The healthcare sector is one of the most innovation-rich industries. But every new technology must prioritize the security of protected health information (PHI). Strong data security and adherence to regulatory requirements are non-negotiable, given the severe consequences a breach can cause. Integrating AI algorithms in healthcare cybersecurity is the smart option for achieving and sustaining Governance, Risk and Compliance (GRC).
Developing an ethical artificial intelligence framework for healthcare enhances cybersecurity and facilitates GRC compliance. It’s a convenient and effective solution in today’s healthcare sector, where cyberattacks have surged significantly. According to an FBI Internet Crime Report, 2024 saw 444 cyber incidents targeting the healthcare industry, with 206 being data breach incidents and 238 ransomware attacks.
To successfully integrate AI in healthcare, GRC professionals and leaders in this space must get it right the first time. Here’s a detailed checklist to get you started.
Cybersecurity Risks of AI Algorithms In Healthcare
While all cybersecurity risks can ultimately expose critical patient data to unauthorized personnel, the risks take different paths. Healthcare leaders should identify these potential risks early and close loopholes before they escalate into costly breaches. These are the common risks to consider.
Ransomware Attacks
Because healthcare institutions hold protected health information (PHI) of patients, they’re popular targets for ransomware attacks. PHI is more valuable on the black market than other personally identifiable information (PII), since medical records cannot be replaced once breached.
Additionally, downtimes in the healthcare sector can be life-threatening, especially for patients with ongoing medical conditions. Cybercriminals capitalize on this nature to engineer ransomware attacks, knowing organizations will be compelled to quickly pay up to avoid these consequences.
Legacy System Vulnerabilities
Using outdated healthcare systems in a tech-first world exposes organizations to multiple operational risks. Outdated software often lacks proper security protocols, such as multi-factor authentication and industry-standard encryption, required to fend off today’s sophisticated cyberattacks.
Third-Party Risks
Third-party software-as-a-service companies, known as Business Associates (BA), provide AI solutions and sustained support to healthcare institutions. This can give them access to critical healthcare data. If a security breach targets a third-party vendor, it affects their clients by extension. For insight into automating these risk checks, read about Automating Third-Party Risk Management with AI-Enabled GRC.
Traditional Risks
Conventional cybersecurity risks, such as insider threats, also affect modern healthcare systems. For instance, vindictive employees may deliberately expose data, while accidental errors create operational risks that can cascade across the organization.. Also, accidental human mistakes such as data entry errors can trigger cyber threats, especially within institutions with a high patient population.
Increasingly Complex Regulations
The requirements of cybersecurity regulations impacting healthcare, such as the Health Insurance Portability and Accountability Act (HIPAA) and SAMHSA, are constantly being updated. Managing the shifting rules covering these regulations across multiple jurisdictions (state and local) poses operational challenges.
How the Growing Popularity of Precision Medicine and Personalized Health Data Has Scaled Cybersecurity Risks
The adoption of precision medicine and wearable devices has amplified both compliance risk and cybersecurity exposure.
Additionally, these innovative practices require a higher level of interconnectivity of various healthcare technologies, such as Internet of Things (IoT) and electronic health records. Such interconnectedness creates opportunities for cyberattackers.
AI Algorithms in Healthcare: Strategies for Cybersecurity Risks
To navigate new cybersecurity risks, you need innovative AI-based strategies:
- Conduct extensive third-party risk assessments: Before onboarding third- (or Nth)-party providers, ensure they have security practices that align with your organization’s requirements. Assess each provider at the engagement level to ensure the proper vetting for the type of data they will handle.
- Implement strict access controls: Establish definitive internal access controls that designate data access privileges within your organization’s personnel with AI-based access control decision engines like Random Forest.
- Modernize legacy healthcare systems: Modernize outdated systems to reduce potential risk tied to legacy infrastructure.
- Conduct regular AI audits and impact assessments: Conduct regular AI audits to detect anomalies, reduce bias and avoid compliance risk.
5 Key Areas AI Algorithms in Healthcare Should Cover
Applying AI algorithms in healthcare goes beyond checking compliance boxes. It takes a wider and more integrated scope encompassing multiple areas that fortify your organization’s entire cybersecurity resilience. These are the five essential areas your AI frameworks for healthcare should address.
