CompTIA SecAI+ (CY0-001) Complete Course from CertMike!

By the end of this video, you will understand why AI security is emerging as a strong career path, including demand, compensation trends, and why the work is especially meaningful. You will also learn common role entry points and how to leverage your existing background, whether you are coming from cybersecurity, data science, IT, or another business domain.

In this video, the focus shifts to why certifications still matter in a rapidly changing technology landscape. You will learn how the SecAI+ certification can validate your skills, strengthen your professional credibility, and improve visibility with employers and recruiters.

This lesson explains how to make the most of the resources available to you while preparing for the exam. You will learn how to combine this course, the companion study guide, and CertMike.com to create an effective, realistic study plan that fits your schedule.

In this video, you will explore the major categories of artificial intelligence as they are used in practice. You will learn how these types differ in capability and scope, and why those differences matter when evaluating risk and security impact.

This video explains how machine learning works at a conceptual level without requiring a data science background. You will learn the core ideas behind training models, making predictions, and why machine learning systems behave differently from traditional software.

Here, the focus shifts to deep learning and why it has driven recent advances in AI. You will learn how deep learning builds on machine learning, what neural networks are doing at a high level, and why these systems introduce unique security considerations.

This lesson introduces natural language processing and how AI systems work with human language. You will learn common NLP tasks, where they are used in real systems, and why language-based AI presents distinct risks and challenges.

Rather than treating all language models as the same, this video compares large language models and small language models directly. You will learn how they differ in capability, cost, deployment, and security tradeoffs, and when each approach makes sense.

This video brings together earlier concepts to explain how generative AI systems create new content. You will learn what makes generative AI different from other AI approaches and why it raises specific concerns around misuse, reliability, and security.

In this video, you will learn how supervised learning works and why labeled data is central to this approach. You will also understand common supervised learning tasks and how this training method affects model behavior and security considerations.

This video introduces unsupervised learning and how it differs fundamentally from supervised approaches. You will learn how models identify patterns without labeled data and why this technique is useful for discovery, clustering, and anomaly detection.

Here, the focus turns to reinforcement learning and decision-making over time. You will learn how agents learn through rewards and penalties, and why reinforcement learning systems raise unique risks related to control, feedback, and unintended behavior.

This video introduces federated learning and how it differs fundamentally from traditional centralized training. You will learn how models are trained across distributed data sources without sharing raw data and why this approach is valuable for privacy preservation, regulatory compliance, and collaborative learning.

This lesson explains how AI models are evaluated before deployment. You will learn common validation techniques, why overfitting and bias matter, and how poor validation can lead to security and reliability issues.

Rather than training from scratch, this video explores how existing models are adapted for specific tasks. You will learn what fine-tuning involves, when it is appropriate, and how it can introduce both benefits and new risks into an AI system.

In this video, you will learn what prompts are and why prompt engineering is the key to getting accurate, useful output from generative AI systems. You will also see how adding specificity and context improves results for security tasks like summarizing threat reports, analyzing logs, and generating YARA rules or regular expressions.

Take a closer look at how modern AI chat systems separate instructions into system, user, and assistant roles. You will learn what each role controls, and how role structure helps keep responses consistent, safe, and aligned with the intended task.

This video breaks down zero-shot prompting and why it is often the default approach when you ask an AI to perform a task without providing examples. You will learn how clarity and constraints, especially specifying the format and focus, can significantly improve zero-shot results.

In this session, you will explore one-shot prompting and how a single example can guide the model’s structure, tone, and level of detail. You will learn why one well-chosen example often produces more reliable output than instructions alone.

We will start with the idea behind multi-shot prompting, where several examples teach the model the pattern you want it to follow. You will learn when few-shot prompting is especially effective for tasks like log analysis, alert triage, and policy generation, and how to distinguish it from zero-shot and one-shot approaches for the exam.

You will explore the main data types used in AI systems, including structured, unstructured, and semi-structured data. You will also learn the security implications of each type, such as access controls for structured stores and the risks of hidden metadata in files like images and documents.

In this video, you will learn why data cleansing is a foundational step for building secure and reliable AI models. You will see how removing errors, duplicates, and inconsistent formats also reduces the chance that poisoned, misleading, or malicious content makes it into the training pipeline.

This lesson shows how data verification confirms that cleaned data is genuine, accurate, and trustworthy. You will learn common verification approaches, including comparison to trusted references and cryptographic checks like hashes, so tampered data can be detected before it influences a model.

