Ever since Artificial Intelligence (AI) became mainstream, the technology has been on a nonstop rollout, impacting various industries and individuals alike. In the healthcare sector, AI is transforming processes faster than most regulations can keep up.
Despite offering promising capabilities, trust remains the missing prescription. Lack of data governance coupled with AI governance leads to heightened data privacy and security risks, which erode patient confidence and strategic business decision-making.
Inadvertent data exposure risks limit healthcare organizations from fully utilizing the technology and maximizing its potential. What further makes matters worse is the inability and lack of awareness regarding AI governance, begging the question: What is AI governance?
What is AI Governance in Healthcare?
AI governance in healthcare refers to the organization’s multi-layered hierarchical approach to developing, deploying, and monitoring the use of AI technologies responsibly within the healthcare institution. It aims to ensure that AI technologies amplify health-related operations without compromising on patients’ Protected health information (PHI).
AI governance in healthcare consists of well-defined AI usage policies, controls, and compliance initiatives that assure patient data remains private and secure from emerging threats, is processed, stored, and shared with fairness and a degree of accountability.
AI governance in healthcare typically resides within the power of a dedicated AI governance team/individuals whose sole responsibility is to manage and oversee the use of AI technologies within the institution, aligning it with regulatory requirements, patient expectations and clinical amplification.
According to Forrester's State of AI Survey, 2024, 79% of AI decision-makers said that AI governance enables their company to quickly adjust to shifting market and regulatory situations.
The Regulatory Landscape: Core Compliance Pillars
There’s no escaping AI, and healthcare institutions understand that healthcare workers, as well as teams within the organization, will inevitably utilize AI technologies. The key for healthcare institutions is to govern the use of AI technologies, whether such tools are developed by the institution itself or onboarded from third parties.
AI governance necessitates the following core compliance pillars:
a. Privacy of Patient Health Data
Healthcare regulations determine the fate of an AI tool. At the core of healthcare regulations lies patient health data safety. To ensure continuous data privacy and security of health data, AI systems must undertake strict checks to certify they are safe to utilize in a healthcare environment, can be relied on with accurate information, and improve processes rather than posing a risk.
Global healthcare data privacy laws, such as HIPAA in the U.S. and GDPR in the EU, mandate organizations to certify the lawful processing of patient data and obtain consent before data processing, ensure adequate data security measures are in place, conduct Data Protection Impact Assessments (DPIAs), affirm data minimization, etc. Noncompliance can lead to hefty penalties.
b. Algorithmic Transparency
There’s no confidence in the AI system if it can’t be trusted. AI tools must ensure transparency by certifying that the AI model isn’t riddled with poor data quality or isn’t ineffective. Additionally, AI users must be able to understand how the AI algorithm makes a diagnosis.
Fortunately, AI regulations require AI developers to disclose how the model makes a judgment, ensuring transparency into the decision-making process and accountability. Global AI-specific regulations such as the EU AI Act, South Korea's Basic Act on AI Advancement and Trust and others mandate the same.
c. Ethical, Fair and Non-Discriminatory
Unlike humans, who may get biased, AI models are expected to be free from unfair and discriminatory practices. They must ensure the health data of various populations isn’t treated differently. This required fairness in testing and embedding regular ethical and moral checks throughout the AI model lifecycle.
d. Transparent, Accountable, and Resilient
Transparency, accountability, and resilience are at the core of a robust AI model. Undisclosed training datasets clouded by secrecy hinder trust in the AI model and escalate noncompliance. Most AI governance frameworks demand transparency into how an AI model is developed and deployed.
This is in addition to the requirement of an AI oversight board that is responsible for the model’s behavior and output by conducting vulnerability testing and other threat detection practices, ensuring no data poisoning or model manipulation occurs, and the ability to withstand evolving threats that could result in the exposure of sensitive health data.
e. Continuous Monitoring and Improvement
Post development and deployment, there’s a critical need to retain human oversight that’s engaged in continuously monitoring to observe abnormalities or threat patterns, perform regular auditing of the model, and consistently improve the model to adapt to emerging requirements.
AI regulations mandate the same requirements, enabling oversight teams to ensure early detection of AI model drift, bias, or safety risk. Continuous oversight turns governance from a static checkpoint into a living system of trust.
AI Governance Policies and Controls in Healthcare
Governance requires the implementation of robust policies and controls that manage AI usage. These include:
a. Establishing an AI Use Policy
The first and foremost step is to define the scope of the AI use policy, which details the responsible use of AI tools by authorized individuals. It outlines what AI is, which AI tools are authorized for use, and which data can be provided to the AI model, the degree of reliability, who has oversight, etc. The policy clarifies that every AI model or tool, whether for operational or clinical use, has a declared purpose, risk score, and a formal approval process.
b. AI/Data Governance and Privacy Policy
AI governance and data governance are similar in nature, where one aims to regulate the responsible development, deployment, and management of AI systems, while the other ensures that data is secure, private, accurate, and accessible throughout its lifecycle. Both governance initiatives, coupled with a privacy policy, reinforce consent, data minimization, security, data transfers and other requirements, establishing uniform standards. Additionally, establishing an accountability policy is also crucial to declare senior ownership and a formal oversight watchdog for all AI-related activities within the healthcare institution.
These policies detail that AI systems ensure the ethical, private, and secure handling of sensitive patient data that complies with data privacy regulations and AI-specific regulations such as HIPAA, GDPR, CCPA/CPRA, EU AI Act, etc. Healthcare institutions must conduct various assessments (readiness, risk, privacy and protection impact, cross-border transfer impact), reinforce data minimization and de-identification principles, and establish contractual obligations for third-party AI vendors to protect patient data.
d. Clinical Safety and Risk Management Policy
This policy prioritizes patient safety over AI output by supporting clinical judgment and not replacing it with a standard AI algorithmic output. Such an AI tool must be carefully evaluated by manual oversight, with the ability for operators to override AI judgments at any time the model makes an error. This also stresses the need for healthcare-specific AI tools designed to handle high-risk scenarios without impacting the patient or their health data.
e. AI Lifecycle Risk Management Policy
This policy ensures that AI models and systems remain safe and reliable throughout their life, from development to ultimately being deployed and utilized within a healthcare environment. Each development stage of the AI model must have manual oversight and a stringent approval process that eliminates errors, inefficiencies, or risks that may impact patients.
Automate AI Governance with Securiti
Large enterprises orchestrating GenAI systems face several challenges: securely processing extensive structured and unstructured datasets, safeguarding data privacy, managing sensitive information, protecting GenAI models from threats like AI poisoning and prompt injection, and unscalable GenAI pipeline operations.
Securiti’s Genstack AI Suite removes the complexities and risks inherent in the GenAI lifecycle, empowering organizations to swiftly and safely utilize their structured and unstructured data anywhere with any AI and LLMs.
It provides features such as secure data ingestion and extraction, data masking, anonymization, and redaction, as well as indexing and retrieval capabilities. Additionally, it facilitates the configuration of LLMs for Q&A, inline data controls for governance, privacy, and security, and LLM firewalls to enable the safe adoption of GenAI.
- AI model discovery – Discover and catalog AI models in use across public clouds, private clouds, and SaaS applications.
- AI risk assessment – Evaluate risks related to AI models from IaaS and SaaS, and classify AI models as per global regulatory requirements.
- Data+AI mapping – Map AI models to data sources, processing, potential risks, and compliance obligations, and monitor data flow.
- Data+AI controls – Establish controls on the use of data and AI.
- Regulatory compliance – Conduct assessments to comply with standards such as NIST AI RMF, EU AI Act, and more than twenty other regulations.
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