2024, the ALPHV/BlackCat group launched a ransomware attack on Change Healthcare, affecting over 100 million patients. The attack exploited system vulnerabilities, causing widespread operational paralysis throughout the healthcare ecosystem ^[8].

Patient Safety Risks escalate when clinical systems are compromised. In February 2025, Australian fertility provider Genea suffered a ransomware breach by the Termite group, which exploited an unpatched Citrix vulnerability. Nearly 1 terabyte of sensitive data, including pathology reports and treatment notes, was stolen ^[3]. When critical patient information is inaccessible or tampered with, healthcare providers lose the ability to make informed care decisions.

Erosion of Trust is another consequence. Patients who lose confidence in their healthcare provider’s ability to safeguard their data may hesitate to share information or follow treatment plans, ultimately affecting health outcomes.

"Let’s be clear… ransomware and other cyberattacks on hospitals and other health facilities are not just issues of security and confidentiality; they can be issues of life and death", warns Tedros Adhanom Ghebreyesus, Director-General of the World Health Organization (WHO) ^[3].

The financial toll is equally staggering. The global average cost of a security breach has climbed to $4.9 million as of 2024 - a 10% increase from previous years ^[4]. For healthcare organizations already operating on tight margins, these costs can be devastating.

Supply Chain Vulnerabilities add yet another layer of risk. Weaknesses in third-party vendors account for 59% of healthcare breaches ^[7]. AI-powered attacks exploit these interconnected relationships, using compromised vendors as entry points to target multiple organizations simultaneously.

"The increasing frequency and sophistication of cyberattacks in the healthcare sector pose a direct and significant threat to patient safety. Any cyberattack on the healthcare sector that disrupts or delays patient care creates a risk to patient safety and crosses the line from an economic crime to a threat-to-life crime", emphasizes John Riggi, Cybersecurity Advisor to the American Hospital Association ^[3].

The challenge for healthcare leaders is clear: the attack surface is evolving faster than traditional defenses can keep up ^[8]. To protect both their organizations and their patients, executives must treat AI-driven security measures as essential components of patient safety and regulatory compliance. This rapidly changing threat landscape demands a shift toward integrated risk management strategies.

Healthcare AI Regulations and Compliance

The rules and guidelines surrounding healthcare AI are shifting quickly, presenting unique challenges for CEOs. As AI-driven risks grow, the regulatory framework continues to evolve, leaving many organizations unsure about how to meet compliance standards.

Key U.S. Regulations for Healthcare AI

In the U.S., federal regulations play a critical role in ensuring both clinical safety and data protection in healthcare AI.

• FDA Oversight: The FDA treats AI systems developed for medical purposes as medical devices ^[11]. This includes diagnostic algorithms, treatment recommendation tools, and clinical decision support systems. The FDA evaluates these technologies based on their integration and intended use, whether for medical devices, wellness products, or drug development ^[10].
• HIPAA and the HITECH Act: Any AI system handling patient data must comply with HIPAA and the HITECH Act. These regulations strictly control how Protected Health Information (PHI) is accessed, used, and disclosed ^[12]. The HIPAA Security Rule, which focuses on electronic PHI (ePHI), presents challenges when AI systems process massive amounts of patient data ^[9].
• FTC Enforcement: The FTC monitors AI tool marketers for unsubstantiated claims or deceptive practices. It also investigates the use of improperly obtained data in AI training ^[10].

On January 23, 2025, a major regulatory shift occurred when President Donald Trump rescinded an AI Executive Order from the Biden administration, adding further uncertainty to federal oversight ^[10].

At the state level, many states are introducing regulations that require specific standards for safeguarding sensitive information, including PHI ^[9]. The Department of Health and Human Services has also raised alarms about the growing number of breaches and cybersecurity incidents among regulated entities ^[9]. Typically, new regulations provide a 60-day implementation period, followed by additional compliance deadlines to allow organizations time to adjust their policies and procedures ^[9].

These regulations form the foundation for addressing AI-related compliance challenges.

Connecting Regulations to AI Risk Areas

CEOs need to understand how specific regulations align with AI risk scenarios. Here's a breakdown:

The dynamic nature of AI, where models evolve and learn continuously, makes traditional compliance approaches less effective. To stay ahead, organizations must conduct AI-specific risk analyses that account for changing data flows, training processes, and access mechanisms ^[12]. Regular audits and clear vendor agreements with AI-specific clauses are key to addressing risks like algorithmic bias ^[12].

