Tech Career Roadmaps

From Beginner to Job – Ready Step-by-Step Guide

“Start your journey today – choose your path and become job – ready in 90 – 180 days.”

AI & Machine Learning Roadmap: From Basics to Responsible AI

Build It. Train It. Secure It.

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From AI basics to building and securing real-world AI systems. Learn machine learning, generative AI, foundation models, responsible AI, and security — step by step.
Phase 1 Fundamentals of AI and ML No experience needed ▶
Concepts & Terminologies of AI
Introduction to Artificial Intelligence Machine learning (ML) Deep learning Generative AI overview
Practical Use Cases of AI
AI for business efficiency Limitations of AI Types of ML problems AWS AI services
ML Development Lifecycle
Machine learning pipeline Amazon SageMaker services
🎯 Goal: Understand what AI and ML are, how they work, and where they are used in the real world
Phase 2 Fundamentals of Generative AI How Gen AI actually works ▶
Concepts of Generative AI
Overview of Gen AI Core components of Gen AI Transformer architecture In-context learning Multimodal & diffusion models Gen AI use cases Gen AI architectures Key techniques & challenges
Capabilities & Limitations for Solving Business Problems
Capabilities of Gen AI Limitations of Gen AI
AWS for Building Gen AI
Advantages of AWS Gen AI services AWS infra for Gen AI applications Pricing models for LLMs AWS services for Gen AI apps
🎯 Goal: Understand how generative AI systems work and how to build them on AWS
Phase 3 Applications of Foundation Models Build with pre-trained models ▶
Design Considerations for Applications
Selection criteria for pre-trained models Biases in training data Availability & compatibility Customisation & explainability Inference parameters Vector databases & retrieval Retrieval-Augmented Generation (RAG)
Effective Prompt Engineering Techniques
Introduction to prompts Prompting techniques Best practices & risk of prompt engineering
Training & Fine-Tuning Foundation Models
Key elements of training a foundation model Fine-tuning techniques Steps in data preparation for fine-tuning AWS data preparation tools
Evaluating Foundation Model Performance
Deployment considerations Evaluation metrics Human evaluation Integration into applications
🎯 Goal: Use, fine-tune, and evaluate foundation models to build real AI applications
Phase 4 Responsible AI Build fair & ethical AI ▶
Development of Responsible AI Systems
Intro to responsible AI Addressing bias in AI models Ethical dataset characteristics Ethical model selection AWS services for monitoring & bias detection
Transparency & Explainability
Recognising the importance of transparent & explainable models Transparency & explainability concepts Tools & techniques for transparency
🎯 Goal: Build AI systems that are fair, transparent, explainable, and ethically sound
Phase 5 Security, Compliance & Governance Protect and govern AI systems ▶
Secure AI Systems
Shared responsibility model Identity & access management (IAM) Identity federation Logging & monitoring S3 block public access SageMaker role manager Data encryption Security configuration & network management AI model security
Governance & Compliance Regulations for AI
Compliance standards & regulations Compliance tools & AWS services
🎯 Goal: Secure, govern, and ensure compliance of AI systems in real-world deployments
Career Roles
AI/ML Engineer ML Ops Engineer AI Solutions Architect AI Ethics Specialist AI Security Engineer Gen AI Developer Data Scientist
Dive deep into how generative AI works — from transformer architecture to building real Gen AI applications on AWS.