Get ready to dig into data integrity and the controls that prevent unauthorized changes over time. You will learn how techniques like signatures, checksums, access control, and audit logging protect model outcomes and support defensibility during reviews and audits.

We will start by separating two concepts that are easy to mix up: data lineage and data provenance. You will learn how lineage tracks data’s journey through systems while provenance establishes origin and authenticity, and why both are essential for transparency, reproducibility, and compliance.

Next, you will learn how data augmentation expands a dataset by creating realistic variations from existing examples. You will see common approaches for images and text, and why augmentation helps models generalize better instead of memorizing the training set.

This video walks through data balancing and why class imbalance can quietly damage model performance and fairness. You will learn techniques like over-sampling, under-sampling, and SMOTE, and how balancing reduces blind spots that attackers can exploit.

In this video, you will learn what Retrieval-Augmented Generation is and how it combines a generative model with external knowledge retrieval to produce more accurate, current responses. You will also see the basic RAG flow from user query to retrieval to adding context before the model generates an answer, including a practical chatbot example.

This lesson focuses on why the knowledge store is the foundation of a RAG system and why protecting it protects the whole pipeline. You will learn the key security controls for a vector or document store, including access control, integrity validation, and guarding against malicious or untrusted content entering the knowledge base.

You will learn how the trustworthiness of a RAG response depends on the integrity of the content retrieved at runtime. The video covers how retrieval integrity failures happen and the safeguards used to prevent them, including verifying sources and using mechanisms like hashes to detect tampering.

In this session, you will examine the privacy risks that come with retrieving potentially sensitive information to answer a user’s prompt. You will learn practical protections such as isolation between interactions, careful access control and encryption, and monitoring retrieval activity to spot misuse or leakage.

Start by building a clear mental model of the AI lifecycle and why it is best understood as an ongoing loop, not a one-time project plan. You will learn the major phases from planning through deployment and upkeep, plus the kinds of failures that appear when data handling, validation, or monitoring are weak.

This video focuses on aligning an AI effort to real business outcomes before anyone starts building models. You will learn how teams define success criteria, surface security and compliance needs early, and use risk assessment to match controls to the stakes of the use case.

We will cover what makes training data trustworthy, secure, and fit for purpose before it ever reaches a model. You will learn how to evaluate source risk, protect data in transit and at rest, and apply principles like legal compliance, data minimization, and dataset diversity.

Next, you will learn how raw data is transformed into a clean, validated dataset that is safe to use for modeling. The lesson includes secure preprocessing practices such as profiling and cleansing, sanitizing sensitive data, splitting datasets, augmentation, and maintaining versioning and lineage.

This session explains what happens when teams choose an algorithm or architecture and turn prepared data into a trained model. You will learn why security in the training environment matters, how to weigh factors beyond accuracy, and what to verify when using pre-trained models or external sources.

You will see how evaluation and validation act as the final gate before deployment and why testing on unseen data is essential. The video also covers measuring more than performance, including fairness and robustness, and why independent review and red teaming help expose weaknesses early.

This lesson takes you into the moment a model enters production and becomes exposed to real users, systems, and inputs. You will learn secure deployment practices such as API hardening, access control, protecting the model as an asset, validating inputs and outputs, and securing telemetry and logs.

To wrap up, you will learn how teams keep deployed AI systems reliable and secure over time as conditions change. The video covers drift detection, spotting suspicious behavior, patching and dependency updates, and using feedback loops and retraining to maintain integrity and performance.

In this video, you will learn what human-centric AI design means and why it matters for building secure AI-enabled systems. You will see how focusing on human decision-making, transparency, and control increases trust while supporting ethical and defensible outcomes.

This lesson introduces human-in-the-loop designs, where people actively review or approve AI decisions as part of normal operation. You will learn when this checkpoint is most valuable, especially in high-risk scenarios, and how it helps catch errors, bias, and security-relevant mistakes before harm occurs.

You will explore human oversight as an approach where AI runs more independently, but humans continuously monitor and intervene when necessary. You will learn what makes oversight effective, including monitoring signals, escalation paths, and clear logs that explain what the system did and why.

We will wrap up by focusing on human validation, which reviews AI outputs over time to ensure the system stays accurate, fair, and safe. You will learn how validation detects issues like bias and drift, including in generative AI contexts, and how regular review keeps outcomes aligned with organizational goals.