{"product_id":"ai-cybersecurity-attack-and-defend","title":"AI and Cyber Security: Attack and Defend","description":"\u003cdiv\u003e\n\u003cp\u003eThis course explores the intersection of AI and cybersecurity, starting with a deep dive into AI architecture, including machine learning, deep neural networks, large language models (LLMs), Retrieval-Augmented Generation (RAG) and Agentic AI.\u003c\/p\u003e\r\n\u003cp\u003eParticipants will learn to securely train models, and manage risks using frameworks like the NIST AI RMF. The curriculum covers OWASP vulnerabilities in ML, LLMs, RAG and agentic AI, and focuses on adversarial AI attacks, and the weaponization of AI for social engineering and deepfakes. Finally, it demonstrates how to transform Security Operations (SecOps) with AI-powered detection and response and navigate the global regulatory landscape, including the EU AI Act.\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003ch3\u003eAI and Cyber Security: Attack and Defend Benefits\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eIn this course, you will:\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eDiscover the AI security ecosystem and the core principles of ML\u003c\/li\u003e\n\u003cli\u003eIdentify attack points of foundation models, genAI, LLM, RAG, and Agentic AI\u003c\/li\u003e\n\u003cli\u003eSecurely train deep neural networks and ensure privacy with federated learning\u003c\/li\u003e\n\u003cli\u003eEstablish a foundation in security risk management and categorize threats to ML models\u003c\/li\u003e\n\u003cli\u003eApply the NIST AI RMF to govern risks throughout the AI lifecycle\u003c\/li\u003e\n\u003cli\u003eImplement defense-in-depth to mitigate vulnerabilities in ML, GenAI, and Agentic systems\u003c\/li\u003e\n\u003cli\u003eUtilize AI hacking techniques for red team proactive defense\u003c\/li\u003e\n\u003cli\u003eLeverage AI-powered SecOps, using SIEM, and SOAR to enhance threat hunting and automate response\u003c\/li\u003e\n\u003cli\u003eComply with AI regulations, including the EU AI Act and US Executive Orders\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cdiv\u003e\n\u003cp paraeid=\"{b3c84709-5dae-4dc0-b97d-e0e815dde4cd}{188}\" paraid=\"690594779\"\u003e\u003cstrong\u003e\u003cspan xml:lang=\"EN-US\" data-contrast=\"auto\"\u003e\u003cspan data-ccp-parastyle=\"No Spacing\"\u003eTraining Prerequisites\u003c\/span\u003e\u003c\/span\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cp paraeid=\"{b3c84709-5dae-4dc0-b97d-e0e815dde4cd}{194}\" paraid=\"707607176\"\u003e\u003cspan xml:lang=\"EN-US\" data-contrast=\"auto\"\u003e\u003cspan data-ccp-parastyle=\"No Spacing\"\u003eAttendees should have foundational knowledge in networking and cybersecurity.\u003c\/span\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003eAI Cybersecurity Training Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eLearning Objectives\u003c\/h4\u003e\n\u003cp\u003eChapter 1: Architecture and Operation of AI\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eEvolution of AI technology from ML and Deep Neural Networks to Agentic AI\u003c\/li\u003e\n\u003cli\u003eGenAI system architecture and attack points\u003c\/li\u003e\n\u003cli\u003eTraining models with MLOps pipeline, and securing and partitioning datasets\u003c\/li\u003e\n\u003cli\u003eTransfer learning of Foundation Models and fine-tuning\u003c\/li\u003e\n\u003cli\u003eNLP mechanics comprising word embeddings, self-attention, and LLM context window\u003c\/li\u003e\n\u003cli\u003eConnecting to knowledge bases with RAG and context window overflow\u003c\/li\u003e\n\u003cli\u003eAI agents functions (Perception, Planning, Action, Learning), and enrichment “Loop of death”\u003c\/li\u003e\n\u003cli\u003eDiscriminative vs Generative AI models and multimodal prompting\u003c\/li\u003e\n\u003cli\u003eDo