Core Concepts How Generative AI Works The engine behind LLMs ▶
Overview of Gen AI Core components Transformer architecture In-context learning Multimodal models Diffusion models Gen AI architectures Key techniques & challenges
🎯 Goal: Understand the inner mechanics of how generative AI systems produce content
Business Use Gen AI for Business Problems Apply Gen AI practically ▶
Gen AI use cases Capabilities of Gen AI Limitations of Gen AI AI for business efficiency
🎯 Goal: Know when and how to apply Gen AI to solve real business problems
AWS Platform Building Gen AI on AWS Deploy with AWS services ▶
Advantages of AWS Gen AI services AWS infra for Gen AI applications Pricing models for LLMs AWS services for Gen AI apps Amazon SageMaker
🎯 Goal: Use AWS to build, deploy, and scale generative AI applications
Learn to select, prompt, fine-tune, and evaluate pre-trained foundation models to build powerful AI applications.
Step 1 Selecting & Designing with Foundation Models Choose the right model ▶
Selection criteria for pre-trained models Biases in training data Availability & compatibility Customisation & explainability Inference parameters Vector databases & retrieval RAG (Retrieval-Augmented Generation)
🎯 Goal: Pick and design with the right foundation model for your use case
Step 2 Prompt Engineering Get the best from LLMs ▶
Introduction to prompts Prompting techniques Best practices of prompt engineering Risks & limitations of prompt engineering
🎯 Goal: Write effective prompts that get accurate, reliable results from AI models
Step 3 Training & Fine-Tuning Customise models for your needs ▶
Key elements of training a foundation model Fine-tuning techniques Steps in data preparation for fine-tuning AWS data preparation tools
🎯 Goal: Fine-tune a foundation model on your own data for specialised tasks
Step 4 Evaluation & Deployment Measure and ship your model ▶
Deployment considerations Evaluation metrics Human evaluation Integration into applications
🎯 Goal: Measure model performance accurately and deploy it into production applications
Build AI systems that are fair, transparent, and ethical. Learn to detect bias, ensure explainability, and follow responsible AI principles.
Part 1 Developing Responsible AI Systems Fairness & ethics in AI ▶
Intro to responsible AI Addressing bias in AI models Ethical dataset characteristics Ethical model selection AWS services for monitoring & bias detection
🎯 Goal: Identify and reduce bias in AI systems from data to deployment
Part 2 Transparency & Explainability Make AI decisions understandable ▶
Why transparency matters in AI Explainable AI (XAI) concepts Transparency & explainability tools Techniques for transparent models
🎯 Goal: Make AI decisions interpretable and explainable to users and stakeholders
Secure, govern, and ensure compliance of AI systems. Learn IAM, encryption, monitoring, and regulatory compliance for AI on AWS.
Part 1 Securing AI Systems Protect your AI from threats ▶
Access & Identity
Shared responsibility model Identity & access management (IAM) Identity federation SageMaker role manager
Data & Network Security
Data encryption S3 block public access Security configuration & network management AI model security
Monitoring
Logging & monitoring
🎯 Goal: Implement end-to-end security for AI systems and data on AWS
Part 2 Governance & Compliance for AI Meet regulations & standards ▶
Compliance standards & regulations for AI systems Compliance tools & AWS services Governance frameworks Audit & accountability
🎯 Goal: Ensure AI systems meet legal, regulatory, and organisational governance requirements
Career Roles
AI Security Engineer AI Compliance Analyst Cloud AI Architect AI Ethics & Governance Lead

