CompTIA SecAI+ Certification Training

Overview CompTIA SecAI+ enables a safer digital future by empowering IT and cybersecurity talent worldwide to meet the emerging challenges and opportunities at the intersection of AI and security. _ CompTIA SecAI+ is the global IT industry's first comprehensive "expansion" certification focused on the security of artificial intelligence systems and the secure application of AI in cybersecurity operations. This certification equips professionals with critical, vendor-neutral skills to understand, defend, and ethically deploy AI technologies within any organization. Course Objectives Apply foundational and advanced AI concepts to strengthen organizational cybersecurity. Implement robust security controls and best practices for protecting AI systems and data. Leverage AI-driven tools to enhance threat detection, response, and automation of security operations. Navigate global governance, risk, and compliance frameworks to ensure responsible AI adoption. Who Should Attend? Ideal for those who currently hold a CompTIA cybersecurity certification (such as Security+, CySA+, PenTest+, etc.) or equivalent experience, and are looking to expand their skill set for evolving job roles in the context of AI technologies Course Prerequisites This is equivalent to 3-4 years of IT experience with approximately 2 years of hands-on cybersecurity experience. Course Outline AI and Data Concepts for Cybersecurity AI concepts and core AI types Generative AI and transformers Machine learning and deep learning Natural language processing AI model training approaches Prompt engineering fundamentals Model security considerations AI data types and data security techniques RAG (Retrieval Augmented Generation) concepts Data integrity and processing controls Threat Modeling and Securing AI Systems AI threat modeling fundamentals Threat modeling processes and prerequisites AI threat modeling frameworks AI security control types Model guardrails and prompt templates Gateway and interface controls Usage quotas and limitation controls Security control testing Access Controls for AI AI access control principles and models Model and agent access controls API and network access security AI data security controls Encryption and data safety measures Monitoring and logging AI systems Performance and cost monitoring AI auditing and compliance monitoring AI Threats and Compensating Controls AI lifecycle security Ethical AI design considerations AI attack types and techniques Backdoor and trojan model attacks Model poisoning and inversion Model theft risks Compensating control strategies Post-incident AI analysis Leveraging AI in Security Operations AI-enabled security tools AI use cases in detection and analysis AI for vulnerability assessment AI-enhanced attack vectors AI for social engineering and deception AI reconnaissance techniques AI-driven automation AI in DevSecOps workflows AI scripting and summarization AI Governance, Risk, and Compliance AI governance structures AI organizational roles Responsible AI principles AI risk identification and assessment AI regulatory themes Compliance frameworks for AI Organizational AI policy design Compliance reporting