AI Governance and Security

This course contains the use of free artificial intelligence general practice tests course.

AI Governance and Security is a practical course designed to help professionals understand how to manage the risks, security concerns, privacy obligations, and oversight requirements associated with artificial intelligence. As organizations adopt generative AI, large language models, AI assistants, and autonomous agents, strong governance is essential for protecting data, maintaining compliance, and ensuring responsible use.

The course begins with the foundations of AI governance. You will explore common AI risk categories, including security, privacy, operational, ethical, legal, financial, and reputational risks. You will learn how policies, controls, accountability structures, and oversight committees help organizations manage AI consistently. You will also examine how responsibilities can be shared across business, technology, security, legal, compliance, and risk teams.

The security section focuses on important AI security risks. You will learn how sensitive information may be exposed through prompts, model responses, connected tools, logs, training data, and external integrations. The course explains prompt injection, indirect prompt injection, model abuse, unauthorized tool use, and other threats that can affect generative AI applications and AI agents. You will also learn why permissions, isolation, validation, and human approval are important when AI systems can take actions.

The course then addresses AI compliance and privacy. You will explore regulatory considerations, responsible data handling, consent, access, retention, documentation, and audit readiness. You will learn how organizations can identify sensitive information, limit unnecessary data exposure, and maintain evidence showing how AI systems are designed, reviewed, approved, and monitored.

In the enterprise-controls section, you will examine role-based access control, permissions, logging, monitoring, approvals, exception handling, and incident response. These controls help organizations understand who used an AI system, what data was accessed, what actions were taken, and whether established policies were followed. You will also learn how approval workflows can be designed for higher-risk use cases.

The final section focuses on responsible AI deployment. You will learn how to conduct AI risk assessments, define human review requirements, document decisions, manage exceptions, and build a repeatable governance operating model. The course emphasizes balancing innovation with safety rather than treating governance as a barrier to progress.

By the end of AI Governance and Security, you will understand how to evaluate AI risks, protect sensitive data, improve security, support compliance, and create practical enterprise controls. You will be prepared to contribute to governance programs involving responsible AI, AI risk management, AI security, data privacy, regulatory compliance, and enterprise AI governance.

This course is ideal for executives, managers, architects, developers, security professionals, risk teams, auditors, privacy specialists, legal professionals, consultants, and anyone responsible for deploying AI safely and responsibly.