Overview: Introduction to AI Governance Certification
In recent times, the world has witnessed a significant increase in the use of artificial intelligence, permeating every aspect of the digital landscape. From automated processing to advanced algorithms, artificial intelligence has now become an integral part of our daily lives and business operations. The use of artificial intelligence technologies in various industries and sectors is increasing at an unprecedented scale and exponential rate with the passage of time. This has also resulted in causing profound impacts on society, as well as dangers and risks to the core rights of individuals.
AI Governance refers to the imposition of frameworks, rules, standards, legal requirements, and best practices that govern, manage, and monitor the use of AI systems and protect an individual’s core rights, including data privacy rights, in relation to the use of AI.
Securiti’s AI Governance Certification delves into the dynamic landscape of artificial intelligence, with a particular focus on the capabilities and data prerequisites of generative AI. It underscores the necessity for a robust AI risk management framework and AI Governance. Throughout the certification, you will gain insights into the global regulatory trends in AI and discover the methodical approach to AI governance.
- Lesson 1: Introduction to AI and Generative AI
This module provides an overview of artificial intelligence (AI) and generative AI. It explores the diverse categories of generative AI technologies and their significance. The module also delves into the prevalent sources of data collection utilized by developers of generative AI and the associated risks.
- Lesson 2: Introduction to AI Governance
This module begins by first providing an overview of AI governance, its purposes, and key drivers. Subsequently, it delves into Gartner’s AI TRiSM, which is a structured approach towards risk mitigation and legal compliance in relation to the use of AI. The module concludes with a coverage of Securiti’s 5-Step approach to AI Governance, which comprises model discovery and AI usage by an organization, assessing applicable risks, mapping data and AI flows, implementing controls, and ensuring legal compliance.
- Lesson 3: AI Model Discovery
This module offers an overview of the necessity and process of discovering sanctioned and unsanctioned AI models across public clouds, private clouds, and SaaS applications within an organization. The module further elaborates on the cataloging of AI models for transparency purposes and the trends in AI model discovery.
- Lesson 4: AI Risk Assessment
This module presents a comprehensive overview of AI risks and AI risk management frameworks. It delves into the framing of AI risks and elaborates on AI risk management through the lifecycle of AI systems. The module provides the key components of AI risk management frameworks. Further, it describes AI impact assessments, AI risk assessments, and AI vendor assessments for scenarios where businesses plan to license or utilize AI applications from third parties.
- Lesson 5: Understanding Data and AI Relationships
This module covers the relationship between AI models and enterprise data, the flow of data within an organization as it interacts with AI systems, and mapping data processes tied to AI.
- Lesson 6: Controlling Data Inputs and Outputs
This module emphasizes the importance of data security for AI models and systems. It provides an overview of technical measures that can mitigate security risks present in AI systems, including those aimed at averting unauthorized data access, accidental data loss, data destruction, or data damage. It also delves into the role of LLM firewalls as crucial security measures and their types.
- Lesson 7: AI Regulatory Compliance
This module presents an overview of the laws, regulations, and industry standards formulated worldwide to regulate the use of AI. It delves into the intersection of data privacy obligations and AI governance. The module lists some of the key compliance obligations for privacy, security, and governance teams and provides key steps for AI compliance management. Further, it touches upon the principles of responsible AI such as ensuring user transparency, explainability, human oversight, elimination of biases, and ensuring the ethical use of AI.
- Lesson 8: AI Governance Program & Management
This module concludes the course by providing an overview of AI governance program management and highlights the importance of embracing a structured approach which can aid organizations in fulfilling legal compliance obligations, meeting business objectives, and safeguarding individuals from potential risks associated with AI usage. The module also lists the key building blocks of an AI governance program. Additionally, the module explores how an effective AI governance program can translate into business success.