AWS Cloud Practitioner Roadmap: From Cloud Basics to Certification

Launch It. Scale It. Secure It.

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Master AWS Cloud from the ground up. Learn core cloud concepts, security, all major AWS services, and billing — aligned with the AWS Cloud Practitioner certification.
Phase 1 Cloud Concepts What is cloud & why AWS? ▶
Core Topics
What is cloud computing? What is cloud computing? Benefits of the AWS cloud Design principles of the AWS cloud Migration to AWS cloud Cloud economics
🎯 Goal: Understand what cloud computing is, why businesses choose AWS, and how to plan a cloud migration
Phase 2 Security & Compliance Secure your cloud environment ▶
Security Foundations
Shared responsibility model AWS cloud security concepts AWS cloud governance AWS cloud compliance
Access & Resources
AWS access management capabilities Components & resources for security
🎯 Goal: Understand who is responsible for what in AWS security and how to control access properly
Phase 3 AWS Cloud Services The full AWS services catalogue ▶
Deploying & Operating
Deploying on AWS Operating on AWS AWS global infrastructure
Core AWS Services
💻
Compute
EC2, Lambda, ECS
🗃
Database
RDS, DynamoDB, Aurora
🌐
Network
VPC, Route 53, CloudFront
💾
Storage
S3, EBS, Glacier
🤖
AI & ML
SageMaker, Rekognition
📈
Analytics
Athena, Redshift, Glue
Other In-Scope AWS Services
Additional AWS services list Managed services overview Serverless services
🎯 Goal: Know what each core AWS service does and when to use it
Phase 4 Billing, Pricing & Support Manage costs & get help ▶
Pricing & Billing
AWS pricing model Billing & budget management Cost management tools AWS Cost Explorer AWS Free Tier
Support & Resources
AWS tech resources AWS support plan options AWS Trusted Advisor AWS Well-Architected Framework
🎯 Goal: Control your AWS spending, understand pricing models, and know where to get support
Career Roles
Cloud Practitioner Cloud Security Engineer AWS Solutions Architect Cloud Cost Analyst DevOps Cloud Engineer Cloud SOC Analyst
Understand the fundamentals of cloud computing and why AWS is the world’s leading cloud platform.
Topic 1 What is Cloud Computing? The foundation of everything ▶
Definition of cloud computing On-premise vs cloud Types of cloud (public, private, hybrid) Cloud service models (IaaS, PaaS, SaaS)
🎯 Goal: Explain what cloud computing is and the different types of cloud deployments
Topic 2 Benefits of the AWS Cloud Why businesses choose AWS ▶
Agility & speed Elasticity & scalability Cost savings Global reach Reliability & availability Security at scale
🎯 Goal: Articulate the key business and technical benefits of moving to AWS
Topic 3 AWS Design Principles & Migration Build & move to the cloud right ▶
AWS Well-Architected Framework 6 pillars of cloud design Cloud migration strategies (6 R’s) Cloud economics & TCO
🎯 Goal: Apply AWS design principles and plan a structured migration to the cloud
Learn how AWS handles security and compliance — and what your responsibilities are as a customer.
Part 1 Shared Responsibility Model Who secures what? ▶
AWS responsibility (security OF the cloud) Customer responsibility (security IN the cloud) Shared controls
🎯 Goal: Clearly understand the boundary between AWS and customer security responsibilities
Part 2 Governance & Compliance Meet regulatory requirements ▶
AWS cloud security concepts AWS governance frameworks AWS compliance programs AWS Artifact (compliance reports)
🎯 Goal: Know how AWS helps organisations meet security and compliance regulations
Part 3 Access Management & Security Resources Control who can do what ▶
AWS IAM (users, roles, policies) AWS access management capabilities Multi-factor authentication (MFA) AWS Shield (DDoS protection) AWS WAF (web application firewall) AWS KMS (key management) Components & resources for security
🎯 Goal: Implement access controls and use AWS security tools to protect your cloud resources
Explore the full range of AWS services — from compute and storage to AI/ML and analytics. Know what each service does and when to use it.
Category 1 Compute Services Run your applications ▶
Amazon EC2 (virtual servers) AWS Lambda (serverless) Amazon ECS / EKS (containers) AWS Elastic Beanstalk AWS Batch
🎯 Goal: Choose the right compute service for any workload — from VMs to serverless
Category 2 Storage Services Store data securely ▶
Amazon S3 (object storage) Amazon EBS (block storage) Amazon EFS (file storage) AWS Glacier (archive storage) AWS Storage Gateway
🎯 Goal: Select the right storage type based on access patterns, cost, and durability needs
Category 3 Database Services Manage your data ▶
Amazon RDS (relational DB) Amazon DynamoDB (NoSQL) Amazon Aurora Amazon Redshift (data warehouse) Amazon ElastiCache
🎯 Goal: Choose the right database service based on your application’s data requirements
Category 4 Network Services Connect everything securely ▶
Amazon VPC (virtual private cloud) AWS Route 53 (DNS) Amazon CloudFront (CDN) AWS Direct Connect AWS Transit Gateway
🎯 Goal: Design and secure cloud networking with the right AWS network services
Category 5 AI & ML + Analytics Services Intelligent data-driven apps ▶
AI & ML
Amazon SageMaker Amazon Rekognition Amazon Comprehend Amazon Lex Amazon Bedrock
Analytics
Amazon Athena AWS Glue (ETL) Amazon Kinesis AWS Lake Formation Amazon QuickSight (BI)
🎯 Goal: Use AWS AI/ML and analytics services to build intelligent, data-driven applications
Understand how AWS charges you, how to control costs, and what support options are available.
Part 1 AWS Pricing Model How AWS charges work ▶
Pay-as-you-go pricing Reserved instances & savings plans Spot instances AWS Free Tier Pricing calculator
🎯 Goal: Understand AWS pricing structures and choose the most cost-effective options
Part 2 Billing & Cost Management Control your AWS spend ▶
AWS Billing dashboard AWS Budgets AWS Cost Explorer Cost allocation tags AWS Cost & Usage Reports Consolidated billing (AWS Organizations)
🎯 Goal: Monitor, analyse, and optimise your AWS costs using the right tools
Part 3 AWS Tech Resources & Support Get the right level of help ▶
AWS Support plans (Basic, Developer, Business, Enterprise) AWS Trusted Advisor AWS Personal Health Dashboard AWS Knowledge Center AWS re:Post (community) AWS Well-Architected Tool
🎯 Goal: Choose the right AWS support plan and use available resources to solve problems fast
Career Roles
AWS Cloud Practitioner Cloud Security Engineer Solutions Architect Cloud FinOps Analyst Cloud DevOps Engineer

DevOps Roadmap: From Fundamentals to AI-Powered DevOps

Build It. Automate It. Deploy It.