Nows: Tinker With a Neural Network using TensorFlow Playground, Exploring CNN, Examine Federated Learning, Google Natural Language API Analysis, Building AI Agents with Vertex AI, Google AI Studio\u003c\/li\u003e\n\u003cli\u003eDemo: Creating a Co-Occurrence Matrix\u003c\/li\u003e\n\u003cli\u003eLAB: Utilizing a Small Language Model\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eChapter 2: Risk in Adopting AI Solutions\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eMitigating risk with CIANA+PS pillars and Risk Register\u003c\/li\u003e\n\u003cli\u003eTracking AI vulnerabilities using CVE and CWE dictionaries\u003c\/li\u003e\n\u003cli\u003eZero Trust Frameworks applied to AI “Quad of IAM”\u003c\/li\u003e\n\u003cli\u003eEthics and Autonomy with human in the loop, and risks with PII, Intellectual Property and Bias\u003c\/li\u003e\n\u003cli\u003eAI Threat Mind Map categorizing threats to\/from models, including human risks\u003c\/li\u003e\n\u003cli\u003eNIST AI RMF core functions(Govern, Map, Measure, and Manage), risks and TEVV processes\u003c\/li\u003e\n\u003cli\u003eMitigate Risk With Trustworthy AI and Privacy-Enhanced AI\u003c\/li\u003e\n\u003cli\u003eAssessing maturity with the AI CMM\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cul\u003e\n\u003cli\u003eAI Risk Assessment Process with RMF Generative AI Profile\u003c\/li\u003e\n\u003cli\u003eMitigating GenAI Risks with grounding, risk signals and DLP safeguards\u003c\/li\u003e\n\u003cli\u003eOWASP Top 10 ML, LLM and Agentic AI Security Risks\u003c\/li\u003e\n\u003cli\u003eDo Nows: Known AI Vulnerabilities, Harm to Organizations, NIST AI RMF Playbook, OWASP AI Privacy, Trolley Problem Ethical Dilemma, Risks of “Free Services”, DoD RAI Risk Assessment, Detection with DLP and GenAI, Attacking the OWASP Top Ten ML, LLM and Agentic AI\u003c\/li\u003e\n\u003cli\u003eLAB: Conducting an AI Risk Assessment\u003c\/li\u003e\n\u003cli\u003eLAB: Deidentify GenAI Responses\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cul type=\"disc\"\u003e\u003c\/ul\u003e\n\u003cp\u003eChapter 3: Securing AI Vulnerabilities\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eIntegrate security into all phases of AI SDLC Lifecycle\u003c\/li\u003e\n\u003cli\u003eAdversarial attacks including, GenAI classification, NLP, Dataset poisoning, backdoor Trojan, “Man in the Prompt”\u003c\/li\u003e\n\u003cli\u003eSecure AI with AI-BOM, sanitization, and security controls\u003c\/li\u003e\n\u003cli\u003eSecure RAG against, indirect prompt injection, data poisoning, embedding inversion, pirate attack\u003c\/li\u003e\n\u003cli\u003eAgentic AI kill chain and threat model\u003c\/li\u003e\n\u003cli\u003eExtending the SAIF Risk Map for AI Agents\u003c\/li\u003e\n\u003cli\u003eHacking Agentic AI through rebus, excessive agency, goal hijacking and tool misuse\u003c\/li\u003e\n\u003cli\u003ePrompt Hacking with injection, jailbreaking and system prompt leaking\u003c\/li\u003e\n\u003cli\u003eDefensive Guardrails including the Google SAIF, AI Agent Firewalls and Model Armor\u003c\/li\u003e\n\u003cli\u003eOWASP AI Threat Model\u003c\/li\u003e\n\u003cli\u003eAI red teaming for proactive defense and interactive testing\u003c\/li\u003e\n\u003cli\u003eSecuring Gen AI with Logging and Monitoring, and Agentic AI with Evaluation Services and AgentOps\u003c\/li\u003e\n\u003cli\u003eDo Nows: Coercing Misclassification of an ML Model, OWASP Agentic AI Threats and Mitigations, OWASP Agentic AI Top 10: Threats in the Wild, System Prompt Security, Prompt Engineering for Generative AI, SAIF Risk Self Assessment, OWASP AI Security Matrix, OWASP Threat Modeling of an LLM Application, DEFCON GenAI Attack Strategies, OWASP GenAI Red Teaming Strategy, RAI Toolkit, Investigating Adversarial Attacks with ART\u003c\/li\u003e\n\u003cli\u003eLAB: Penetration Testing an AI System\u003c\/li\u003e\n\u003cli\u003eLAB: Safeguarding With Gemini AI\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eChapter 4: AI Powered Hacking\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eTraditional hacking phases enhanced by AI smart automation, reinforcement learning to evade detection and Out-of-the-Box AI Thinking\u003c\/li\u003e\n\u003cli\u003eAutonomous hacking in the DARPA DEFCON Cyber Grand Challenge\u003c\/li\u003e\n\u003cli\u003eBelievable AI-Infused Social Engineering and GenAI fraud\u003c\/li\u003e\n\u003cli\u003eDeepfake technology fabricates target’s video and audio\u003c\/li\u003e\n\u003cli\u003eAI infused tools including Nmap, Metasploit, and Wireshark enhancements\u003c\/li\u003e\n\u003cli\u003eSide channel attacks like AI acoustic keyboard monitoring\u003c\/li\u003e\n\u003cli\u003eThe Long Con using AI to build trust and erode resilience over time\u003c\/li\u003e\n\u003cli\u003eDoNows: Bing Chat as a Social Engineer, Famous Deepfakes, Creating Deepfakes\u003c\/li\u003e\n\u003cli\u003eLAB: Enhance Hacking With GenAI\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eChapter 5: Defending Security Operations With AI\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eModern SecOps using Autonomic Security Operations and CD\/CR pipelines\u003c\/li\u003e\n\u003cli\u003eBenefits of AI in Cybersecurity and AI Powering SecOps Functions\u003c\/li\u003e\n\u003cli\u003eAI Powered detection for intrusions and malware\u003c\/li\u003e\n\u003cli\u003eAI-Powered IGA, IAM, Security Analytics and Incident Response\u003c\/li\u003e\n\u003cli\u003eGenAI in SIEM, SOAR, TIM using intelligent data ingestion, automated playbooks and NLP\u003c\/li\u003e\n\u003cli\u003eThe MITRE ATLAS matrix for understanding AI adversarial tactics\u003c\/li\u003e\n\u003cli\u003eGoogle AI SecOps leveraging Gemini, SecLM and Mandiant for threat intelligence\u003c\/li\u003e\n\u003cli\u003eGoogle Agentic SOC Defense\u003c\/li\u003e\n\u003cli\u003eMicrosoft Security Copilot and GitHub Copilot for malware reverse engineering and policy summarization\u003c\/li\u003e\n\u003cli\u003eDoNows: Threat Intelligence Platform AV-ATLAS, MITRE ATLAS Navigator\u003c\/li\u003e\n\u003cli\u003eLAB: Analyze a Codebase With Gemini\u003c\/li\u003e\n\u003cli\u003eLAB: SecOps Threat Hunting With AI\u003c\/li\u003e\n\u003cli\u003eLAB: Anatomy of an AI Model Attack\u003c\/li\u003e\n\u003cli\u003eLAB: Secure Coding With AI\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eChapter 6: Regulating AI Governance\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eGlobal regulations such as UN Ethics of AI and accountability standards\u003c\/li\u003e\n\u003cli\u003eThe EU AI Act risk based framework\u003c\/li\u003e\n\u003cli\u003eUS Executive AI Order\u003c\/li\u003e\n\u003cli\u003ePillars of Trustworthy AI comprising responsible, reliable, and resilient systems\u003c\/li\u003e\n\u003cli\u003eGoogle’s Responsible AI and the \"Agentic\" Shift\u003c\/li\u003e\n\u003cli\u003eEU AIGA Hourglass Model Governance framework\u003c\/li\u003e\n\u003cli\u003eThe OECD AI system lifecycle stages\u003c\/li\u003e\n\u003cli\u003eModel AI Governance Framework (MGF) for Agentic AI\u003c\/li\u003e\n\u003cli\u003eFour dimensions of Agentic AI\u003c\/li\u003e\n\u003cli\u003eDoNow: AIGA AI Governance Lifecycle\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e","brand":"Learning Tree","offers":[{"title":"267C01US \/ 2026-07-22T09:00:00 \/ Online","offer_id":47534203404507,"sku":"US-1216-IL","price":2228.0,"currency_code":"USD","in_stock":true},{"title":"268A04CN \/ 2026-08-26T09:00:00 \/ Ottawa","offer_id":47534203437275,"sku":"US-1216-IL","price":2228.0,"currency_code":"USD","in_stock":true},{"title":"268B62US \/ 2026-08-05T09:00:00 \/ 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