Building an AI Compliance Framework

To tackle these challenges, CEOs need a comprehensive compliance framework tailored to healthcare AI. Here’s how to get started:

• Create Cross-Functional AI Governance Committees: Bring together clinical leaders, IT security teams, legal experts, and compliance officers to oversee AI systems throughout their lifecycle ^[14].
• Set Documentation Standards: Keep detailed records of AI development, including data sources, feature selection, validation methods, and bias mitigation strategies. Independent validation of AI performance using internal data is crucial ^[14].
• Develop AI-Specific Data Governance Policies: Ensure policies address data completeness, representativeness, and bias reduction. Standards should also cover the use of synthetic data, anonymization techniques, and data retention practices that respect patient rights ^[13].
• Implement Transparency and Accountability Measures: Focus on explainability in AI outputs and maintain audit trails for AI decisions. For clinical applications, human review of AI-generated decisions is essential to meet regulatory standards and protect patient safety ^[12][13].
• Stay Updated on Regulatory Changes: Keep track of new state and federal AI legislation affecting digital health ^[10].
• Conduct Regular AI Governance Impact Assessments: Evaluate risks tied to third-party AI tools, system failures, and potential regulatory violations. These assessments should address issues like bias, privacy concerns, reliability, explainability, and security ^[13].

Without strong governance, AI systems can introduce serious risks ^[14].

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Creating an AI Risk Management Framework

Managing AI risks effectively requires a structured approach that combines collaboration across departments, a balance of automation with human oversight, and stringent vendor controls.

Setting Up Cross-Department AI Governance

AI governance starts with forming committees that bring together leaders from IT, clinical operations, compliance, and data privacy, alongside specialists in AI, ethics, and legal matters. These teams are responsible for overseeing risks, reviewing AI initiatives, and ensuring that policies and ethical standards are in place before any project is launched.

Bruce A. Scott, MD, President of the AMA, highlights the importance of keeping humans at the center of AI-driven healthcare:

"At the AMA, we like to refer to AI not as 'artificial intelligence,' but rather as 'augmented intelligence' to emphasize the human component of this new resource and technology so patients know that, whatever the future holds, that there will be a physician at the heart of their health care and their decisions." ^[2]

To make this governance effective, organizations should appoint an AI governance leader, create an oversight board, and establish policies that align with both internal strategies and external regulations. It's also crucial to set protocols for identifying and addressing biases in training datasets, ensuring AI tools remain accessible and fair for all patients. This foundation paves the way for integrating automated monitoring systems and managing vendor relationships.

Using Automation with Human Oversight

A strong risk management framework blends automated monitoring systems with human expertise. Automated systems can track performance metrics, spot anomalies, and flag compliance issues in real time. However, critical decisions - particularly in clinical environments - must always involve human validation. Assigning dedicated AI stewards to oversee regulatory and ethical considerations ensures this balance is maintained.

To achieve this, organizations can implement real-time performance dashboards, automated alerts, and clear escalation procedures for human review when necessary. Conducting impact assessments helps pinpoint areas where human intervention is essential. Additionally, robust security measures, such as conformity assessments and ESG evaluations, protect AI systems from unauthorized access or misuse. These efforts work in tandem with vendor oversight to address risks across the supply chain.

Managing Vendor and Supply Chain Risks

Healthcare organizations increasingly depend on AI vendors, with over 70% using generative AI and 59% relying on third-party vendors for custom AI solutions ^[16]. This reliance makes vendor risk management a critical priority.

Effective vendor management begins with thorough due diligence. This includes evaluating the vendor's role, the sensitivity of the data they handle, their security controls, compliance certifications, and performance history. Contracts should clearly outline data ownership, security standards, incident reporting processes, audit rights, and cyberinsurance requirements, while also addressing algorithmic bias and data quality.

Continuous monitoring of vendors is equally important. AI tools can help track vendor cybersecurity and flag emerging risks. As the Chief Marketing Officer at UpGuard puts it:

"Ongoing monitoring supports prioritizing risks and taking timely action, reducing potential harm." ^[16]

Extending these controls to fourth-party vendors - subcontractors within the supply chain - adds another layer of protection. This approach has already delivered measurable results. For example, Alameda Alliance for Health reported significant time savings using dedicated vendor platforms, and Piedmont Healthcare reduced pricing errors by 81% through automated checks and contract matching ^[15]. Additionally, 46% of U.S. healthcare companies now use AI to manage supply chain risks ^[15]. Past incidents, such as the 2019 Quest Diagnostics data breach that exposed nearly 12 million patient records, emphasize the importance of strong vendor risk practices ^[16].

