Risks Associated with AI
This lab directly supports the following CompTIA SecAI+ (CY0-001) exam objectives by providing the foundational knowledge necessary to understand, govern, and mitigate the risks associated with AI systems.
| Lab Concept/Task | CompTIA SecAI+ (CY0-001) Objective |
|---|---|
| Understanding the nature of AI risks (Bias, Model Drift, etc.) | 4.2: Explain risks associated with AI |
| Responsible AI (RAI) Framework and Principles | 4.1: Explain AI governance structures |
| NIST AI RMF (Govern, Map, Measure, Manage) | 4.1: Explain AI governance structures |
| Shadow AI and Data Leakage | 2.4: Given a scenario, implement data security controls for AI systems |
| Compliance and Regulatory Violations (GDPR, HIPAA) | 4.3: Explain the impact of compliance on the business use and development of AI |
| Security and Resilience, Adversarial Attacks | 1.3: Explain the importance of security in the AI life cycle |
| Accountability and Transparency | 2.5: Given a scenario, implement monitoring and auditing for an AI system |
Overview
This theory lab, Risks Associated with AI, is designed to provide a comprehensive understanding of the multifaceted risks inherent in the design, development, deployment, and use of Artificial Intelligence (AI) systems. The learning objective is to explain risks associated with AI, focusing on three critical areas: the principles and practices of responsible AI (RAI), the broad spectrum of AI Risks as defined by leading industry frameworks, and the specific, often hidden, dangers posed by Shadow IT (or Shadow AI). By exploring these topics, learners will gain the knowledge necessary to identify, assess, and mitigate potential harms, ensuring the trustworthy and ethical application of AI technologies in various organizational and societal contexts.
VM Credentials
Username: student
Password: student
Key terms and descriptions
Responsible AI (RAI)
A framework of principles and practices designed to ensure AI systems are developed and deployed in a manner that is fair, transparent, accountable, safe, and beneficial to society
AI Risk Management Framework (AI RMF)
A structured approach, such as the one developed by NIST, for identifying, assessing, and mitigating risks associated with AI systems throughout their life cycle
Shadow AI
The use of AI tools, services, or models by employees within an organization without the knowledge, approval, or oversight of the IT, security, or governance teams
Data Leakage
The unauthorized transmission of sensitive or confidential data from within an organization to an external destination, often a risk when using unapproved AI tools
Bias
Systematic error in an AI system's output due to flawed assumptions in the machine learning process, often stemming from unrepresentative or prejudiced training data
Fairness
A principle of RAI ensuring that AI systems do not perpetuate or amplify unjust or discriminatory outcomes against individuals or groups.
Accountability
The principle that organizations and individuals responsible for the design, development, and deployment of AI systems can be held responsible for their outcomes and impacts
Transparency
The degree to which an AI system's inner workings, data, and decision-making processes are understandable and accessible to relevant stakeholders
Explainability
The ability to articulate how an AI system arrived at a particular output or decision, which is crucial for building trust and enabling effective risk management
Model Drift
The phenomenon where the performance or accuracy of an AI model degrades over time due to changes in the real-world data it processes
Adversarial Attack
Maliciously crafted input data designed to intentionally cause an AI model to make an incorrect classification or decision
Systemic Risk
Risks that can cascade across multiple systems, sectors, or society, often associated with the widespread deployment of a single, flawed AI model.
P-FER
The four core functions of the NIST AI RMF: govern, map, measure, and manage
Governance
The organizational structures, policies, and processes put in place to direct and control the development and use of AI systems
Validation
The process of ensuring that an AI system meets the needs of its users and stakeholders in its intended operational environment
Resilience
The ability of an AI system to maintain its function and integrity despite internal or external disturbances, such as cyberattacks or data corruption
Inadvertent Leakage
The unintentional exposure of sensitive data when employees input proprietary information into public, unmonitored AI services
Data Sovereignty
The concept that data is subject to the laws and governance structures of the nation in which it is collected and stored, a key concern with Shadow AI
Model Card
A short document accompanying a trained machine learning model that provides benchmarked evaluation metrics, intended uses, and ethical considerations
Human-Centric AI
An approach to AI development that prioritizes human values, well-being, and control, ensuring the technology serves human needs