Building Visibility to Enable Secure Healthcare AI Adoption

Generative AI isn’t just a buzzword in healthcare anymore—it’s table stakes. Physicians, nurses, and analysts are tapping into generative AI to transform patient care. Whether it’s summarizing notes into a patient record, coding faster with AI assistants, or automating time-consuming documentation, the technology promises massive improvements in operational efficiency and clinical accuracy.

But as healthcare embraces AI, most organizations are flying blind. Your staff isn’t waiting for enterprise rollouts—they’re solving problems right now. There’s that cardiologist using ChatGPT to streamline discharge summaries, the nurse with a "smart" summarization tool, or the analyst uploading "anonymized" electronic health record (EHR) exports to a coding assistant. In every case, they’ve jumped ahead. Unfortunately, what they see as innovation, your network sees as risk.

This is the uncomfortable truth: AI users in your organization may just have triggered your next biggest security incident. Here's why—and how to fix it.

Shadow AI: The Elephant in the Room

Every healthcare leader knows that AI adoption is happening. It’s already underway in more than 60% of organizations piloting or implementing enterprise AI solutions. But here’s the problem: the real number is likely much higher because shadow AI tools—AI systems adopted by users without enterprise approval—are flying under the radar.

When one healthcare organization deployed inline WebSocket inspection, they discovered 31 unique AI tools being used within 72 hours. None of them had been approved, evaluated for compliance, or configured to safely handle Protected Health Information (PHI). AI-related traffic across enterprises has increased 3,000% over the last year, and 10–20% of that traffic already violates policies. This widespread activity creates significant blind spots for security teams—and significant opportunities for attackers.

Shadow AI Risks Are Rising

AI has brought unprecedented opportunities, but it has also introduced unique risks. Without visibility into what tools are being used and how your people interact with AI, you risk:

• PHI exposure: Shadow AI users may unintentionally upload sensitive patient data, creating major compliance risks.
• Vulnerability to AI-related attacks: Threat actors are using AI for sophisticated phishing campaigns, compromise tactics like prompt injections, and exploiting organizational blind spots. AI-fueled attacks jumped 146% from 2023 to 2025, with healthcare data theft rising 92%.
• Regulatory fines: With updated regulations like the proposed HIPAA Security Rule and the HITRUST AI Security Framework, compliance gaps related to AI adoption could lead to millions in penalties.

Shadow AI isn’t a future problem. It’s happening in your organization now.

Why WebSocket Blindness Keeps You in the Dark

Most security teams already rely on SSL/TLS inspection for visibility. While this approach may work for traditional web traffic, it isn’t suited for generative AI platforms like ChatGPT, Microsoft Copilot, Claude, or Google Gemini. These modern platforms don’t communicate in the simple HTTPS formats you’re used to inspecting.

Instead, they rely on WebSockets—persistent, bidirectional connections that continuously stream complex payloads. This creates a black box for organizations without inline WebSocket inspection. Your firewall may flag a session to an AI domain, but it won’t reveal what’s inside that session.

Without WebSocket Inspection, You Miss:

• User attribution: Who sent the prompt?
• Sensitive content: PHI, MRNs, ICD-10 codes embedded in AI requests.
• Risks in action: Prompt chaining, jailbreak attempts, or hallucinated clinical recommendations.

With WebSocket Inspection, You Gain:

• Full prompt and response visibility in real time.
• Identification and blocking of policy violations before sensitive data leaves your network.
• Tied attribution of AI sessions to users and devices for rich audit trails.
• Detection of risky or malicious prompt activities.

In short, WebSocket inspection transforms AI-related blind spots into protected environments where you can allow safe use of AI without compromise.

Governance and Innovation: Striking the Balance

Blocking AI outright isn’t realistic. Your clinicians, analysts, and staff will find ways to adopt tools—often through less secure methods that increase risk. Instead, organizations need to embrace AI responsibly by anchoring their governance model in Zero Trust principles.

Step 1: Focus on Visibility First

• Deploy WebSocket inspection to see the tools and data your staff are already using.
• Monitor prompts at the application level with full attribution (who, when, what).
• Flag risky patterns like jailbreak attempts or PHI-laden queries in real time.

Step 2: Govern Approved AI Solutions

• Build a structured approval process for generative AI tools, defining requirements for data retention, licensing, and compliance certifications like HITRUST or HIPAA.
• Explicitly block unsanctioned AI tools or browser extensions at the network level while enabling access to approved solutions.

Step 3: Secure the Data

• Use contextual detection like regular expressions or natural language processing (NLP) to identify and block sensitive data (e.g., SOAP notes, clinical codes, or names) from being transmitted accidentally.
• Build immutable audit trails for all AI-related activity, enabling continuous improvement and compliance reporting.

The Bottom Line: AI Can’t Come at the Cost of Safety

Your people are excited about AI—and for good reason. From saving hours on documentation to improving diagnostic processes and reducing errors, generative AI offers healthcare organizations incredible potential. But adoption must come with safety, visibility, and governance.

With inline WebSocket inspection and a Zero Trust approach, you can:

• Protect PHI while enabling safe AI-driven workflows.
• Identify and block shadow AI usage without stifling innovation.
• Comply with emerging regulations and maintain trust with patients and stakeholders.

Generative AI is inevitable. The question isn’t whether your organization will use it; the question is whether you’ll use it securely. Your first step to building a safer, AI-enabled future starts with visibility.

Download our eBook to learn more about how you can secure AI while enabling innovation.