Domain-Specific AI Security

Overview of AI security challenges across industry verticals including healthcare, finance, autonomous vehicles, content moderation, education, and customer service. Domain-specific threat models, regulations, and testing approaches.

AI security is not one-size-fits-all. The same prompt injection technique has radically different implications when applied to a customer service chatbot versus a clinical decision support system. Domain-specific red teaming requires understanding the unique threat models, regulatory requirements, data sensitivities, and failure consequences in each vertical.

Why Domain Matters

Different Domains, Different Stakes

Domain-Specific Threat Modeling

Generic AI threat models must be extended for each domain:

Generic AI Threats          Domain-Specific Amplifiers
─────────────────          ──────────────────────────
Prompt Injection      →    + Regulatory violation
                           + Domain-specific harm
                           + Protected data exposure

Data Exfiltration     →    + HIPAA/PCI breach
                           + Mandatory reporting
                           + Class action liability

Safety Bypass         →    + Physical safety risk
                           + Professional malpractice
                           + Criminal liability

Model Manipulation    →    + Clinical misdiagnosis
                           + Financial fraud
                           + Safety system failure

Adapting Your Testing Approach

1. 1

   Understand the Regulatory Landscape

   Before testing, identify all applicable regulations (HIPAA, PCI-DSS, COPPA, FERPA, EU AI Act). These regulations define what constitutes a reportable incident, which expands your vulnerability assessment scope.

2. 2

   Map Domain Data Classifications

   Identify what data the AI system accesses and generates. Healthcare: PHI. Finance: PII and financial records. Education: minor student data. Each data classification has specific handling requirements that AI systems may violate.

3. 3

   Define Domain-Specific Impact Scenarios

   Translate generic attack outcomes into domain-specific impacts. "The model outputted incorrect information" becomes "the model provided an incorrect drug interaction warning" in healthcare or "the model recommended an unsuitable financial product" in finance.

4. 4

   Identify Domain-Specific Attack Vectors

   Each domain has unique attack surfaces. Healthcare: DICOM images, HL7/FHIR interfaces. Finance: trading APIs, payment systems. Autonomous vehicles: sensor inputs, V2X communications.

5. 5

   Calibrate Engagement Intensity

   Higher-consequence domains require more thorough testing, more conservative scoping, and more detailed reporting. A healthcare AI engagement should be more rigorous than a marketing chatbot assessment.

Cross-Domain Patterns

Despite their differences, certain patterns appear across all domains:

Pattern 1: Overreliance on AI Outputs

In every domain, the most common root cause of AI-related harm is humans trusting AI outputs without verification. Clinicians trusting diagnostic suggestions, financial analysts trusting risk assessments, moderators trusting classification decisions — the failure mode is consistent even when the consequences differ.

Pattern 2: Training Data Domain Mismatch

AI systems trained on general data often perform unpredictably when deployed in specialized domains. Medical terminology, financial jargon, legal language, and domain-specific edge cases are underrepresented in general training data, leading to confident but incorrect outputs.

Pattern 3: Regulatory Compliance Theater

Organizations deploy AI systems that are technically HIPAA-compliant or PCI-compliant in their data handling but violate the spirit of these regulations through AI-specific channels. For example, a system may properly encrypt PHI at rest but leak it through model-generated summaries that are not treated as PHI.

Engagement Scoping by Domain

Getting Started with Domain-Specific Red Teaming

If you are transitioning from general AI red teaming to domain-specific work:

1. Learn the domain basics — You do not need to be a doctor to test healthcare AI, but you need to understand HIPAA, PHI categories, and clinical workflows
2. Partner with domain experts — Engage subject matter experts who can validate whether your findings have real-world impact in their domain
3. Study domain incidents — Review domain-specific AI failures to understand what types of errors have the highest consequences
4. Build domain test suites — Create reusable test cases tailored to domain-specific data types, terminology, and scenarios

For foundational attack techniques that apply across all domains, see Prompt Injection, Agent Exploitation, and Defense Evasion.

Related Topics

• Healthcare AI Security -- clinical AI risks and HIPAA implications
• Financial AI Security -- trading, credit, and regulatory compliance risks
• Autonomous Vehicle AI Security -- physical safety implications of AI failures
• Content Moderation AI -- evasion techniques and moderation bypass
• Lessons Learned from AI Security Incidents -- cross-domain incident patterns

References

• "AI Risk Management Framework (AI RMF 1.0)" - National Institute of Standards and Technology (2023) - Risk management guidance with domain-specific considerations for high-consequence AI deployments
• "EU Artificial Intelligence Act: Annex III High-Risk AI Systems" - European Parliament (2024) - Classification of high-risk AI systems by domain including healthcare, finance, and transportation
• "Sector-Specific AI Risk Analysis" - World Economic Forum (2024) - Cross-industry analysis of AI deployment risks and domain-specific failure modes
• "AI Incident Database" - Responsible AI Collaborative (2024) - Domain-categorized database of AI failures and incidents across industry verticals