Explainability and transparency play what roles in SecAI+, and why are they important?

• A. Explainability Enables Stakeholders To Understand Model Decisions; Transparency Supports Accountability; Important For Trust, Compliance, And Debugging Security Issues
• B. Explainability Slows Training; Transparency Increases Latency; Not Important
• C. Explainability Guarantees No Errors; Transparency Replaces Governance
• D. Explainability Is Optional; Transparency Is Optional

Answer: (A)

Explainability makes model decisions understandable for humans, while transparency provides visibility into how the model was built, what data it uses, and how its processes flow. In SecAI+ this matters because it lets security teams, auditors, and managers interpret predictions, assess risk, and verify that the system behaves as intended. When you can see why a decision was made and what factors influenced it, you gain trust, meet regulatory and policy requirements, and can more easily debug or investigate security issues by tracing outcomes back to the data and features involved. It also supports accountability: if a decision leads to an incident, you can explain what influenced it and identify where improvements are needed. Statements suggesting that explainability slows training, that transparency isn’t important, or that they are optional don’t fit the reality of secure, responsible AI practice, and they don’t reflect the ongoing need for governance and reliability in SecAI+.