Navigating the landscape of AI security requires fluency in the language of compliance and governance. This section provides a quick reference to the key standards, regulations, and protocols that define the operational boundaries for AI systems. A red teamer’s understanding of these frameworks is critical for identifying compliance gaps that often double as security vulnerabilities.
Key Regulatory, Security, and AI-Specific Frameworks
The following table lists common acronyms you will encounter when assessing the legal, ethical, and security posture of AI deployments.
| Acronym | Full Name | Description for AI Red Teaming |
|---|---|---|
| AIA (EU) | Artificial Intelligence Act | A landmark EU regulation classifying AI systems by risk level (unacceptable, high, limited, minimal). High-risk systems face stringent requirements, creating a significant compliance-driven attack surface to test. |
| API | Application Programming Interface | The primary communication method for many AI models (e.g., LLM-as-a-service). Red teaming often focuses on exploiting API vulnerabilities like insecure authentication, rate limiting flaws, and injection attacks. |
| CCPA/CPRA | California Consumer Privacy Act / California Privacy Rights Act | US state-level data privacy laws granting consumers rights over their personal data. Relevant for testing how AI systems handle, process, and delete user data upon request, especially data used for training or inference. |
| GDPR | General Data Protection Regulation | The EU’s comprehensive data protection law. Violations related to training data (e.g., right to be forgotten, data minimization) are a key area for compliance testing and can indicate systemic data handling weaknesses. |
| gRPC | gRPC Remote Procedure Call | A high-performance RPC framework often used for communication between microservices in an AI pipeline. Testing focuses on its security configuration, authentication mechanisms, and vulnerability to denial-of-service attacks. |
| HIPAA | Health Insurance Portability and Accountability Act | A US law governing the security and privacy of protected health information (PHI). When AI systems process medical data, red teaming must validate compliance with HIPAA’s strict security and access control rules. |
| HTTP/HTTPS | Hypertext Transfer Protocol / Secure | The foundational protocol of the web. Testing AI web interfaces involves standard web application security checks (e.g., OWASP Top 10) applied to the specific context of the AI’s inputs and outputs. HTTPS is mandatory. |
| ISO/IEC 27001 | Information Security Management | An international standard for managing information security. An organization’s certification provides a baseline for security controls, which a red team can use as a starting point for testing their real-world implementation. |
| ISO/IEC 42001 | Artificial Intelligence Management System | The first international standard for AI management systems. It provides a framework for responsible AI development and deployment, offering a checklist of controls and processes to test against. |
| NIST AI RMF | NIST AI Risk Management Framework | A voluntary framework from the US National Institute of Standards and Technology for managing risks associated with AI. It provides a structured approach (Govern, Map, Measure, Manage) that can guide red team engagement objectives. |
| NIST CSF | NIST Cybersecurity Framework | A set of voluntary guidelines for improving critical infrastructure cybersecurity. Its functions (Identify, Protect, Detect, Respond, Recover) offer a comprehensive model for structuring a red team assessment of an AI system’s resilience. |
| PCI DSS | Payment Card Industry Data Security Standard | A security standard for organizations that handle branded credit cards. If an AI system is part of a payment processing pipeline, it falls under PCI DSS scope, requiring rigorous testing of data handling and storage controls. |
| REST | Representational State Transfer | An architectural style for creating web services, commonly used for APIs. RESTful APIs for AI models are frequent targets for tests involving parameter tampering, access control bypasses, and injection attacks. |
| SOC 2 | Service Organization Control 2 | A framework for managing customer data based on five “trust service principles” (security, availability, processing integrity, confidentiality, privacy). A SOC 2 report details an organization’s controls, providing valuable intelligence for a red team. |