1. Transparency and Documentation
Your framework should clearly document your development processes to transparently evaluate the effectiveness of your AI algorithms. Clear documentation supports scientific and regulatory reviews and builds trust in the algorithms, your staff and end users.
When regulators come knocking, they’ll have a well-established pathway for tracing back the development processes of your AI framework and all the decision points involved. Avenues you can follow to expedite transparency and documentation include open sourcing your code, documenting your processes on peer-reviewed publications and sharing your datasets.
2. Risk Management Throughout the Product Development Lifecycle
What is the meaning of compliance risk management?
Compliance risk management is the practice of identifying, evaluating and mitigating risks that could cause an organization to fall out of step with laws, regulations or internal policies. In healthcare, it means safeguarding patient data, maintaining trust and ensuring adherence to strict standards like HIPAA or GDPR while adopting new technologies such as AI.
In AI frameworks, compliance risk management goes beyond box checking. It is a structured process that ensures AI strengthens both security posture and compliance standing. To put this into action, here are the five structured steps to follow throughout the product development lifecycle:
- Security risk analysis: Establish the intended use and the security characteristics of the AI framework or device that can be a potential target of cybercrime.
- Security risk evaluation: Weigh the impact of threats on assets and business processes.
- Security risk control: Develop an action plan with mitigation strategies and updated internal controls.
- Conduct residual security risk acceptability evaluation: Assess if the remaining operational risks are acceptable.
- Prepare a security risk management report: Document the results for regulators and senior management oversight.
3. Clinical Evaluation and Validation
In this step, review all available evidence supporting the AI system’s viability as it relates to its intended use. Ascertain that your AI framework has top safety and performance features that match the standards of its intended purpose. The aim is to evaluate the total product life cycle (TPLC) of your AI system from the development stage to analytical and clinical validation and post-market surveillance.
4. Data Quality
Data is by far the most essential component of training AI algorithms in healthcare. You must guarantee the accuracy and authenticity of the data you feed your AI framework to get accurate results.
Your datasets must be clinically relevant to avoid negative outcomes like AI errors or bias. To get it right, focus on the 10 Vs of big data, encompassing crucial characteristics like validity, velocity and veracity.
5. Privacy and Data Protection
Review all applicable regulatory compliance frameworks, such as HIPAA, GDPR, CCPA, CPRA and pertinent state privacy laws that your AI system must abide by. Understanding the continually changing world of privacy and data protection laws from the development stage on allows for dynamic AI systems that can accommodate tweaks fast without extensive reengineering.
A Quick AI Governance Checklist To Keep You Compliant
Reference the following healthcare governance checklist to manage risks and stay compliant:
- Prioritize patient-centric values such as patient privacy and informed consent in your AI applications.
- Set up a cross-disciplinary team to assess the impact that AI technologies have on patient care.
- Audit your AI algorithms regularly to appraise quality, filter biases and maintain transparency.
- Design a structure to continuously track and adapt to new AI-related governance policies.
- Implement multi-disciplinary collaboration involving law, social sciences and ethics professionals when designing your AI framework.
Before Deploying Your AI Framework, Expand Your Knowledge
AI has the potential to transform healthcare, but it also introduces new layers of compliance risk and cybersecurity challenges. Building an ethical framework is not just about adopting technology; it is about embedding governance, accountability and transparency into every stage of your business processes.
By strengthening internal controls, keeping senior management actively involved and aligning with evolving regulatory requirements, healthcare leaders can create AI systems that protect patients, reduce operational risks and inspire trust. Most importantly, a structured approach to compliance risk management ensures innovations in precision medicine, data-driven care and connected devices enhance outcomes instead of exposing vulnerabilities.
Getting this right the first time is critical. The cost of a single data breach or failed compliance audit can be devastating, but the right governance model turns AI into an asset rather than a liability.
If you are ready to take the next step in designing an AI governance model that is both practical and ethical, download our detailed white paper to learn actionable tips for implementing an effective AI framework for healthcare.
