CompTIA SecAI+ CY0-001: Full Certification Prep Bootcamp

Explore ai fundamentals for cybersecurity, covering five ai types, generative ai, deep learning, machine learning, and transformers, plus machine learning powered threat detection and ai's benefits and risks in security.

Explore the core ai types in cybersecurity, including generative ai, machine learning, deep learning, transformers, and nlp, and examine how data, autonomy, and governance shape risks and defenses.

Learn how machine learning and statistical learning enable predictive threat modeling, automation, and enhanced detection in cybersecurity, with supervised and unsupervised approaches and core algorithms.

Deep learning uses multi-layered neural networks to learn from raw, unstructured data, enabling CNNs, RNNs, and autoencoders for malware classification, log analysis, and anomaly detection beyond rule-based IDS.

Learn how supervised learning trains models on labeled security data to distinguish benign from malicious activity, using gradient boosted trees and CNNs, with explainability, weak supervision, and evaluation metrics.

Explore reinforcement learning in cybersecurity, detailing the state-action-reward loop, isolated training, and human feedback across adaptive firewall tuning, automated email triage, and endpoint containment.

Master prompt engineering for security by shaping system roles and JSON outputs to empower SIEM, SOAR, threat intel, and enrichment workflows, with audit-ready tagging.

Identify data level threats across the AI life cycle and apply security controls at stage. Include CIA triad, data types training, inheritance, output, and handling techniques like classification, minimization, anonymization.

Learn data handling techniques for secure AI systems, including cleansing, verification, and lineage and provenance. Implement safeguards like cryptographic hashes, digital signatures, and immutable ledgers to protect training data.

Learn to identify AI threats and mitigate risks by analyzing data poisoning, prompt injection, model extraction, data integrity, access controls, and AI architectures through a step-by-step threat modelling approach.

Explore how prompt templates separate system prompts from user inputs, use parameterized placeholders, and enforce context isolation to prevent prompt injection and leakage in secure artificial intelligence design.

Explore the threat landscape of AI systems, from insider and external threats to data poisoning and prompt injection, guided by ISO standards and the OWASP Top 10.

Apply role-based data access to ensure users see only what they need, while agents act autonomously and control data with tools; enforce MFA, encryption, auditing, and human-in-the-loop oversight.

Monitor ai system performance and costs by tracking prompts, queries, workload, and token usage, and apply prompt compression to reduce costs and supplemental queries while preserving answer quality.

Identify AI-specific threats like data poisoning, model inversion, and adversarial attacks, and apply compensating controls across the full AI life cycle—from design and training to deployment, monitoring, and decommission.

This lecture emphasizes AI lifecycle security by ensuring data quality, provenance, chain of custody, trusted sources, model registries, and active change control and pipeline protection.

Learn to analyze ai system attacks with a framework that identifies attack vectors, including backdoors, trojans, model and data poisoning, inversion, and theft, then apply specific and compensating controls.

Identify and defend against backdoor and trojan attacks by understanding training-time data poisoning, hidden triggers, and supply chain compromises, and implement data lineage validation, canary records, and code auditing.

Explore model and data poisoning, how corrupted training data and mislabeled or injected data mislead AI systems. Protect the entire AI life cycle to prevent dangerous production outcomes.

Explore model inversion and model theft, including query-based cloning and file theft, and risk of exposing training data and PII; apply mitigations like differential privacy, output generalization, encryption, and fingerprinting.