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• date: 21 October 2026
• posted by: DrZero

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• date: 21 October 2026
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• date: 21 October 2026
• posted by: 0nelove

Azure Infrastructure Recovery Operations
Released 10/2026
By Zachary Bennett
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Advanced | Genre: eLearning | Language: English + subtitle | Duration: 1h 9m 48s | Size: 232.7 MB

When an Azure workload degrades, on-call engineers often reach for whatever worked last time, redeploying, restarting, or escalating by habit, and the same failure returns.

When an Azure workload degrades, on-call engineers often reach for whatever worked last time, redeploying, restarting, or escalating by habit, and the same failure returns. In this course, Azure Infrastructure Recovery Operations, you'll gain the ability to diagnose a degraded Azure workload from change and health evidence. First, you'll explore how to correlate deployment, configuration, and role assignment changes against the onset of a symptom to separate deployment failure, configuration drift, permission change, policy enforcement, capacity pressure, and runtime failure, and how to decide whether the evidence justifies acting now, investigating further, or escalating. Next, you'll discover how to select and perform the right recovery action, whether that means restoring, redeploying, rolling back, reconciling drift, or adjusting capacity, within the access and governance controls of your environment, and how to prove the workload is serving again rather than merely running. Finally, you'll learn how to update the runbook where the failure differed from what was documented, decide which conditions deserve human review and which can be automated, and specify the follow-up change that makes recurrence less likely. When you're finished with this course, you'll have the skills and knowledge of Azure recovery operations needed to take a page from symptom to verified recovery and leave the runbook better than you found it.

https://nitroflare.com/view/4194A32103661A5/Azure_Infrastructure_Recovery_Operations.rar

https://rapidgator.net/file/e4df4293cfa403a7178deda169239ea4/Azure_Infrastructure_Recovery_Operations.rar.html

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• views: 11
• date: 21 October 2026
• posted by: DrZero

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AZ-900: Managing, Deploying, and Monitoring Azure Resources
Released 10/2026
By Alex Potasnick
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 1h 23s | Size: 148.6 MB

Managing Azure resources means knowing which tool fits the task: where to make changes, how to make deployments repeatable, and where to look when you need recommendations, service health information, or monitoring data.

Managing Azure resources means knowing which tool fits the task: where to make changes, how to make deployments repeatable, and where to look when you need recommendations, service health information, or monitoring data. In this course, AZ-900: Managing, Deploying, and Monitoring Azure Resources, you'll gain the ability to distinguish the Azure tools used for day-to-day management, repeatable deployment, hybrid and multicloud management, and monitoring. First, you'll explore the Azure portal, Azure CLI, Azure PowerShell, and Azure Cloud Shell. Next, you'll discover Azure Resource Manager, how Azure Arc extends Azure management beyond Azure, infrastructure as code, and ARM templates. Finally, you'll learn how Azure Advisor, Azure Service Health, and Azure Monitor - including Log Analytics, Azure Monitor alerts, and Application Insights - answer different operational questions. When you're finished with this course, you'll have the skills and knowledge of Azure management and monitoring tools needed to recognize their purpose and choose the appropriate tool in foundational AZ-900 scenarios.

Homepage

https://nitroflare.com/view/6A08700CE7D62FD/AZ-900_Managing%2C_Deploying%2C_and_Monitoring_Azure_Resources.rar

https://rapidgator.net/file/b8df0206fe104dc5de6efacbb06a652e/AZ-900_Managing,_Deploying,_and_Monitoring_Azure_Resources.rar.html

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• views: 20
• date: 21 October 2026
• posted by: 0nelove

AWS CloudFormation: Operate, Troubleshoot & Fix Drift
Published 10/2026
Created by ACHRAF ER-RAYA
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 12 Lectures ( 1h 56m ) | Size: 1.1 GB

Master AWS CloudFormation operations, Change Sets, drift detection, failure handling, and multi-region StackSet setups.

What you'll learn
⚡ Safely preview and validate infrastructure changes using CloudFormation Change Sets before applying updates to production.
⚡ Diagnose stack creation and update failures quickly and recover stacks stuck in UPDATE_ROLLBACK_FAILED status.
⚡ Deploy, manage, and troubleshoot infrastructure across multiple AWS accounts and regions using CloudFormation StackSets.
⚡ Detect unmanaged infrastructure drift and safely restore resources back to their declared CloudFormation state.