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From SDLC basics to AI-powered DevOps. 20 topics covering Linux, CI/CD, Docker, Kubernetes, cloud platforms, security, and monitoring — step by step.
Phase 1SDLC FundamentalsStart here▶
Core Concepts
SDLC modelsDevOps introductionCI/CD introduction
🎯 Goal: Understand the software delivery lifecycle and where DevOps fits in
Phase 2DevOps Tools IntroTool landscape overview▶
Overview
DevOps toolchain overviewCategories of DevOps toolsWhen to use which tool
🎯 Goal: Get a bird’s-eye view of all DevOps tools before diving deep
Phase 3Command Line & OSLinux & scripting▶
Skills
Linux administrationShell scriptingPython basicsFile system & permissions
🎯 Goal: Comfortably navigate Linux and write shell/Python automation scripts
Phase 4NetworkingConnectivity essentials▶
Concepts
TCP/IP fundamentalsDNS & HTTP/HTTPSFirewalls & VPNsLoad balancingNetwork troubleshooting
🎯 Goal: Understand networking concepts critical to cloud and DevOps environments
Phase 5Cloud InfrastructuresAWS, Azure, GCP & more▶
Platforms
AWS CloudAzure CloudGCPOracle Cloud
Core Services
Compute (EC2, VMs)Storage (S3, Blob)Networking (VPC, VNet)IAM & Security
🎯 Goal: Provision and manage cloud resources across major cloud platforms
Phase 6Infrastructure as CodeAutomate your infra▶
Tools
TerraformCloudFormationARM Templates (Azure)
Concepts
IaC principlesState managementModules & reusability
🎯 Goal: Provision and manage infrastructure using code instead of manual clicks
Phase 7Source Code ManagementGit & version control▶
Tools
GitGitHubSVNPerforceClearcase
Concepts
Branching strategiesPull requests & code reviewGit workflows
🎯 Goal: Manage source code professionally using Git and GitHub workflows
Phase 8Java JDKBuild tool foundation▶
Topics
Java JDK intro & whyJVM & classpath basicsJava for DevOps pipelines
🎯 Goal: Understand Java enough to work with Java-based build tools and pipelines
Phase 9CI/CDThe heart of DevOps▶
CI/CD Tools
JenkinsJenkins pluginsGitHub ActionsGitOpsArgoCD
Build — Java
AntMaven.jar / .war / .ear files
Build — C/C++ & .Net
MakefileVisual Studio.exe files
Testing & QA
JUnitSeleniumSonarQube
Artifacts
JFrog ArtifactoryPipeline scripts
🎯 Goal: Build full CI/CD pipelines from code commit to artifact delivery
Phase 10Web ServerDeploy applications▶
Tools
TomcatApache HTTP ServerNginx
Concepts
Deployment strategiesApp server vs web server
🎯 Goal: Deploy web applications to app servers as part of a CD pipeline
Phase 11ContainerizationDocker & containers▶
Tools
DockerDockerHubDocker Compose
Concepts
Images vs containersDockerfileContainer registriesContainer networking
🎯 Goal: Containerize any application using Docker and manage images in registries
Phase 12Configuration ManagementAutomate server config▶
Tools
AnsiblePuppetChefSalt
Concepts
Playbooks & rolesIdempotencyInventory management
🎯 Goal: Automate server configuration and application deployment at scale
Phase 13Container OrchestrationKubernetes & Helm▶
Tools
KubernetesHelmkubectl
Concepts
Pods & deploymentsServices & ingressConfigMaps & secretsAuto-scaling
🎯 Goal: Orchestrate containerized applications in production using Kubernetes
Phase 14ProjectsHands-on practice▶
Environments
On WindowsOn LinuxAWS-basedAzure-basedGCP-based
Scope
Small projects in all toolsEnd-to-end pipelinesMulti-cloud projects
🎯 Goal: Apply all learned tools in real projects across different environments
Phase 15Continuous MonitoringObserve everything▶
Tools
GrafanaPrometheusCloudWatchSNS (alerts)
Concepts
Metrics, logs & tracesDashboards & alertingObservability principles
🎯 Goal: Monitor applications and infrastructure in real time with dashboards and alerts
Phase 16DevSecOpsSecurity in the pipeline▶
Tools
SonarQubeJFrog XrayTrivy
Concepts
Shift-left securitySAST & DASTContainer image scanningSecrets management
🎯 Goal: Integrate security checks at every stage of the DevOps pipeline
Phase 17AI-Powered DevOpsGitHub Copilot & more▶
Tools
