This module provided an overview of artificial intelligence (AI) and generative AI. It explored the diverse categories of generative AI technologies and its significance. The module also delved into the prevalent sources of data collection utilized by developers of generative AI and the associated risks.
This module began by first providing an overview of AI governance, and its purposes and key drivers. Subsequently, it delved 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 concluded with a coverage of Securiti’s 5-Step approach to AI Governance, which comprised model discovery and AI usage by an organization, assessing applicable risks, mapping data and AI flows, implementing controls and ensuring legal compliance.
This module offered 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 elaborated on cataloging of AI models for transparency purposes, and the trends in AI model discovery.
This module presented a comprehensive overview of AI risks and AI risk management frameworks. It delved into framing of AI risks, and elaborates on AI risk management through the lifecycle of AI systems. The module provided the key components of AI risk management frameworks. Further, it described AI impact assessments, AI risk assessments, and AI vendor assessments for scenarios where businesses plan to license or utilize AI applications from third parties.
This module covered the relationship between AI models and enterprise data, the flow of data within an organization as it interacts with AI systems, mapping data processes tied to AI.
This module emphasized the importance of data security for AI models and systems. It provided 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 delved into the role of LLM firewalls as crucial security measures and their types.
This module presented an overview of the laws, regulations, and industry standards formulated worldwide to regulate the use of AI. It delved into the intersection of data privacy obligations and AI governance. The module listed some of the key compliance obligations for privacy, security, and governance teams, and provided key steps for AI compliance management. Further, it touched upon the principles of responsible AI such as ensuring user transparency, explainability, human oversight, elimination of biases, and ensuring ethical use of AI.
This module concluded the course by providing an overview of AI governance program management, and highlighted 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 listed the key building blocks of an AI governance program. Additionally, the module explored how an effective AI governance program can translate into business success.
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