Requirements
❗ Basic understanding of core AWS services (EC2, S3, IAM) and access to an active AWS account for hands-on practice.

Description
This course contains the use of artificial intelligence.

Master AWS CloudFormation infrastructure operations with real-world enterprise techniques. Learn how to preview infrastructure updates safely, recover from stack failures, manage multi-region deployments, and detect resource drift. Designed for cloud engineers and system administrators who want complete control over AWS infrastructure automation.

Who this course is for
⭐ Cloud Engineers, DevOps professionals, and System Administrators who want to master production-grade AWS CloudFormation operations and troubleshooting.

https://nitroflare.com/view/DC6D31FDA58278D/AWS_CloudFormation_Operate%2C_Troubleshoot_%26amp%3B_Fix_Drift.part1.rar
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• views: 67
• date: 21 October 2026
• posted by: DrZero

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AWS Certified Cloud Practitioner CLF-C02 + 2 Practice Tests
Published 10/2026
Created by Elias Machuca
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 70 Lectures ( 6h 27m ) | Size: 2.7 GB

Pass CLF-C02: AWS cloud concepts, security, services & billing, plus 2 full Practice Tests and 240 original questions

What you'll learn
⚡ Explain the benefits of the AWS Cloud, the Well-Architected Framework pillars and the Cloud Adoption Framework perspectives.
⚡ Apply the shared responsibility model and choose the right AWS security, identity and compliance services for a scenario.
⚡ Describe AWS global infrastructure and choose compute, database, networking and storage services for common workloads.
⚡ Identify AWS AI/ML, analytics, integration and management services and what each one is used for.
⚡ Compare AWS pricing models, cost management tools and AWS Support plans to pick the most cost-effective option.
⚡ Pass the CLF-C02 exam with confidence after 2 full-length practice tests and 240 original exam-style questions.

Requirements
❗ No prior AWS experience is needed. Basic computer and internet skills are enough.
❗ Helpful but optional: general IT experience such as help desk, desktop support or networking.
❗ An AWS account is optional. The course explains every service without requiring hands-on labs.

Description
This course contains the use of artificial intelligence.

Get ready for the AWS Certified Cloud Practitioner (CLF-C02) exam with a focused, beginner-friendly course built on the current official CLF-C02 exam guide. Every section maps to one exam domain, and the time spent on each topic follows the published weights (Cloud Concepts 24%, Security and Compliance 30%, Cloud Technology and Services 34%, Billing, Pricing, and Support 12%), so your study time goes where the points are.

Your instructor, Elias Machuca, is CCNA-certified and has 8 years in IT doing desktop support and field technician work. This course is written for people making the same move from help desk and on-site IT into the cloud. Every concept is tied to something you already know: a locked-down admin account becomes the root user, a server closet becomes an Availability Zone, and a purchase order for hardware becomes an On-Demand instance.

What's inside

✅ 6+ hours of narrated video lectures with clean, diagram-based slides and accurate captions. Most lectures run 5 to 7 minutes, so you can study in short sessions.

✅ Exam-style questions inside the lectures, with a pause point and a full walkthrough of why each answer is right or wrong.

✅ 4 section quizzes, one after each domain.

✅ 2 full-length Practice Tests (60 questions each, 90 minutes, 70% to pass), weighted by domain, with multiple-choice and multiple-response questions and an explanation for every answer.

✅ 2 additional Practice Exams (50 questions each) as course quizzes for extra review.

✅ 240 original questions in total, and no question repeats between sets.

✅ Downloadable study notes for all four domains, plus the practice tests as printable PDF.

How the course is organized

1. Cloud Concepts: the six advantages of cloud computing, the Well-Architected Framework, the Cloud Adoption Framework, migration strategies and cloud economics.

2. Security and Compliance: the shared responsibility model, IAM, MFA, federation, KMS, Organizations and SCPs, compliance with AWS Artifact, logging, network protection and threat detection.

3. Cloud Technology and Services: global infrastructure, EC2, containers, Lambda, databases, VPC networking, S3 storage classes, AI/ML, analytics and the other in-scope services.

