AI Security & Risk Fundamentals Training is a comprehensive three-day program designed to help professionals understand the security risks, threats, and governance challenges associated with artificial intelligence systems. This course focuses on identifying AI-specific risks, securing AI pipelines, managing operational and ethical risks, and implementing effective AI risk management frameworks, enabling organizations to adopt AI responsibly, securely, and at scale.

• Understand AI security fundamentals
• Learn AI risk management concepts
• Understand AI system vulnerabilities
• Gain knowledge of threat detection basics
• Learn secure AI deployment practices
• Understand data privacy and compliance concepts
• Explore AI governance frameworks
• Identify enterprise AI security risks

• Cybersecurity and risk management professionals
• AI and machine learning practitioners
• DevSecOps and platform engineering teams
• Compliance, governance, and audit professionals
• Technology leaders overseeing AI initiatives

• Basic understanding of artificial intelligence or machine learning concepts
• Familiarity with IT systems, applications, or cloud environments
• General awareness of cybersecurity or risk management concepts
• Interest in responsible and secure AI adoption

• Understanding AI-specific security threats and risks
• Identifying vulnerabilities across the AI lifecycle
• Applying AI risk management and governance frameworks
• Implementing security controls for AI systems
• Evaluating ethical, legal, and compliance considerations

By the end of this training, participants will be able to identify and assess AI security risks, apply structured risk management frameworks, implement governance and security controls across the AI lifecycle, and support responsible, compliant, and secure AI adoption within their organizations.

Foundations of AI Security
• Overview of AI systems and attack surfaces
• Difference between traditional IT security and AI security
• AI threat landscape and risk categories
• Security responsibilities across the AI lifecycle

AI Architecture and Risk Exposure
• Data pipelines, models, and deployment environments
• Training, inference, and integration risks
• APIs, third-party models, and tool dependencies
• Identifying trust boundaries in AI systems

Common AI Security Threats
• Data poisoning and model manipulation
• Adversarial inputs and evasion attacks
• Model theft and intellectual property risks
• Abuse and misuse of AI capabilities

AI Risk Management and Governance
• AI risk assessment and threat modeling
• Governance structures and accountability
• Policy development for secure AI usage
• Aligning AI security with enterprise risk management

Privacy, Ethics, and Compliance
• Data privacy and protection in AI systems
• Bias, fairness, and ethical risks
• Regulatory and compliance considerations
• Responsible AI principles and practices

Operational Security for AI Systems
• Securing AI infrastructure and deployments
• Monitoring, logging, and anomaly detection
• Incident response for AI-related security events
• Managing third-party and supply chain risks

Mitigation Strategies and Secure AI Design
• Secure-by-design AI architectures
• Risk mitigation controls and safeguards
• Validation, testing, and red-teaming AI systems
• Continuous risk monitoring and improvement

AI Security in Practice
• Case studies of AI security failures and lessons learned
• Applying risk management to real-world AI use cases
• Cross-functional collaboration between AI and security teams
• Building organizational AI security maturity

Future Trends and Strategic Readiness
• Emerging AI threats and evolving risk landscape
• Preparing for advanced AI and autonomous systems
• Integrating AI security into long-term strategy
• Continuous learning and future readiness

• AI security concepts
• AI risk management fundamentals
• AI system vulnerabilities
• Threat detection techniques
• Secure AI deployment basics
• Data privacy and protection concepts
• AI governance and compliance
• Access control and authentication basics
• Enterprise AI security use cases
• Practical AI security scenarios

• AI security concepts
• AI risk management fundamentals
• AI system vulnerabilities
• Threat detection techniques
• Secure AI deployment basics
• Data privacy and protection concepts
• AI governance and compliance
• Access control and authentication basics
• Enterprise AI security use cases
• Practical AI security scenarios

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Participants who successfully complete the training will receive an AcadNXT Certification in AI Security & Risk Fundamentals Training, validating their expertise in understanding AI security threats, risk management practices, governance frameworks, and real-world AI security implementation.