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 # **1.2 Principles of OWASP AI Testing**
 
-Effective AI testing is built upon four macro domains: Security, Privacy, Responsible AI, and Trustworthy AI Systems. We chose these four core domains because they collectively address the full range of AI risks. Security ensures resilience against adversarial and infrastructure threats. Privacy prevents unintended data exposure and inference attacks. Responsible AI focuses on ethical behavior and fairness, guarding against bias and misuse. Trustworthy AI Systems maintain ongoing confidence through explainability, stability, and governance alignment. Together, they form a comprehensive framework for validating, controlling, and sustaining safe and reliable AI deployments. Each domain includes key principles that guide the evaluation of modern AI applications.
+**Trustworthy AI** is achieved through the combined strength of three foundational domains — **Responsible AI (RespAI)**, **Security AI (SecAI)**, and **Privacy AI (PrivacyAI)**.
+These domains form the *testable foundation* of Trustworthy AI within the **OWASP AI Testing Framework**.
+While broader definitions of Trustworthy AI may also encompass governance, reliability, and accountability, these qualities are enabled and operationalized through **continuous testing** across the three domains below.
+
+Effective AI testing integrates these dimensions holistically:
+
+* **Security** ensures resilience against adversarial and infrastructural threats.
+* **Privacy** protects confidentiality and prevents misuse or inference of sensitive data.
+* **Responsible AI** enforces ethical, transparent, and bias-resistant behavior.
+
+Together, they form a unified structure for validating, controlling, and sustaining **Trustworthy AI Systems** : systems that operate safely, predictably, and in alignment with human values.
+
+### **1. Security (SecAI)**
+
+AI systems must be resilient to adversarial threats and systemic exploitation, ensuring protection across the full AI stack and lifecycle.
+
+* **Prompt & Input Control:** Safeguard system prompts, instructions, and user inputs from injection or manipulation.
+* **Adversarial Robustness:** Test resistance to evasion, poisoning, model theft, jailbreaks, and indirect prompt injections.
+* **Infrastructure Security:** Assess API endpoints, plugins, RAG pipelines, and agentic workflows for vulnerabilities.
+* **Supply-Chain Risk:** Inspect models and dependencies for poisoning, tampering, or third-party compromise.
+* **Continuous Testing:** Integrate automated adversarial and dependency scanning into CI/CD pipelines.
+
+### **2. Privacy (PrivacyAI)**
+
+Ensure confidentiality and user control over data exposed to or generated by AI systems throughout the model lifecycle.
+
+* **Data Leakage Prevention:** Detect unintended disclosures of training data, private context, or user inputs.
+* **Membership & Property Inference Resistance:** Evaluate susceptibility to attacks that infer if data was part of training.
+* **Model Extraction & Exfiltration:** Simulate adversaries attempting to replicate proprietary models or weights.
+* **Data-Governance Compliance:** Validate adherence to principles of minimization, purpose limitation, and consent management.
+
+### **3. Responsible AI (RespAI)**
+
+Promote ethical, safe, and aligned system behavior through ongoing evaluation and mitigation.
+
+* **Bias & Fairness Audits:** Identify discriminatory outputs across demographic groups and edge cases.
+* **Toxicity & Abuse Detection:** Test resilience against producing or amplifying harmful or misleading content.
+* **Safety Alignment:** Validate adherence to alignment constraints and resistance to jailbreak or role-play exploits.
+* **Guardrail Coverage:** Evaluate safety filters, refusal mechanisms, and abuse-prevention logic.
+* **Human-in-the-Loop Controls:** Ensure escalation and review pathways for high-impact decisions.
+
+### **4. Trustworthy AI Systems**
+
+**Trustworthy AI = RespAI + SecAI + PrivacyAI**, supported by governance, transparency, and monitoring mechanisms that preserve trust over time.
+
+* **Explainability:** Ensure users and auditors can interpret how and why decisions are made.
+* **Consistency & Stability:** Verify predictable responses under prompt variations and regression tests.
+* **Continuous Monitoring:** Apply runtime observability, drift detection, and automated anomaly alerting.
+* **Lifecycle Testing:** Extend validation from design to deployment and post-market phases.
+* **Policy & Regulatory Alignment:** Map testing and validation processes to frameworks such as **NIST AI RMF [1]**, **ISO/IEC 42001 [2]**, and the **OWASP Top 10 for LLMs [3]**.
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+Effective AI testing is built upon three macro domains: Security, Privacy, Responsible AI,to build Trustworthy AI Systems. We chose these 3 core domains because they collectively address the full range of AI risks. Security ensures resilience against adversarial and infrastructure threats. Privacy prevents unintended data exposure and inference attacks. Responsible AI focuses on ethical behavior and fairness, guarding against bias and misuse. Together, they form a comprehensive framework for validating, controlling, and sustaining safe and reliable AI deployments. Each domain includes key principles that guide the evaluation of modern AI applications.
 
 ### **1\. Security**