Use of AI in Healthcare: How to Address Security, Privacy, and Ethical Challenges

Like all fields, AI is changing healthcare fast. It helps doctors diagnose diseases faster, reduces admin workload, and improves patient experiences. However, while AI brings many benefits, it also introduces new risks. biased results, data breaches, and ethical dilemmas, to name a few.

Healthcare teams juggling compliance and innovation lose sleep over these threats. This guide points out the main privacy, security, and ethical issues related to the use of AI in healthcare. Plus, it explains common pitfalls and provides a clear playbook to use AI safely.

The Biggest Privacy and Security Concerns with AI in Healthcare

AI relies on data, but in healthcare, that data is highly sensitive Protected Health Information (PHI). These AI models access large amounts of data without proper safeguards. The unchecked spread of AI keeps compliance and security teams up at night. The exposure of PHI and the loss of control over data worry them most.

PHI Exposure Through Prompts, Files, and Outputs

The number one risk is data leakage via user interaction. The way a healthcare worker uses AI creates multiple avenues for data leakage. For instance, a doctor uploads a patient’s medical history to a public AI to draft notes. A nurse or an admin asks AI to summarize a case file. Now, this sensitive information is sitting on someone else’s server.

In addition, it can leak in the following ways:

• Prompts and History: Many mainstream AI are designed to store user prompts to train their next model or debug systems. If a doctor asks a question about a patient’s diagnosis into a public AI prompt, it will be stored on third-party servers.
• File Uploads: Clinicians might upload lab reports or clinical notes in unredacted PDF format into AI for analysis. In other cases, researchers could feed entire datasets into an AI. In doing so, they have handed over PHI.
• Model Outputs: The AI could unintentionally include PHI in its responses to future users. If the output is saved in an unsafe place, it creates another point of exposure.
• Third-party Tools: Typically, every interaction is logged by the AI provider, its cloud host, and integrated tools. Consequently, this creates multiple copies of sensitive data that you can't control.

Data Retention, Secondary Use, and Training Uncertainty

Procurement teams rarely get a straight answer about data retention and secondary use. Even when organizations try to use AI responsibly, vendor policies create grey areas.

Many public AI providers have vague policies regarding what happens to your data after hitting ‘Submit’. Most of these public LLMs keep data forever for secondary use. They use vague terms like ‘service improvements’ that could mean training their next model on your patient data.

If your patient data helps AI learn, it could appear in outputs for other users later. This lack of control is a major red flag for health and compliance. This uncertainty during procurement increases the risk of data breaches and heavy fines from HIPAA and GDPR.

Third-party Vendors and “Shadow AI”

Shadow AI is becoming more common in the healthcare industry. When the approved tools are less capable or limited, clinicians and admin staff find workarounds. They might use an unapproved, consumer-grade AI app on their personal phone without IT’s knowledge.

These unapproved tools create massive governance gaps. The problem is not just the AI itself, but the vendor, cloud providers, partners, and subprocessors handling the data. Even more concerning is that IT has no visibility into the outgoing data and where it is stored. As a result, shadow AI makes audit impossible because data used outside of approved systems does not appear in logs.

Encryption and Secure Storage

Weak encryption leaves PHI vulnerable. Your data is at risk of being stored in unmanaged storage buckets or sent without proper encryption. Verify that PHI is encrypted when it travels to AI (in transit) and when the vendor stores it (at rest). PHI in AI systems must be protected in transit with TLS 1.2+ and at rest with AES-256.

Notably, data is at risk if encryption keys are poorly managed and inaccessible. Further, your PHI is not truly safe if it is stored in an insecure location rather than your secure EHR.

How to Mitigate Privacy and Security Risks for Using AI in Healthcare?

Identifying risk is only half the battle. You can mitigate them through disciplined practices, governed tools, and strict access controls. Here is how healthcare teams can mitigate these risks effectively.

Use a Private, Governed Workspace Instead of Ad-Hoc Tools

The single most effective way to curb Shadow AI is to provide a better, official option to medical staff. Instead of banning AI, give your team a private, governed workspace.

Not to mention, a secure, managed workspace gives you complete visibility into how AI is being used. Plus, you can define access rules and set retention policies. When clinicians have a safe, capable tool, they will stop looking for unsafe workarounds.

Enforce Role-Based Access and Least Privilege

Not everyone in a healthcare organization needs access to PHI. Apply the principle of least privilege to your AI tool.