4. Billing, Pricing, and Support: purchasing options, Savings Plans, cost tools, consolidated billing, the current AWS Support plans and where to find help.

5. Final review: exam strategy and a rapid review of every domain.

This is an independent course. It is not affiliated with, endorsed by or sponsored by Amazon Web Services. All lecture content, slides and questions are original.

Who this course is for
⭐ Beginners preparing for the AWS Certified Cloud Practitioner (CLF-C02) exam.
⭐ Help desk, desktop support and field technicians moving into cloud roles.
⭐ Sales, finance, project management and other non-technical professionals who work with AWS.
⭐ Students who want a first cloud certification before the AWS Associate-level exams.

https://nitroflare.com/view/A136F39EADBB1AD/AWS_Certified_Cloud_Practitioner_CLF-C02___2_Practice_Tests.part1.rar
https://nitroflare.com/view/E2610B440A9D878/AWS_Certified_Cloud_Practitioner_CLF-C02___2_Practice_Tests.part2.rar
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• views: 12
• date: 21 October 2026
• posted by: 0nelove

Artificial intelligence powered AML compliance
Published 10/2026
Created by Guillermo Casal
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 14 Lectures ( 3h 41m ) | Size: 1.4 GB

From Manual Monitoring to Smart Compliance: Empowering AML Professionals with AI.

What you'll learn
⚡ • You will understand the main concepts of artificial intelligence, machine learning, advanced analytics, natural language processing and generative models
⚡ how to use AI to monitor regulatory changes and support the development and updating of policies, procedures, risk matrices, and compliance controls.
⚡ • You will learn about AI applications for the assessment of money laundering risk, customer segmentation, scoring, prioritization and continuous updating
⚡ how AI can strengthen KYC, due diligence, beneficial ownership identification, PEP analysis, sanctions, and open sources.
⚡ how to apply AI to transactional monitoring to detect anomalies, unusual patterns, fractionation, circular operations, and reduce false positives
⚡ how to use network analytics, generative AI, and OSINT to uncover relationships, reconstruct financial flows, and support the investigation of suspicious transa
⚡ identify the main risks of using AI in AML, including bias, privacy, explainability, hallucinations, model risk, traceability, and the need for supervision
⚡ • You'll be able to identify automation opportunities, select use cases, and establish a roadmap for implementing AI sustainably within the compliance program

Requirements
❗ Basic knowledge on Anti Money laundering and compliance

Description
This course presents a practical and current overview of the main applications of artificial intelligence (AI) in the prevention, detection, and investigation of money laundering. It is designed for compliance professionals, AML/CFT-AML, audit, risk, financial investigation, financial intelligence, obliged entities, lawyers, and consultants who want to understand how to responsibly incorporate these technologies into their processes.

The program begins with a fundamental aspect: the use of AI to monitor regulatory changes and support the development and updating of standards, policies, procedures, risk matrices, and internal controls. From there, it addresses the fundamentals of artificial intelligence, machine learning, advanced analytics, natural language processing, and generative models applied to the AML environment.

Participants will learn about applications to improve data quality and integration, assess risks, segment customers, update profiles, strengthen KYC and beneficial ownership identification, and optimize transactional monitoring by detecting anomalies, unusual patterns, and outlier operations, including tools to prioritize alerts and reduce false positives.

The course also delves into the analysis of financial networks and relationships, the reconstruction of fund flows, and the investigation of suspicious transactions. The use of generative AI and OSINT to analyze documents, news, public records, and links between people and companies is studied, as well as new threats related to crypto assets, synthetic identities, deepfakes, and AI-enabled fraud.

An essential component is governance. The risks of bias, privacy, explainability, model risk, hallucinations of generative systems, validation, auditing, traceability and human supervision are analyzed.

Finally, the course provides criteria for identifying automation opportunities, selecting use cases based on risk, impact, and feasibility, conducting pilots, defining indicators, and integrating AI into the existing compliance system.

Upon completion, the participant will be able to identify concrete opportunities to apply AI throughout the AML cycle, understanding both its benefits and its necessary limits and controls.

