AI Security & Governance (2026)

Name: AI Security & Governance (2026)
Rating: 4.7 (75 reviews)

Mastering Risk, Compliance, and Trust in the Age of Autonomous AI

Bestseller

Role Play

Created bySerge Movsesyan | CISSP / CCSP

Last updated 7/2026

English

What you'll learn

Understand foundational principles of AI security, governance, and risk management for enterprise environments.
Learn to assess AI risks and perform structured impact and privacy assessments for AI systems.
Gain practical knowledge on AI policy, acceptable use, and workforce training, including executives, technical teams, and employees.
Apply AI monitoring, detection, and incident response techniques to manage autonomous and agentic AI systems.
Develop skills to evaluate AI vendors, supply chain risk, and third-party integrations to ensure secure adoption.
Learn how to prepare for audits, maintain evidence, and demonstrate compliance with frameworks, regulations, and internal policies.
Understand secure AI architecture, control design, and deployment strategies, including human oversight and risk-based approvals.
Explore emerging AI trends, including agentic AI, autonomous systems, AI-to-AI interactions, and associated security challenges.
Learn to balance innovation with governance, including building an AI Center of Excellence, approved tooling strategy, and sustainable adoption roadmap.
Build the skills to lead trustworthy AI programs with accountability, ethics, and continuous improvement.

Course content

1 section • 17 lectures • 8h 18m total length

Legal Disclaimer0:25
The 2026 AI Security & Governance Landscape20:30
AI Fundamentals for Security and Governance Professionals20:57
AI Threat Modeling and Risk Classification29:40
AI Governance Frameworks and Operating Models32:15
AI Inventory, Discovery, and Shadow AI Management33:53
Data Security, Privacy, and Confidentiality in AI Systems35:33
Explore data security, privacy, and confidentiality in AI systems and discuss governance considerations for AI security.
LLM and Generative AI Application Security34:49
Explore llm and generative ai application security within the ai security and governance framework, examining risk, safeguards, and best practices for deploying secure ai systems.
Secure AI Architecture and Control Design30:47
Third-Party AI, Vendor Risk, and Supply Chain Governance30:54
Learn how third-party ai vendors and supply chain governance shape risk, data protection, and compliance through due diligence, contracts, monitoring, and exit strategies.
AI Risk Assessments, Impact Assessments, and Control Testing32:04
Learn how to perform AI risk assessments, impact assessments, and control testing to ensure safe, ethical, and compliant AI deployments. Understand inputs, governance, and risk mitigation.
AI Monitoring, Detection, and Incident Response37:22
AI Policy, Acceptable Use, and Workforce Training32:11
AI Compliance, Audit, and Evidence Management36:38
Secure and Responsible AI Adoption Strategy35:44
Future Trends: Agentic AI, Autonomous Systems, and Emerging Risk44:27
Explore future trends in agentic ai and autonomous systems, and assess emerging risk for ai security and governance.
50 Critical AI Security Controls10:25
AI Vendor Risk Assessment & Contract Negotiation
Autonomous AI Incident Response Simulation
Ethical & Compliance Decision-Making for High-Risk AI
AI Security & Governance - Final Test

Requirements

Basic cybersecurity knowledge, including access control, authentication, and risk management.
General understanding of IT systems, networks, cloud services, and software applications.
Familiarity with AI concepts, such as machine learning and large language models (helpful but not required).
Analytical thinking skills for evaluating processes, policies, and risks.
Access to a computer and internet to view course lectures and materials.
Interest in governance, compliance, and emerging AI trends to maximize learning.

Description

Artificial Intelligence is rapidly reshaping business operations, but deploying AI without proper oversight can expose organizations to significant security, privacy, ethical, and regulatory risks. AI Security & Governance provides professionals with the skills and frameworks needed to safely manage AI systems in enterprise environments. This course covers the full lifecycle of AI adoption, from risk assessment and secure architecture to monitoring, policy enforcement, and incident response. Learners will explore both foundational and advanced topics, including AI governance frameworks, human oversight, secure data handling, vendor management, and audit-ready evidence practices, preparing them to design robust, compliant, and trustworthy AI programs.

This course emphasizes a practical, real-world approach to AI governance. You will learn how to assess AI risks, classify and inventory AI systems, implement risk-based approvals, and design secure architectures that balance automation with human control. Topics include securing AI APIs and integrations, establishing human approval gates for high-risk actions, monitoring autonomous AI systems, managing third-party vendors and supply chain risks, and addressing emerging threats such as agentic AI, autonomous workflows, and deepfake or synthetic identity risks. Each module provides detailed examples, step-by-step guidance, and actionable strategies to help you apply governance principles effectively within your organization.

By the end of the course, learners will be able to develop and lead comprehensive AI governance programs that are auditable, compliant, and resilient to evolving risks. You will understand how to implement continuous monitoring, conduct impact and risk assessments, enforce policy across employees and AI systems, respond to incidents, prevent bias, and maintain accountability in high-stakes or autonomous AI deployments. Whether your role is technical, managerial, or executive, this course equips you with the knowledge and confidence to safely scale AI adoption while protecting your organization, its data, and the trust of stakeholders.

Who this course is for:

IT and cybersecurity professionals seeking to understand AI risk and governance.
Security architects, solution architects, and analysts responsible for AI system oversight.
Compliance officers and risk managers evaluating AI for regulatory requirements.
Data privacy and legal professionals working with AI-powered systems.
Executives and business leaders overseeing AI adoption in their organizations.
AI project managers responsible for deploying, monitoring, and governing AI solutions.
Developers and technical teams integrating AI tools while maintaining security and compliance.
AI and machine learning enthusiasts interested in enterprise governance practices.
Professionals preparing for AI-related audits, certifications, or regulatory reviews.
Organizations looking to implement responsible, ethical, and auditable AI programs at scale.