• Restrict Control: Role-based access control (RBAC) makes sure only authorized personnel can upload or access PHI within the AI platform. Least privilege means giving each role just enough access to do their job.
• Separate Workspaces: Separate workspaces by department (e.g., radiology, oncology, marketing, billing). Teams in marketing and billing departments do not need access to PHI at all.
• Offboarding: Your private workspace should allow for immediate offboarding. When a clinician leaves practice, their access to the AI tool should be revoked immediately and automatically. Consider it part of audit readiness. You should have clear answers when an auditor asks who accessed what and why.

Control Retention, Deletion, and Data Handling Terms

Organizations must ask questions about data handling and transparency before deploying an AI partner. The minimum requirement is BAA and HIPAA compliance.

• Data Retention: Ask vendors about the enforceable retention policy and whether you can set an auto-delete window. Data should be deleted automatically after a set period unless you explicitly choose to keep it.
• Deletion: An organization should have control over the manual deletion of data.
• Storage Location: You have every right to know where your data is physically located. Make sure it complies with regional regulations, such as those requiring data to be kept within the US or EU.
• Secondary Use Policy: Embed it in the contract that your data will NOT be used to train the provider’s models or for any secondary purpose without your consent.

Standardized Prompts and Templates for Safer Use

Human error is unavoidable and often the leading cause of data leaks. Clinicians are busy, often exhausted, and may not always remember what is safe to type. Reduce this risk by creating a library of prompt templates for common tasks. This way, staff can interact with AI without revealing patients' identities or personal details.

Data Encryption as a Security Layer

Encryption is your last and best line of defence. Make sure that your AI solution protects data in transit with TLS 1.2 or higher. Also, confirm that all data stored by the vendor is encrypted with AES-256.

If possible, opt for providers that allow you to manage your own encryption keys. If not, check that the vendor’s key management practices are secure and access-controlled. Not to mention, backups of AI data should be secured and encrypted like the primary data.

Ethical Use of AI in Healthcare: Best Practices

Healthcare professionals need to consider the moral implications of AI. Follow these practices to keep the use of AI responsible:

• Mitigate Bias: AI models trained on limited datasets can produce skewed results. Regularly review outputs to ensure they do not discriminate against specific races, genders, or demographics. Use diverse training data and fairness tools, such as AIF360, to evaluate outputs.
• Transparency: AI’s role should be limited to recommendations, and it should never be allowed to make the final clinical decision. Inform the patient if you are using AI for diagnosis or treatment plan.
• Human Oversight: AI can not replace clinical judgement by a human professional. A qualified professional must review all AI-generated outputs before they are included in patient care.
• Obtain Informed Consent: Obtain informed consent from a patient if you are using patient data for purposes other than treatment (research, quality improvements). Make sure the consent forms clearly mention the use of AI.
• Document Everything: Keep a clear record of all AI tools in use, their purposes, and the data accessed. Plus, you should also mention how you validate AI-assisted outcomes. This documentation is important for both audit and defending against potential malpractice claims.
• Monitor Everything: AI models can “drift” over time and become less accurate. A model that worked well at deployment may begin underperforming months later. Track metrics like output quality and flag anomalies. Establish a feedback loop where doctors can report errors and “hallucinations.”

Private, Secure AI Workspaces by Okara for Healthcare Professionals and Teams

Navigating privacy risks, security checklists, and moral implications can feel overwhelming. Healthcare workers surely need the benefits of AI. However, they can not afford to compromise on compliance and patient trust.

Well, they can use a privacy-focused, governed workspace like Okara. Since it is a secure workspace, healthcare professionals don't need to resort to risky, public tools.

Medical teams can upload PHI to this encrypted platform with confidence. More importantly, your data is never used to train models. Period.

Okara gives access to 20+ AI models, so try it for free now and experience a safer way to use AI.

Frequently Asked Questions

Are public AI chats safe for use in healthcare?
No, public AI chats (like the free version of ChatGPT) are generally not safer for healthcare use. They store prompts for training and usually do not sign BAAs. Plus, uploading PHI to them can lead to a data breach and non-compliance.

What are the most common privacy concerns with AI in healthcare?
The most common privacy concerns include PHI leaks from prompts/files, unclear data retention and deletion, shadow AI, and weak encryption.

Is it compliant to use generative AI with patient data (PHI)?
Yes, it can be, but only under specific conditions. Use HIPAA-aligned tools with strong encryption, least privilege, and no secondary use. Additionally, verify data handling policies and sign a BAA before committing.

Is Okara HIPAA compliant?
Yes, Okara is HIPAA compliant and meets the privacy and governance requirements of healthcare teams. In addition to encryption, it signs a Business Associate Agreement (BAA) to ensure PHI is handled securely.

How do data retention and “used for training” policies affect healthcare AI risk?
If the vendor retains data indefinitely or uses it for training, you lose the ability to secure that data. Worse, sensitive patient information could potentially appear in outputs for other users.