Who this course is for
⭐ • Compliance officers and professionals responsible for anti-money laundering and terrorist financing prevention programs.
⭐ • AML/AML-CFT specialists and analysts who wish to incorporate artificial intelligence into their work processes
⭐ • Internal auditors and risk professionals interested in assessing the use, controls, and risks associated with AI.
⭐ • Financial investigators and financial intelligence analysts who work with suspicious transactions, documentary information and open sources.
⭐ • Professionals from obliged entities involved in KYC, due diligence, transactional monitoring and risk management
⭐ • Lawyers and consultants who advise organizations on compliance, prevention of money laundering and technological transformation
⭐ • Compliance, risk, technology, or innovation managers who need to evaluate AI automation opportunities
⭐ • Professionals interested in understanding how to apply artificial intelligence to the cycle of prevention and investigation of money laundering.

https://nitroflare.com/view/DC68E117AA21966/Artificial_intelligence_powered_AML_compliance.part1.rar
https://nitroflare.com/view/38C496535B135D0/Artificial_intelligence_powered_AML_compliance.part2.rar

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• views: 19
• date: 21 October 2026
• posted by: 0nelove

Gnomon Workshop - Anatomy Workshop, Volume 1: Structure and Proportion
Charles Hu | Duration: 1:55 h | Video: H264 1280x720 | Audio: AAC 44,1 kHz 2ch | Size: 1,81 GB | Language: English, Russian

In this first lecture of a series focusing on human anatomy as it relates to figurative art, figure painter and instructor Charles Hu discusses key skeletal and muscular landmarks along with methods for maintaining the correct proportional relationships between the various structures of the human body. Throughout the lecture, Charles refers to skeletal reference to illustrate how skeletal elements not only connect, but more importantly, how these structures move in relation to one another, providing a clear roadmap for maintaining correct proportional relationships in your figure drawings.

Charles begins with an initial "mechanical" representation of the human form from the front view. He first discusses the basic proportions and structures of the human head. As the human head is often used as the base unit of measurement in figure drawing, the important landmarks and relationships between the brow region, eye sockets, cheek bones, nose, mouth and chin will be explored in great detail. After blocking in the head, the neck and shoulder area is discussed before moving down to the chest, stomach and pelvis regions. Again, key structural landmarks are discussed in order to understand the anatomical, structural, and proportional importance of each.

Charles then moves on to discuss arms and legs, along with correct proportional guides for laying in key elements like elbows, wrists, knees and ankles. Once completed with the anatomical sketch from the front view, he moves onto the drawings from both the back and side views, providing a clear and comprehensive study of the base human form. Suitable for all artists developing their understanding of the figure and its complex anatomical relationships, this lecture and demonstration is an immersive and critical foundation.

https://nitroflare.com/view/CF81DDD73694F4F/Anatomy_Workshop_Vol_1.rar

https://rapidgator.net/file/2f4e322b0aacaa948fc04b983898dfe8/Anatomy_Workshop_Vol_1.rar.html

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• views: 19
• date: 21 October 2026
• posted by: 0nelove

Airtable Bootcamp: Airtable N8N Automation Course
Published 2/2026
Created by Being Commerce
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 19 Lectures ( 5h 24m ) | Size: 3.5 GB

Airtable Tutorial, Automation & N8N Agent Workflows: Learn structure, views, forms, automation, and smart data systems

What you'll learn
⚡ Airtable structure and core terms
⚡ Tables, fields, and records setup
⚡ All major field types
⚡ Formulas and computed fields
⚡ Sorting, grouping, and color rules
⚡ Filters and saved views
⚡ Grid, gallery, kanban, and calendar views
⚡ Forms and advanced form setup
⚡ Version history and snapshots
⚡ Base editing and data control
⚡ Templates and table creation
⚡ Sharing and permission control
⚡ Built-in Airtable automation
⚡ Slack + Airtable automation example
⚡ Advanced automation flows
⚡ N8N setup and connection
⚡ Airtable API and credentials
⚡ Node concepts inside N8N
⚡ AI agent workflows with Airtable + N8N
⚡ Dashboard and interface setup
⚡ Omni AI basics

Requirements
❗ No prior Airtable experience required
❗ A free Airtable account
❗ A computer with internet access
❗ Basic spreadsheet familiarity helps
❗ Willingness to follow hands-on examples

Description
If you want to learn Airtable the right way — not just buttons and menus, but real working systems — this Airtable tutorial is for you.

