The LICQual Level 2 Award in AI Risk Management and Incident Response is the perfect qualification for professionals looking to enhance their understanding of managing the risks and responding to incidents associated with Artificial Intelligence (AI) technologies. As AI continues to transform industries, the potential risks—ranging from security breaches to ethical dilemmas—become more complex and critical to address.
This course provides in-depth insights into identifying, evaluating, and mitigating AI risks while offering practical strategies for responding to incidents. If you’re working in a tech, cybersecurity, or risk management role, this award will help you take your expertise in AI risk management to the next level.
The LICQual Level 2 Award in AI Risk Management and Incident Response delves deeper into the practical and theoretical aspects of managing AI-related risks. You will learn to identify the specific challenges AI technologies bring to organizations, including vulnerabilities in machine learning algorithms, data privacy concerns, and the ethical implications of AI deployment. The course offers valuable knowledge on building comprehensive AI risk management frameworks, ensuring that AI systems are secure, ethical, and compliant with relevant regulations.
Additionally, the course covers the key components of an incident response strategy tailored to AI-related issues, such as cyber-attacks on AI systems, system failures, or misuse of AI. You will gain hands-on knowledge in responding to incidents effectively, minimizing damage, and recovering systems swiftly.
By completing this award, you’ll be equipped with practical skills to assess AI risks, implement effective risk management frameworks, and handle AI incidents with confidence. This qualification is ideal for professionals in AI, cybersecurity, and IT management roles who want to advance their understanding of AI risk and enhance their incident response capabilities in the fast-evolving field of artificial intelligence.
Course Overview
Qualification Title
LICQual Level 2 Award in AI Risk Management and Incident Response
Total Units
6
Total Credits
6
GLH
12
Qualification #
LICQ2200305
Qualification Specification
To enrol in the LICQual Level 2 Award in AI Risk Management and Incident Response, candidates must meet the following entry requirements:
- Educational Requirements: Applicants should have a minimum of a high school diploma or equivalent. A basic understanding of technology or AI concepts is recommended but not mandatory. However, individuals with prior knowledge or experience in IT, cybersecurity, or related fields will find this course particularly beneficial as it builds on foundational knowledge.
- Experience: While prior professional experience in AI or risk management is not required, it is recommended that participants have some exposure to technology-related roles or business environments where AI systems may be used. Individuals who have completed introductory courses in AI, risk management, or incident response will be better prepared to grasp more advanced concepts presented in this course.
- English Language Proficiency: As the course is delivered in English, participants should have a basic level of English language proficiency, sufficient to engage with the course content, understand instructional materials, and participate in discussions. Applicants must be able to read, write, and communicate effectively in English to ensure they can complete the assessments and interact with instructors and peers.
- Age Requirement: Candidates must be at least 18 years of age at the time of enrolment.
|
Qualification# |
Unit Title |
Credits |
GLH |
|---|---|---|---|
|
LICQ2200305-1 |
Advanced AI Risk Identification |
1 |
2 |
|
LICQ2200305-2 |
AI Incident Response Strategies |
1 |
2 |
|
LICQ2200305-3 |
Legal and Ethical Implications of AI Risk |
1 |
2 |
|
LICQ2200305-4 |
AI Risk Mitigation Techniques |
1 |
2 |
|
LICQ2200305-5 |
Creating Incident Response Protocols for AI |
1 |
2 |
|
LICQ2200305-6 |
AI Risk Communication & Reporting |
1 |
2 |
By the end of this course, learners will be able to:
Advanced AI Risk Identification:
- Analyze and identify complex risks in AI systems, including technical, operational, and ethical risks.
- Evaluate the potential impact of emerging AI risks on organizational systems and operations.
- Apply advanced risk identification techniques to assess AI technologies in diverse sectors.
AI Incident Response Strategies:
- Develop and implement effective incident response strategies tailored to AI systems.
- Evaluate the role of AI incident response in minimizing operational disruptions and reputational damage.
- Apply industry best practices to respond to AI incidents efficiently and mitigate damage.
Legal and Ethical Implications of AI Risk:
- Understand and assess the legal and ethical implications of managing AI risks in compliance with relevant laws and regulations.
- Identify the ethical challenges and responsibilities associated with AI risk management.
- Evaluate the importance of legal frameworks and ethical considerations in the risk management process for AI systems.
AI Risk Mitigation Techniques:
- Identify and implement advanced risk mitigation techniques to reduce the likelihood and impact of AI-related risks.
- Apply best practices in risk mitigation across various AI applications to enhance security, privacy, and functionality.
- Assess the effectiveness of mitigation strategies and adapt them to the evolving AI risk landscape.
Creating Incident Response Protocols for AI:
- Develop comprehensive incident response protocols specific to AI systems and technologies.
- Tailor incident response protocols to meet the unique needs and challenges of different AI environments.
- Ensure that AI incident response protocols align with organizational risk management policies and industry standards.
AI Risk Communication & Reporting:
- Communicate AI risks and incident responses effectively to stakeholders, both technical and non-technical.
- Develop clear and concise risk reports that outline risk assessments, mitigation strategies, and incident response outcomes.
- Understand the importance of transparent communication in AI risk management and how it contributes to organizational resilience.
This diploma is ideal for:
- Professionals working in cybersecurity, risk management, or IT who want to deepen their knowledge of AI-specific risks and incident response strategies.
- AI developers, engineers, and data scientists who need to understand how to identify and mitigate risks associated with AI technologies.
- Business leaders, managers, and decision-makers overseeing AI projects who want to ensure the security and compliance of AI systems.
- Individuals involved in compliance, governance, or legal roles within organizations that develop or use AI systems.
- Professionals looking to enhance their skills in managing AI incidents and responding to potential threats effectively.
- Consultants and advisors in technology, risk management, or security fields who want to expand their expertise in AI risk management.
- Individuals with a basic understanding of AI and risk management looking to advance their career and gain a deeper, practical understanding of AI incident response.
- Those working in sectors such as healthcare, finance, manufacturing, or any industry implementing AI technologies and requiring specialized risk management knowledge.
Assessment and Verification
All units within this qualification are subject to internal assessment by the approved centre and external verification by LICQual. The qualification follows a criterion-referenced assessment approach, ensuring that learners meet all specified learning outcomes.
To achieve a ‘Pass’ in any unit, learners must provide valid, sufficient, and authentic evidence demonstrating their attainment of all learning outcomes and compliance with the prescribed assessment criteria. The Assessor is responsible for evaluating the evidence and determining whether the learner has successfully met the required standards.
Assessors must maintain a clear and comprehensive audit trail, documenting the basis for their assessment decisions to ensure transparency, consistency, and compliance with quality assurance requirements.
