Securing AI in Healthcare: Prompt Injection to Unauthorized Actions

Hands-on, one-day AI security training in Southlake, Texas focused on protecting healthcare AI workflows from prompt injection and unnecessary authorization.

Tuesday, January 12, 2027, 9:00 AM–5:00 PM CST

This in-person instructor-led training moves beyond AI security concepts into applied technical validation. Participants examine how untrusted clinical content can introduce indirect prompt-injection risk, how security controls detect that risk before model access, and how authorization boundaries can prevent an AI-enabled workflow from performing actions it does not need.

The training centers on two hands-on labs using Microsoft cloud and AI security technologies. Participants work from observable evidence and learn to distinguish what a system appears capable of doing from what the implementation actually proves.

Class size is limited to 10 participants to preserve the hands-on experience.

The training follows two related security boundaries.

Examine a healthcare AI workflow in which externally sourced clinical content contains an adversarial instruction. Use Azure AI Content Safety Prompt Shields to evaluate the content and examine how application logic enforces the security decision before model invocation.

Examine what an AI-enabled workflow is authorized to do compared with what the business operation actually requires. Evaluate READ versus WRITE requirements and use observable execution evidence to support conclusions about whether access is appropriately constrained.

• Azure AI Content Safety Prompt Shields
• Azure Blob Storage
• Azure Functions
• Azure Logic Apps
• Azure Authorization

• Identifying indirect prompt-injection risk in healthcare AI workflows.
• Validating Prompt Shields detection results.
• Examining application enforcement before model invocation.
• Evaluating authorization requirements against the intended business operation.
• Distinguishing required READ access from unnecessary WRITE capability.
• Using observable technical evidence instead of assumptions to support security conclusions.

• Hands-on experience examining two AI application security boundaries.
• A practical approach for validating security controls through observable evidence.
• Experience connecting prompt-injection detection, application enforcement, and authorization decisions.
• A framework for communicating defensible technical conclusions about AI security risk.
• An NTEKNO Certificate of Completion documenting the training and instructional time.

• The training documents 370 minutes (6 hours 10 minutes) of instructional and hands-on lab time.
• Participants receive an NTEKNO Certificate of Completion and may submit their training documentation to their credentialing organization for consideration.
• CPE or CEU eligibility and any credit awarded are determined by the applicable credentialing organization.

Chief Learning Architect, NTEKNO | Cloud Security SME & Technical Instructor

Serling brings 20 years of technical training experience. Prior to founding NTEKNO, he worked with Microsoft Worldwide Learning as a Principal Content Developer, where his responsibilities included serving as global portfolio custodian for AZ-500: Microsoft Azure Security Technologies and contributing to applied security learning experiences including SC-5002.

Through NTEKNO SecureStack™, he develops scenario-based security training designed around applied technical validation, observable evidence, and defensible security conclusions.

View the complete course details and register directly with NTEKNO.

View course details and registration

Info: Course orientation, healthcare AI security scenario, and the evidence-over-assumption approach used throughout the training.

Event venue & nearby stays

NTEKNO™ SecureStack™ Training | Southlake, 550 Reserve Street, Southlake, United States

Concerts, fests, parties, meetups — all the happenings, one place.

Get the app →