Most people treat Airtable like a spreadsheet. That is a mistake. Airtable is much more powerful when you know how to structure bases, design fields, create smart views, connect forms, and run automations. This course walks you through all of that step by step, using real examples instead of theory.

This is a full Airtable course and hands-on bootcamp. You start from the basics and move toward advanced workflows, automation, dashboards, and AI agent setups using N8N. By the end, you won't just "know Airtable" — you will be able to set up working systems for projects, clients, teams, and products.

You will begin with Airtable structure and core terms so nothing feels confusing later. Then you will learn how tables, fields, records, and views actually work in real use. After that, you move into field types, formulas, filters, grouping, sorting, forms, sharing, and permissions. Every topic connects to a practical task.

Many learners skip structure and jump to automation too early. That leads to broken systems. This course fixes that. You will first learn how to design a clean base. Then you move into automation — both built-in Airtable automation and advanced flows with N8N.

You will also learn how to connect Airtable with Slack and other tools through automation examples. Then you move into AI agent workflows using Airtable + N8N. This is where your database becomes an active system instead of a static table.

There is also a full section on views like grid, gallery, kanban, and calendar so you can present data in the right way for each use case. You will learn forms in depth so you can collect clean data without errors. You will also learn version history, snapshots, and backup methods so your work stays safe.

Later sections cover collaboration, sharing controls, and permission levels. This matters if you work with teams or clients. Wrong settings can expose data or break tables. You will learn the safe way to manage access.

The final sections walk through dashboard interface setup and Airtable Omni AI so you understand how modern Airtable workflows are evolving.

If you skip this training, you will likely keep using Airtable like a basic sheet and miss its system power. You may waste time fixing messy tables and broken automations. This course saves that time and gives you a clean path.

After finishing, you will be able to

✨ Design structured Airtable bases

✨ Choose correct field types

✨ Write working formulas

✨ Create filtered and grouped views

✨ Collect data using forms

✨ Share data safely

✨ Run Airtable automations

✨ Connect Airtable with N8N

✨ Create AI agent workflows

✨ Set up dashboards

This Airtable bootcamp is direct, practical, and result-focused. No filler. Just real working skills.

Who this course is for
⭐ Beginners who want a full Airtable tutorial
⭐ Freelancers managing client data
⭐ Startup founders tracking projects
⭐ Marketers managing campaigns
⭐ Operations and workflow managers
⭐ No-code and low-code learners
⭐ Automation enthusiasts
⭐ Anyone who wants a full Airtable bootcamp path

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• views: 106
• date: 21 October 2026
• posted by: 0nelove

AI-Native Software Engineering with Coding Agents
Published 10/2026
Created by Baalu Mukkara
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 107 Lectures ( 13h 24m ) | Size: 9.3 GB

Specs, tests, code review, security and legacy Java: ship production software with Claude Code, Cursor and Copilot

What you'll learn
⚡ Give a coding agent the context it needs with one AGENTS md file that Claude Code, Cursor and GitHub Copilot all read
⚡ Turn tickets into agent-ready specs and plans, and slice work into pull requests a human can review properly
⚡ Drive features test-first with an agent, and catch agents that weaken or edit tests to get a green build
⚡ Review agent-written pull requests risk-first and catch scope creep, invented APIs and performance traps CI misses
⚡ Lock down agent permissions, keep secrets out of context and add a CI security gate for agent-written code
⚡ Map, test and safely modernise legacy Java code with an agent using characterisation tests and the strangler pattern
⚡ Run coding agents unattended in CI with budgets, kill switches and a human merge gate
⚡ Measure the real impact of coding agents on your team and write a team agent policy and 90-day rollout plan

Requirements
❗ At least one year of professional programming in any language
❗ Comfortable with Git, the command line and basic automated tests
❗ Able to read Python and Java code (you do not need to be fluent in both)
❗ A computer that can run Python, Java and Docker; Windows users need WSL
❗ No paid AI subscription is required: every lab can be completed with a free tier or by hand against the same tests

Description
Coding agents now write a large share of the first draft of software. Most teams adopting them hit the same problems: pull requests too big to review, tests that pass for the wrong reason, agents that ignore conventions, keys in context files, and managers asking whether any of it made delivery faster.

