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Security Architecture in AI Systems (CY0-001) Flashcards
CompTIA SecAI+ CY0-001 Flashcards
| Front | Back |
| How can AI bias impact system security | Biased AI models may lead to unfair or unexpected decisions, increasing risks or vulnerabilities |
| How can data poisoning attacks compromise AI models | By introducing malicious data to skew training outcomes or performance |
| How can dynamic risk assessments enhance AI security | By continuously evaluating potential threats and adapting security measures accordingly |
| How can encryption enhance AI system security | By safeguarding data at rest and in transit from unauthorized access |
| How can secure APIs improve AI system architecture | By ensuring communication channels prevent unauthorized access or exploitation |
| How can secure model deployment mitigate risks in AI | By implementing safeguards like containerization and runtime security |
| How do adversarial attacks threaten AI models | By exploiting weaknesses in models to alter predictions or outcomes |
| How does continuous integration and deployment (CI/CD) affect AI system security | Automating updates to reduce human errors while ensuring secure practices |
| How does insider threat impact AI system security | Unauthorized actions by trusted users leading to data breaches or exposure |
| How does multi-factor authentication enhance AI system access control | By adding extra security layers beyond just a password |
| How does the principle of defense in depth apply to AI security | Using multiple layers of security measures to protect against threats |
| How does version control support AI security | Tracking changes in models or data pipelines to quickly identify unauthorized modifications |
| What is AI model integrity assurance | Processes to ensure models behave as expected and are not tampered with |
| What is differential privacy in the context of AI | A method to ensure individual data points in datasets remain unidentifiable |
| What is secure data processing in AI systems | Ensuring data is processed safely without exposure or leakage |
| What is supply chain security in AI systems | Safeguarding the integrity of third-party components or dependencies |
| What is the concept of federated learning in AI security | Training models on decentralized data to reduce the risk of data breaches |
| What is the concept of least privilege in AI systems | Restricting user or system permissions to only what is strictly necessary |
| What is the function of data governance in AI systems | Managing data access and usage policies to ensure compliance and security |
| What is the impact of explainable AI (XAI) on security | Improving transparency to identify and mitigate malicious biases or vulnerabilities |
| What is the importance of response playbooks in AI security | Providing structured procedures to handle security incidents effectively |
| What is the main goal of data anonymization | Protecting sensitive information while enabling data use for AI training |
| What is the role of a sandbox environment in AI security | Isolating new AI components for testing to prevent harm to the main system |
| What is the role of access controls in AI system security | Limiting access to sensitive data and resources to authorized individuals only |
| What is the role of audits in AI security frameworks | Verifying compliance with security policies and identifying potential gaps |
| What is the significance of logging in AI systems | Creating a trail of activities for analyzing security incidents |
| Why is model encryption important in AI systems | Protecting AI models against theft or reverse engineering |
| Why is regular patching important for AI system security | Fixing vulnerabilities to prevent exploitation by attackers |
| Why is system monitoring important in AI systems | Detecting anomalies or potential security threats in real-time |
| Why is threat modeling important for AI systems | Identifying and assessing risks to better design security measures |
This deck emphasizes security designs and frameworks for AI systems, covering topics like secure data processing, system monitoring, and AI model integrity assurance.