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skills does CompTIA SecAI+ help learners build for AI security?","Question",{"text":63,"@type":64},"Learners develop practical skills to recognize and respond to AI-driven threats, such as prompt injection, model abuse, data leakage, and adversarial attacks, and to apply secure-by-design principles to AI use cases and workflows.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"What prerequisites are required for the course?",{"text":68,"@type":64},"The prerequisites are equivalent to 3–4 years of IT experience, including approximately 2 years of hands-on cybersecurity experience.",{"name":70,"@type":61,"acceptedAnswer":71},"What major topics are covered in the course outline?",{"text":72,"@type":64},"The outline covers basic AI concepts related to cybersecurity, securing AI systems using threat modeling and security controls, and using AI-enabled tools to facilitate security 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and cybersecurity talent worldwide to meet the emerging challenges and opportunities at the intersection of AI and security.  \nCompTIA 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.  \nWHO WILL BENEFIT FROM THIS COURSE?  \n• Individuals seeking CompTIA SecAI+ certification (CY0-001) exam.  \nPREREQUISITES  \n• This is equivalent to 3–4 years of IT experience with approximately 2 years of hands-on cybersecurity experience.  \nCOURSE OBJECTIVES  \n• Learners will develop practical, job-ready skills for recognizing and responding to AI-driven threats, such as prompt injection, model abuse, data leakage, and adversarial attacks. They will be able to apply secure-by-design principles to AI use cases, policies, and workflows. Upon completion, learners will be prepared to contribute to risk management, governance, and incident response activities involving AI.  \n• SecAI+ also provides a verifiable way to demonstrate your skills to employers, helping you stand out in a competitive market. They signal a commitment to ongoing professional development and can support career advancement, role expansion, or salary growth overtime.  \nCOURSE OUTLINE  \nBasic AI Concepts Related to Cybersecurity  \nCompare and contrast various AI types and techniques used in cybersecurity.  \n• Types of AI  \no Generative AI  \no Machine learning  \no Statistical learning  \no Transformers  \no Deep learning  \no Natural language processing (NLP)  \n▪ Large language models (LLMs)  \n▪ Small language models (SLMs)  \n▪ Generative adversarial networks (GANs)  \n• Model training techniques  \no Model validation  \no Supervised learning  \no Unsupervised learning  \no Reinforcement learning  \no Fine-tuning  \n▪ Epoch  \n▪ Pruning  \n▪ Quantization  \n• Prompt engineering  \no System prompts  \no User prompts  \no One-shot prompting  \no Multi-shot prompting  \no Zero-shot prompting  \no System roles  \no Templates  \nExplain the importance of data security in relation to AI.  \n• Data processing  \no Data cleansing  \no Data verification  \no Data lineage  \no Data integrity  \no Data provenance  \no Data augmentation  \no Data balancing  \n• Data types  \no Structured data  \no Semi-structured data  \no Unstructured data  \n• Watermarking  \n• Retrieval-augmented generation (RAG)  \no Vector storage  \no Embeddings  \n• Business use case  \no Alignment with corporate objectives  \n• Data collection  \no Trustworthiness  \no Authenticity  \n• Data preparation  \n• Model development/selection  \n• Model evaluation  \n• Deployment  \n• Validation  \n• Monitoring and maintenance  \n• Feedback and iteration  \n• Human-centric AI design principles  \no Human-in-the-loop  \no Human oversight  \no Human validation  \nSecuring AI Systems  \nGiven a scenario, use AI threat-modeling resources.  \n• Open Worldwide Application Security Project (OWASP) Top 10  \no LLM Top 10  \no Machine Learning (ML) Security Top 10  \n• Massachusetts Institute of Technology (MIT) AI Risk Repository  \n• MITRE Adversarial Threat Landscape for Artificial-Intelligence Systems (ATLAS)  \n• Common Vulnerabilities and Exposures (CVE) AI Working Group  \n• Threat-modeling frameworks  \nGiven a set of requirements, implement security controls for AI systems.  \n• Model controls  \no Model evaluation  \no Model guardrails  \n▪ Prompt templates  \n• Gateway controls  \no Prompt firewalls  \no Rate limits  \no Token limits  \no Input quotas  \n▪ Data size  \n▪ Quantity  \no Modality limits  \no Endpoint access controls  \no Guardrail testing and validation  \nGiven a scenario, implement appropriate access controls for AI systems.  \n• Model access  \n• Data access  \n• Agent access  \n• N","cbCaivX7EA13NYMH","https://ap.wps.com/l/cbCaivX7EA13NYMH","pdf",181702,"English","# Course Overview\n# Who Will Benefit From This Course?\n# Prerequisites\n# Course Objectives\n# Course Outline\n## Basic AI Concepts Related to Cybersecurity\n## Securing AI Systems\n## AI-assisted Security","[{\"question\":\"What skills does CompTIA SecAI+ help learners build for AI security?\",\"answer\":\"Learners develop practical skills to recognize and respond to AI-driven threats, such as prompt injection, model abuse, data leakage, and adversarial attacks, and to apply secure-by-design principles to AI use cases and workflows.\"},{\"question\":\"What prerequisites are required for the course?\",\"answer\":\"The prerequisites are equivalent to 3–4 years of IT experience, including approximately 2 years of hands-on cybersecurity experience.\"},{\"question\":\"What major topics are covered in the course outline?\",\"answer\":\"The outline covers basic AI concepts related to cybersecurity, securing AI systems using threat modeling and security controls, and using AI-enabled tools to facilitate security tasks.\"}]","CompTIA SecAI+ - Course Overview - AI Security Fundamentals | PDF"]