This course teaches the engineering discipline that makes coding agents work in a professional team. You will learn to give an agent the context and specification it needs, drive the work with tests it cannot argue with, review its output like a senior engineer, keep it inside safe limits, use it on legacy code, run it in your CI pipeline, and measure whether it improved delivery.

You work throughout on OrderHub Brownfield, a realistic codebase with history: a modern Python service and an older Java billing module with a nightly batch and undocumented rules. Every lab has a starting point and a solution, and acceptance tests decide when you are done, whichever tool you use.

You will learn to

Write one shared context file (AGENTS md) that Claude Code, Cursor and GitHub Copilot all read, and avoid the precedence traps

Turn tickets into agent-ready specs and plans, and slice work so it can be reviewed

Drive features test-first, and catch agents that weaken tests to get a green build

Review agent pull requests risk-first, including performance traps and invented APIs

Lock down agent permissions and add a CI security gate

Map, pin and strangle legacy Java code safely with an agent

Run unattended agents in CI with budgets, kill switches and a human merge gate

Measure pull-request size, review time, rework and cost, and run a fair pilot

Write a team agent policy and a 90-day rollout plan

About the tools: the course is tool-neutral at its core. Most live sessions use Claude Code, and the same workflows are shown in Cursor and GitHub Copilot. Tool-specific lectures are dated and updated as the tools change.

About the evidence: this course does not promise that you will code ten times faster. It shows you what the research found, including where AI tools slowed experienced developers down, and gives you a way to measure your own results.

Who this course is for
⭐ Professional developers and senior engineers whose teams use, or are about to use, coding agents
⭐ Tech leads who set how their team works with Claude Code, Cursor or GitHub Copilot
⭐ Engineering managers who have to show whether agent licences improved delivery
⭐ Not for first-time programmers, or anyone who wants to build an app without learning to code

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• views: 38
• date: 21 October 2026
• posted by: 0nelove

AI Red Teaming: Attacking LLM and Agentic Systems
Last updated 10/2026
Created by Vasco Patrício
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Expert | Genre: eLearning | Language: English + subtitle | Duration: 28 Lectures ( 3h 55m ) | Size: 3.5 GB

Red team LLM and agentic systems with threat modeling, prompt attacks, campaign scoring, and CI/CD testing

What you'll learn
⚡ AI security professionals and red teamers looking to assess vulnerabilities in LLM, RAG, and agentic systems.
⚡ Developers, tech leads, and architects responsible for building or securing LLM applications and AI agents.
⚡ Security, risk, and assurance professionals who need to prioritize, score, report, and track adversarial AI findings.
⚡ AI practitioners curious about prompt injection, jailbreaks, tool abuse, memory poisoning, and safe red team campaigns.

Requirements
❗ You don't need any prior AI red teaming knowledge. Familiarity with LLM applications, RAG, agents, or software security helps, but is NOT required.

Description

WHEN AI CAN ACT, A BAD OUTPUT IS ONLY THE BEGINNING
LLM applications do more than generate text. They retrieve content, call tools, retain memory, delegate work, communicate with other agents, and influence downstream systems.

Each capability creates new trust boundaries. Prompt injection can arrive through retrieved documents. A hijacked agent can misuse a legitimate tool. Poisoned memory can influence future sessions. Delegation can allow authority to travel farther than intended.

That means testing only direct prompts or model responses is not enough.

This course gives you a structured approach to AI red teaming across models, applications, tools, memory, agents, and shared state. You will learn how to identify attack paths, execute relevant attacks, evaluate outcomes, classify severity, and convert confirmed findings into repeatable regression tests.

The focus stays practical and defensive. You will examine how to run controlled campaigns in isolated, production-like environments without turning testing into a real incident.

MODULE WALKTHROUGH

Module 01: Foundations of LLM and Agentic Vulnerabilities
✨ You begin by building a shared language for LLM and agentic risk. You will use OWASP risk categories, distinguish model-level failures from system-level failures, and examine how tools, persistent memory, and inter-agent communication turn harmful outputs into harmful actions.

✨ You will also learn what AI red teaming includes: diverse adversarial inputs, escalating attack strategies, automated coverage, human creativity, scoring, severity-based triage, and clear limits.