Using Censinet for AI and Cyber Risk Management

Censinet builds on a solid AI risk management framework to offer healthcare organizations tools that simplify oversight and compliance. For healthcare CEOs, managing AI and cybersecurity risks while staying compliant with regulations is no small feat. Censinet steps in with tailored solutions designed specifically for the healthcare sector, providing an integrated platform that supports governance across departments and automates compliance processes.

Censinet RiskOps™ and AITM Overview

Censinet RiskOps™ is a risk management platform crafted for healthcare delivery organizations and their vendor networks. Its AI Trust Management (AITM) features streamline risk assessments, automate compliance tracking, and enhance governance for AI-driven healthcare technologies.

The AITM module speeds up third-party risk assessments by automating tasks like vendor questionnaire completion, evidence summarization, and tracking fourth-party exposures. This automation saves time while ensuring thorough evaluations.

The platform combines automation with human oversight. Key steps in the risk assessment process - like validating evidence, drafting policies, and managing risks - are automated but remain under the control of risk teams through configurable rules and review workflows.

Censinet AITM serves as a central hub for AI governance, directing assessment results and tasks to relevant stakeholders, such as members of the AI governance committee. Its intuitive AI risk dashboard consolidates real-time data on policies, risks, and tasks, making it easier to manage AI-related concerns.

In February 2025, Renown Health partnered with Censinet to streamline IEEE UL 2933 compliance checks for new AI vendors using Censinet TPRM AI™. This collaboration not only simplified vendor evaluations but also reinforced their commitment to patient safety and data security. Chuck Podesta, Chief Information Security Officer at Renown Health, remarked that this automation supports their high standards for trust, safety, and interoperability in healthcare technology.

Key Features and Benefits for CEOs

Censinet's platform offers features that address the unique challenges healthcare executives face when managing AI and cybersecurity risks. Here’s how it helps:

The platform’s impact on efficiency is evident in user feedback. Terry Grogan, CISO at Tower Health, shared:

"Censinet RiskOps allowed 3 FTEs to go back to their real jobs! Now we do a lot more risk assessments with only 2 FTEs required." ^[17]

Healthcare AI Risk Management Use Cases

Censinet tackles common challenges healthcare CEOs face when adopting AI technologies and managing associated risks, including regulatory compliance, AI model drift, and vendor oversight.

Third-Party AI Vendor Assessment and Continuous Monitoring
With healthcare organizations increasingly relying on external AI solutions, Censinet automates vendor evaluations to ensure security and regulatory compliance. It also monitors AI model updates, tracks algorithmic drift, and ensures ongoing alignment with regulatory standards.

Cross-Departmental AI Governance
The platform facilitates decision-making across IT, compliance, clinical, and executive teams by routing AI-related tasks to the right stakeholders. This ensures efficient governance without sacrificing oversight.

Regulatory Compliance Automation
As of 2025, over 250 health AI-related bills have been introduced across 34 U.S. states, adding complexity to compliance efforts. Censinet updates frameworks and issues alerts when new regulations impact existing AI systems.

Supply Chain Risk Management
Beyond direct vendors, Censinet extends risk management to fourth-party relationships and subcontractors. This expanded visibility helps organizations address risks that could compromise patient safety or data security.

Brian Sterud, CIO at Faith Regional Health, highlighted the value of these features:

"Benchmarking against industry standards helps us advocate for the right resources and ensures we are leading where it matters." ^[17]

These examples show how Censinet shifts AI risk management from a manual, reactive approach to a proactive, automated system that grows with an organization’s needs and regulatory demands.

Conclusion: CEO Priorities for Managing Healthcare Risks

Key Points for Managing the New Risk Stack

Healthcare CEOs are navigating a perfect storm of challenges: the rapid integration of AI, tightening regulations, and increasingly sophisticated cyber threats. The stakes are high, with over 180 million individuals' health information exposed by November 30, 2024, and cybercrime costing $6 trillion in 2021, impacting 42 million U.S. patients ^[21][18]. Addressing these risks requires immediate focus on security, governance, and vendor management.

Treat AI Security as a Patient Safety Priority

One of the most pressing tasks for healthcare leaders is to elevate AI security to the same level of importance as patient safety and regulatory compliance. A healthcare CEO emphasized the gravity of ransomware threats:

"Our primary challenges with ransomware are that it threatens patient safety, disrupts our operations, and undermines trust. In healthcare, system downtime directly harms patients." ^[20]

AI systems must meet these same high standards. Security teams should collaborate with AI developers to ensure models are trained on secure, tamper-resistant data. At the same time, organizations need robust AI governance frameworks that enforce accountability for AI-driven decisions ^[19].

