[100% Off] Ai Governance And Security

Build secure, compliant, and responsible AI systems with practical governance, risk, privacy, and enterprise controls.

Added on October 10, 2026 Business 3 min read

What you’ll learn

• Explain the purpose of AI governance and why organizations need formal oversight for AI systems.,Identify major AI risk categories
• including operational
• ethical
• legal
• security
• privacy
• and reputational risks.,Develop practical AI policies
• standards
• controls
• and accountability structures.,Understand how governance committees
• review boards
• risk owners
• and business leaders support AI oversight.,Recognize data-exposure risks involving sensitive
• confidential
• regulated
• and proprietary information.,Explain prompt injection
• indirect prompt injection
• model manipulation
• and common AI abuse scenarios.,Identify security risks associated with AI agents
• connected tools
• external data sources
• and automated actions.,Understand regulatory
• privacy
• and compliance considerations that may affect enterprise AI deployments.,Apply safe practices for handling personal
• financial
• customer
• employee
• and confidential business data.,Prepare AI systems and documentation for audits
• assessments
• and regulatory reviews.,Design access controls
• permissions
• role-based security
• and approval workflows for AI applications.,Use logging
• monitoring
• traceability
• and incident reporting to improve AI accountability.,Conduct structured AI risk assessments before deploying new models
• tools
• or use cases.,Define human review
• escalation
• exception handling
• and approval processes.,Build a practical AI governance operating model that aligns business
• technology
• security
• legal
• compliance
• and risk teams.,Support responsible AI adoption while balancing innovation
• security
• compliance
• and business value.

Requirements

• No previous AI governance
• cybersecurity
• legal
• or compliance experience is required.,The course is designed for both technical and non-technical professionals.,Basic familiarity with artificial intelligence or generative AI tools is helpful but not mandatory.,No programming
• advanced mathematics
• or machine learning knowledge is required.,A computer
• tablet
• or smartphone with an internet connection is recommended.,Experience with business processes
• technology projects
• security
• risk
• or compliance can be helpful.,Learners should be willing to evaluate AI risks from business
• technical
• ethical
• and regulatory perspectives.,Access to organizational policies or sample AI use cases may help with practical exercises but is not required.,Curiosity about responsible AI
• privacy
• security
• and enterprise controls is the most important prerequisite.

Description

This course contains the use of artificial intelligence.

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.