Module 02: Threat Modeling and Attack Surfaces
✨ Next, you will map where attacks can enter and where harmful outcomes can occur. You will break down the trust boundaries around inputs, model generation, outputs, RAG, tools, planning, memory, delegation, and shared state.

✨ You will then prioritize targets using OWASP, MITRE ATLAS, likelihood, impact, ease of exploitation, and architectural trust boundaries. This creates a reasoned testing order rather than an unfocused list of attacks.

Module 03: Attack Techniques and Execution
✨ With the system mapped, you will examine how attackers manipulate prompts, conversations, training inputs, tools, agents, memory, and state.

✨ You will study direct and indirect prompt injection, jailbreak families, adversarial suffixes, obfuscation, crescendo attacks, data poisoning, backdoors, goal hijacking, unsafe affordance chaining, delegation laundering, consensus forcing, collusion, and memory poisoning.

✨ The module also covers defensive controls such as least privilege, message authentication, provenance, validation, consistency checks, state isolation, and versioning.

Module 04: Running and Scoring Red Team Campaigns
✨ Finally, you will turn individual tests into safe, repeatable campaigns. You will combine manual and automated testing in isolated, production-like environments using synthetic fixtures, mocked consequences, and stable inputs.

✨ You will learn to evaluate refusal robustness, false negatives, false positives, policy circumvention, Attack Success Rate, and finding severity. You will also validate model judges against human consensus and use deterministic checks or systems of record when outcomes can be verified directly.

✨ The course closes with CI/CD integration, scheduled scans, actionable disclosure, remediation verification, attack variants, and permanent regression tests.

LET ME TELL YOU... EVERYTHING
Some people - including me - love to know what they're getting in a package.

And by this, I mean, EVERYTHING that is in the package.

So, here is a list of everything that this course covers

✨ You will learn how the OWASP Top 10 for LLM Applications provides a shared taxonomy for vulnerabilities;

✨ You will learn to distinguish model-level failures from system-level failures and route remediation correctly;

✨ You'll learn about prompt injection, insecure output handling, sensitive information disclosure, excessive agency, and overreliance;

✨ You will learn how tools, APIs, code execution, and record changes turn manipulated outputs into consequential actions;

✨ You will learn how persistent memory allows poisoned information to influence future sessions;

✨ You will learn why inter-agent communication creates manipulation risks when agents trust peer outputs by default;

✨ You will learn how indirect prompt injection arrives through retrieved content and tool responses;

✨ You will learn why retrieved documents and tool outputs must be treated as untrusted inputs;

✨ You will learn how NIST, MITRE ATLAS, and OWASP define what to test before tools determine how to test it;

✨ You'll learn about PyRIT, garak, Promptfoo, DeepTeam, and Giskard as AI red teaming tools;

✨ You will learn to combine diverse adversarial inputs, escalating attacks, automation, expert creativity, and severity triage;

✨ You will learn to map attack points at LLM inputs, during model generation, and where outputs leave the system;

✨ You will learn how system prompts, user messages, and injected context combine trusted and untrusted content;

✨ You will learn how RAG adds an untrusted input channel through knowledge bases and retrieved documents;

✨ You will learn to validate and constrain generated code, SQL, links, markup, and downstream decisions;

✨ You will learn to threat model planning, tools, memory, delegation, and shared state across an agentic loop;

✨ You will learn why permissions must be checked at every hop in a delegation chain;

✨ You'll learn about authentication laundering, where routing through another agent makes a prohibited operation appear permissible;

✨ You will learn how shared mutable state expands blast radius and how isolated state limits interactions;

✨ You will learn to prioritize targets by likelihood, impact, ease of exploitation, and architectural trust boundaries;

✨ You will learn to use OWASP for risk coverage and MITRE ATLAS for realistic attacker tactics and techniques;

✨ You will learn why target rankings are hypotheses that should change when real attack results provide new evidence;

✨ You will learn how role-play, hypothetical scenarios, and gradual escalation disguise restricted requests;

✨ You'll learn about adversarial suffixes that use optimized strings to push models toward compliance;

✨ You will learn to test prohibited actions, data leakage, and task abandonment rather than checking only model wording;

✨ You will learn how encodings, substitutions, and translations can conceal malicious intent;