Tackle the Human Element in Cybersecurity

Even with cutting-edge technology, human error remains a major vulnerability in healthcare cybersecurity. As one CEO explained:

"Phishing and insider threats remain challenging due to inherent human error. We invest in cyber security awareness programs, but staff under pressure may inadvertently cause breaches." ^[20]

To address this, organizations should maintain real-time records of interactions with patient data and implement comprehensive staff training. This includes regular phishing simulations, awareness programs, and well-defined incident response strategies ^[18][20].

Adopt Effective Risk Management Frameworks

Frameworks like HITRUST and NIST provide a solid foundation for risk management. These tools support cross-department governance and ensure alignment with an integrated risk strategy. Key steps include vulnerability assessments, advanced threat detection, and robust data backup systems ^[1][20]. For AI-specific risks, limit the use of patient data to the minimum necessary for training and decision-making, while ensuring data is de-identified to reduce the impact of breaches ^[18].

Moving Forward: Continuous Adaptation and Investment

Looking ahead, healthcare CEOs must focus on strategic investments to maintain resilience. With the healthcare AI market projected to grow at a compound annual rate of 44.2% between 2019 and 2027 ^[18], staying adaptable is critical. Balancing innovation with risk management will be the cornerstone of success.

Foster Collaborative Leadership

Collaboration between CIOs, CFOs, and other executives is essential to align priorities and maximize returns on investment ^[21]. Organizations where CEOs take an active role in AI governance often see the greatest impact on their bottom line ^[22].

Strengthen Vendor Risk Oversight

Third-party vendors pose unique challenges, as one CEO noted:

"We rely on third-party vendors for critical services. These vendors' security practices directly impact us, but we often lack full visibility into their risk management practices." ^[20]

To mitigate these risks, healthcare organizations should implement continuous monitoring, conduct thorough vendor assessments, enforce strict access controls, and prepare incident response plans tailored to third-party risks ^[20].

Stay Ahead of Regulatory Changes

Proactively monitoring and addressing regulatory updates is essential for reducing compliance risks in a constantly shifting landscape ^[21].

Balance Automation with Human Oversight

Organizations leveraging security AI and automation save an average of $2.22 million in breach-related costs compared to those that do not ^[22]. However, successful implementation requires balancing automation's efficiency with human oversight to ensure critical decisions remain in capable hands.

FAQs

What steps can healthcare CEOs take to integrate AI governance into their risk management strategies?

Healthcare CEOs can weave AI governance into their risk management strategies by honing in on a few critical areas:

• Define accountability: Assign clear roles and responsibilities for managing AI systems to promote transparency and ethical practices.
• Integrate with existing policies: Embed AI governance within current frameworks for cybersecurity, compliance, and regulatory measures to maintain consistency across operations.
• Conduct regular evaluations: Continuously monitor AI systems to assess their performance, compliance, and potential risks, addressing any concerns before they escalate.

Taking a structured approach to AI governance allows CEOs to manage risks effectively, meet regulatory requirements, and ensure AI is used safely and ethically in healthcare settings.

What steps can healthcare organizations take to safeguard against AI-driven cyber threats like deepfake scams and autonomous malware?

To guard against AI-driven cyber threats like deepfake scams and autonomous malware, healthcare organizations need a robust, layered cybersecurity strategy. Here are some essential measures to consider:

• Use advanced AI-based threat detection tools to spot and neutralize malicious activities in real time.
• Monitor systems continuously to detect unusual behavior or hidden vulnerabilities before they can be exploited.
• Educate employees to identify and respond to AI-related threats, such as phishing attempts that use deepfakes.
• Keep software updated and patched regularly to eliminate security loopholes that AI-powered malware could exploit.
• Implement a zero trust architecture, ensuring all access requests are verified and secured, whether they come from inside or outside the network.

These steps, when combined, can help healthcare organizations build stronger defenses and stay one step ahead of emerging AI-powered cyber threats.

How can healthcare organizations comply with new AI regulations while protecting patient data and addressing algorithmic bias?

To navigate the shifting landscape of AI regulations, healthcare organizations should prioritize three critical areas:

• Data Privacy: Strengthen privacy protocols to align with HIPAA and other applicable laws, ensuring patient data remains protected and confidential.
• Bias Audits: Conduct regular evaluations of AI systems to identify and address any algorithmic bias, promoting fairness and impartiality in outcomes.
• Governance: Develop transparent governance structures to oversee AI applications, emphasizing accountability and ethical standards.

Focusing on these areas allows healthcare organizations to mitigate risks effectively while building confidence in their AI-driven initiatives.

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