AI Cybersecurity Training is a focused two-day program designed to help professionals understand cybersecurity risks, threats, and defense strategies specific to artificial intelligence systems. This course covers how AI systems can be attacked, how traditional cybersecurity principles extend to AI pipelines, and how organizations can secure AI models, data, infrastructure, and integrations to ensure safe and trustworthy AI deployments.

• Understand AI in cybersecurity
• Learn AI-driven threat detection concepts
• Understand automated security workflows
• Gain knowledge of cyber risk analysis
• Learn anomaly detection basics
• Understand AI-powered incident response
• Explore intelligent security monitoring
• Identify AI cybersecurity use cases

• Cybersecurity and application security professionals
• AI and machine learning engineers
• DevSecOps and platform engineering teams
• Cloud and infrastructure security teams
• Technology leaders responsible for AI security

• Basic understanding of cybersecurity or IT security concepts
• Familiarity with artificial intelligence or machine learning fundamentals
• Awareness of cloud, APIs, or application architectures
• Interest in securing modern AI-driven systems

• Understanding cybersecurity threats targeting AI systems
• Identifying vulnerabilities across the AI lifecycle
• Applying security controls to AI models and data pipelines
• Securing AI APIs, integrations, and deployments
• Evaluating risks and defensive strategies for AI systems

By the end of this training, participants will be able to identify cybersecurity risks in AI systems, understand common attack vectors, apply security controls across AI architectures, and support secure, compliant, and resilient AI deployments.

Introduction to AI Cybersecurity
• Overview of AI systems and security challenges
• Difference between traditional cybersecurity and AI security
• AI threat landscape and attacker motivations
• Security responsibilities across the AI lifecycle

AI Architecture and Attack Surfaces
• Data collection, training, and inference pipelines
• Model hosting, APIs, and integrations
• Third-party models and supply chain risks
• Identifying trust boundaries in AI systems

Common Attacks on AI Systems
• Data poisoning and training data manipulation
• Adversarial inputs and evasion attacks
• Model extraction and intellectual property theft
• Abuse and misuse of AI capabilities

Securing AI Systems and Deployments
• Securing data, models, and infrastructure
• Authentication, authorization, and access controls
• Monitoring, logging, and anomaly detection
• Incident response for AI-related security events

Privacy, Compliance, and Governance
• Data privacy and protection in AI systems
• Bias, fairness, and ethical security risks
• Regulatory and compliance considerations
• Governance frameworks for secure AI usage

AI Cybersecurity Best Practices and Future Trends
• Secure-by-design AI development principles
• Red teaming and security testing for AI
• Emerging AI security threats
• Preparing for advanced and autonomous AI systems

• AI cybersecurity fundamentals
• Threat detection techniques
• Anomaly detection concepts
• Security monitoring workflows
• Incident response automation
• Malware and phishing detection basics
• Risk analysis concepts
• AI-driven security operations
• Compliance and security governance
• Practical cybersecurity AI scenarios

• Conceptual understanding assessments
• AI attack and defense scenario analysis
• Security control and governance evaluation
• Final knowledge evaluation quiz

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 Cybersecurity Training, validating their expertise in securing AI systems, managing AI-specific threats, and applying cybersecurity best practices to artificial intelligence deployments.