AI Governance in Healthcare: Security, Ethical Development & Best Practices

In the wake of AI’s rapid development, we are left with the (sometimes forgotten) importance of building secure, ethical, and human-in-the-loop systems. While the EU AI Act is the first legally mandated AI framework to take effect, conversations around AI governance are gaining urgency worldwide. These principles are therefore becoming essential business imperatives to consider at the start of any software build. 

At SRG, they aren’t new territory. They reflect how we’ve always approached software development: speed matters, but never at the expense of accuracy, security, or trust. It goes without saying that AI governance goes hand in hand with these core values. 

Our previous blog posts explored the basics of Agentic AI and successful integration. We wrap up this series by examining the operational impacts of AI and detailing how to ensure transparency and strong governance. 

Read the rest of our blog series: Part 1: Agentic AI vs Traditional Healthcare Automation: What’s the Difference? & Part 2: Beyond Automation: Integrating AI Agents.

Explainability, Transparency & Accountability

From each team member to stakeholders, adopting a ‘transparent approach’ is the most effective way to ensure that no one feels there is a ‘hidden agenda’ in developing a new healthcare solution using AI processes. 

Transparency and explainability have long been the twin pillars of custom software development. Before the rise of AI agents, this simply meant that engineering teams could explain their code, tools, and processes to stakeholders and everyday users. This process relied on open data, clear operational limits, and the translation of technical logic into plain language. While custom development—especially at SRG—has always championed this approach, the complexity of modern AI systems makes it absolutely vital today.

Accountability is an additional important aspect of adopting AI workflows and agents. It means the AI-Augmented Engineer ultimately needs to take responsibility for the outcomes. However useful, AI is not an excuse to shift the blame for mistakes, especially in healthcare environments where these mistakes could mean much more than just a bug in the code. There are specific frameworks and approaches for ensuring accountability, such as assigning ownership and maintaining a clear chain of responsibility.  

Designing Human-in-the-loop AI Systems

Human-in-the-loop AI design (HITL) is essential, especially when the stakes are higher, such as when working with sensitive data like patient records or other medical data. HITL systems help ensure security, accountability, and a proper protocol for when issues arise. The core principles and best practices for designing HITL systems are as follows:

• Risk-tiered autonomy models - the higher the stakes and the less reversible the action, the more directly a human needs to be in the loop before it happens.

  • Human-in-command — AI recommends, human approves before anything happens (used for high-stakes actions: patient care, financial/data exports)

  • Human-on-the-loop — AI acts autonomously but a human monitors and can intervene (used for lower-risk, high-volume tasks like document classification)

  • Human-out-of-the-loop with audit — AI acts fully autonomously, but every action is logged and reviewable after the fact (only appropriate for very low-risk, reversible actions)

• Build approval gates with the right friction level - good systems calibrate friction to risk:

  • Routine, low-risk actions (e.g., auto-classifying a scanned document) → no gate, just logging 

  • Ambiguous or edge cases → flagged and routed to a human reviewer automatically

  • High-stakes actions (patient care, financial transactions) → hard stop, requires explicit human sign-off before proceeding

• Documentation: Log everything - every action needs to be traceable, for both compliance (HIPAA, HITRUST, audit requirements) and so teams can identify failure patterns and improve the system over time. Logs should capture the AI's input, output, confidence, and whether/how a human intervened.

Operational Best Practices

When implementing AI in healthcare development workflows, two things remain at the epicenter: bias and regulatory compliance. 

Auditing for bias

Healthcare models need regular monitoring for accuracy and bias, since clinical patterns, coding rules, and patient populations shift over time. This means scheduled audits and clear ownership over who's responsible for catching drift. Bias can develop over time; even if a model is unbiased at launch, it can easily pick up external data and drift, so ongoing monitoring is essential. 

Testing and review teams should also consider comparing real-world baseline data. In any case, flagging bias and continuous audits should always be kept on record (what was tested, findings, iterations). Documentation is always a good idea for both accountability and regulatory readiness. 

Regulatory Landscape & Security

Healthcare software operates under some of the strictest regulatory requirements of any industry, and regulations are still catching up to the added complexity that AI introduces.  With the emergence of AI-specific regulation (the EU AI Act and evolving FDA guidance, including pathways like 510(k) for AI-enabled medical devices), it’s important to track changes. 

Compliance-first development, combined with ongoing testing, validation, and scrutiny, is a great approach to ensuring that everything built is both secure and compliant with regulatory requirements. Check out our blog post on Compliance-First Development in an AI-Driven Dev Cycle for more information on how to implement this. 

Assessing Risk (We can help!)

Assessing risk means asking what happens if an AI-driven action is wrong, whether that outcome is reversible, and who is affected. For example, a coding error is recoverable, but a clinical decision may not be. Anything touching PHI carries inherent regulatory weight, and understanding where a model is most likely to be uncertain is where human oversight matters most. These assessments should be revisited regularly in the current shifting environment.

SRG offers consulting services for organizations needing a clearer understanding of risks, AI readiness, or navigation of regulatory requirements. Strategic advising is a great way forward if you’re questioning how to make the most of AI while ensuring secure and accurate project builds.

Questions We Help You Answer Before You Build

✓	What does AI readiness actually look like?
✓	What should be prioritized first?
✓	Are there compliance risks?
✓	Is custom development the right move?
✓	How can human oversight be factored in?

Reach out to start the conversation!