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Introduction to AI Risk & Security
This beginner course provides a comprehensive understanding of AI risk and security tailored to the needs of developers and technical professionals. It covers foundational concepts of AI risk, security, safety, and resilience, techniques for identifying and assessing AI risks, strategies for mitigating those risks, and the importance of AI governance and accountability. The course also explores technical measures for AI security, such as data protection, robustness, and model security, and delves into evaluating AI trustworthiness through principles, transparency, human compatibility, and auditing techniques. By integrating these elements, the course equips learners with the knowledge and tools to develop, deploy, and manage AI systems responsibly and securely.
Provider Information
SecureCodeWarrior
265 Franklin Street Suite 1702
Boston, MA 02110
Course Overview
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Learning Objectives
By the end of this content, learners will be able to: Understand and apply foundational concepts of AI risk, security, safety, and resilience.
- Identify where and how AI is used within their organization and recognize potential AI-specific risks.
- Assess the impact and likelihood of identified AI risks using quantitative and qualitative data.
- Develop and implement effective mitigation strategies to manage AI risks.
- Apply technical, procedural, and policy controls to secure AI systems.
- Continuously monitor and review AI risks to ensure the effectiveness of implemented controls.
- Prepare and manage responses to AI-related incidents through established incident response plans.
- Utilize governance frameworks to enhance AI resilience and manage AI risks.
- Define roles and responsibilities in AI risk management to ensure clear accountability.
- Develop and implement ethical and responsible AI policies to ensure transparency, accountability, and fairness.
- Employ techniques to enhance the interpretability and explainability of AI decision-making processes.
- Conduct comprehensive AI audits to verify compliance, performance, security, and ethical standards.
- Evaluate AI trustworthiness using established frameworks and tools, ensuring alignment with human values and preferences.
Framework Connections
The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):
Competency Areas
Feedback
If you would like to provide feedback on this course, please e-mail the NICCS team at NICCS@mail.cisa.dhs.gov. Please keep in mind that NICCS does not own this course or accept payment for course entry. If you have questions related to the details of this course, such as cost, prerequisites, how to register, etc., please contact the course training provider directly. You can find course training provider contact information by following the link that says “Visit course page for more information...” on this page.