✨ You'll learn about crescendo attacks that build toward prohibited outcomes across individually harmless turns;

✨ You will learn why complete conversations must be evaluated when the unit of attack spans multiple messages;

✨ You will learn how training data poisoning can plant hidden behavior activated by a trigger phrase;

✨ You will learn why small fine-tuning datasets can amplify the influence of poisoned examples;

✨ You will learn to verify sources, pin versions, and test external models, datasets, model fileand dependencies;

✨ You will learn how goal hijacking redirects an agent from its intended objective;

✨ You will learn how legitimate tools can be used with attacker-chosen arguments or for illegitimate purposes;

✨ You'll learn about unsafe affordance chaining, where allowed tool calls combine into an unauthorized outcome;

✨ You will learn to classify tools as low-risk, medium-risk, high-risk, or forbidden for focused testing;

✨ You'll learn about consensus forcing, delegation laundering, and collusion in multi-agent systems;

✨ You will learn why agents should be authenticated and their messages validated against spoofing, alteration, and replay;

✨ You will learn to apply fan-out caps, depth limits, per-agent budgets, and explicit termination conditions;

✨ You will learn how memory poisoning can persist, spread across users, and influence downstream agents;

✨ You will learn how provenance distinguishes stale memory from poisoned memory;

✨ You will learn how versioning, validation before promotion, and consistency checkssupport detection and recovery;

✨ You will learn to run campaigns in a controlled purple environment shared by red and blue teams;

✨ You will learn to replicate representative production configurations while isolating real users and real data;

✨ You will learn to replace sensitive data with synthetic fixtures and snapshot non-deterministic inputs;

✨ You will learn to mock irreversible actions, external messages, money movement, and metered or credit-affecting calls;

✨ You will learn to measure refusal robustness across different attacks and formulations;

✨ You will learn to test false negatives, false positives, benign look-alikes, and policy circumvention;

✨ You will learn when rule-based scoring fits objective outcomes and when an LLM judge is needed for meaning;

✨ You will learn to validate attack scorers against independent human judgment and reviewer consensus;

✨ You will learn to sample clear successes, clear failures, and borderline outputs when testing a scorer;

✨ You will learn to reduce verbosity bias by requiring judges to cite specific evidence before scoring;

✨ You will learn to verify actions through deterministic checks and the system of record;

✨ You will learn how Attack Success Rate measures attack frequency but not finding severity;

✨ You will learn to classify severity using data sensitivity, reversibility, and blast radius;

✨ You will learn to define low, medium, high, and critical severity criteria before testing begins;

✨ You will learn to use adversarial testing as an automated CI/CD gate for risky releases;

✨ You will learn to regenerate and version adversarial test sets when prompts, tools, or guardrails change;

✨ You will learn to run scheduled scans for drift caused by model updates, data changes, and new attack techniques;

✨ You will learn why pipeline detection and runtime guardrails must work together;

✨ You will learn to document exact inputs, configurations, reproduction steps, boundaries, tools, and affected data;

✨ You will learn to rerun original attacks, test variants, and confirm legitimate behavior after remediation;

✨ You will learn to close findings with evidence and retain fixed attacks as permanent regression tests;

MY INVITATION TO YOU
Remember that you always have a 30-day money-back guarantee, so there is no risk for you.

Also, I suggest you make use of the free preview videos to make sure the course really is a fit. I don't want you to waste your money.

If you think this course is a fit and can take your knowledge to the next level... it would be a pleasure to have you as a student.

See on the other side!

Who this course is for
⭐ You will learn to identify, attack, evaluate, and reduce vulnerabilities in LLM and agentic systems through structured AI red teaming.
⭐ You will learn to classify model-level and system-level failures using OWASP, MITRE ATLAS, and agentic risk taxonomies.
⭐ You will learn to threat model LLM calls, RAG, agentic loops, tools, memory, delegation, shared state, and trust boundaries.
⭐ You will learn to test prompt injection, jailbreaks, obfuscation, data poisoning, goal hijacking, tool abuse, and agent attacks.
⭐ You will learn to run manual and automated campaigns in isolated, production-like sandboxes with repeatable synthetic fixtures.
⭐ You will learn to validate attack scorers, assess ASR and severity, integrate tests into CI/CD, and verify remediation.

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