GitHub CopilotAI code reviewAI-assisted testing
Use Cases
AI for pipeline optimizationIntelligent alertingAuto-remediation
🎯 Goal: Leverage AI tools to boost productivity across the DevOps workflow
Phase 18DevOps using AWSAWS-native DevOps▶
AWS DevOps Services
CodeCommitCodeBuildCodeDeployCodePipelineECS / EKS
🎯 Goal: Build complete DevOps pipelines using AWS-native tools and services
Phase 19DevOps using AzureAzure-native DevOps▶
Azure DevOps Services
Azure ReposAzure PipelinesAzure ArtifactsAKSAzure Monitor
🎯 Goal: Build complete DevOps pipelines using Azure DevOps and Azure cloud services
Phase 20AI + Sec + Dev + OpsThe full convergence▶
Convergence Topics
AI-driven securityML in pipelinesAutomated complianceIntelligent observability
Career Roles
DevOps Engineer Cloud Engineer Site Reliability Engineer Platform Engineer DevSecOps Engineer MLOps Engineer
🎯 Goal: Master the convergence of AI, security, development, and operations
Build the essential base: SDLC, Linux, networking, Git, and Java — the skills every DevOps engineer needs before touching pipelines.
1SDLC FundamentalsWaterfall, Agile, DevOps▶
SDLC modelsDevOps introductionCI/CD introduction
🎯 Understand the full software delivery lifecycle
2Command Line & LinuxShell & Python scripting▶
Linux administrationShell scriptingPython basics
🎯 Automate tasks with Linux and scripting languages
3NetworkingTCP/IP, DNS, firewalls▶
TCP/IP fundamentalsDNS & HTTP/HTTPSFirewalls & load balancing
🎯 Understand networking for cloud and DevOps
4Source Code ManagementGit & GitHub▶
GitGitHubSVN / Perforce / ClearcaseBranching strategies
🎯 Manage code professionally with Git workflows
5Java JDKBuild tool foundation▶
Java JDK introWhy Java in DevOpsJVM basics
🎯 Understand Java enough for DevOps build pipelines
The heart of DevOps — automate build, test, and deploy. From Jenkins to ArgoCD, Ant to Maven, JUnit to SonarQube.
CI ToolsContinuous IntegrationJenkins & GitHub Actions▶
JenkinsJenkins pluginsGitHub ActionsPipeline scripts
🎯 Automate code integration and build triggers
BuildBuild ToolsMaven, Ant, Make▶
Java
AntMaven.jar / .war / .ear
C/C++ & .Net
MakefileVisual Studio.exe files
🎯 Build artifacts from source code automatically
Test & QATesting & Quality GatesJUnit, Selenium, SonarQube▶
JUnit (unit testing)Selenium (UI testing)SonarQube (code quality)
🎯 Catch bugs and enforce code quality automatically
CD ToolsContinuous Delivery/DeploymentArgoCD & GitOps▶
GitOps principlesArgoCDJFrog ArtifactoryTomcat deployment
🎯 Deliver artifacts to production reliably and automatically
Provision, manage, and scale infrastructure on cloud platforms using code, containers, and orchestration tools.
CloudCloud PlatformsAWS, Azure, GCP, Oracle▶
AWSAzureGCPOracle CloudCore services (compute, storage, networking)
🎯 Provision and manage cloud resources
IaCInfrastructure as CodeTerraform & CloudFormation▶
TerraformCloudFormationARM TemplatesState management
🎯 Automate infrastructure provisioning with code
ContainersContainerizationDocker & DockerHub▶
DockerDockerHubDockerfileDocker Compose
🎯 Package and ship apps in containers
ConfigConfiguration ManagementAnsible, Puppet, Chef▶
AnsiblePuppetChefSalt
🎯 Automate server configuration at scale
OrchestrationContainer OrchestrationKubernetes & Helm▶
KubernetesHelmPods, services & ingressAuto-scaling
🎯 Orchestrate containers in production at scale
Take your DevOps skills further — monitoring, security, AI tooling, and cloud-specific pipelines on AWS and Azure.
MonitoringContinuous MonitoringGrafana, Prometheus, CloudWatch▶
GrafanaPrometheusCloudWatchSNS alertsObservability principles
🎯 Monitor systems and alert on issues in real time
SecurityDevSecOpsSonarQube, JFrog, Trivy▶
SonarQubeJFrog XrayTrivyShift-left securityContainer scanning
🎯 Embed security at every stage of the pipeline
AIAI-Powered DevOpsGitHub Copilot & more▶
GitHub CopilotAI code reviewAI-assisted testingIntelligent alerting
🎯 Use AI to accelerate DevOps workflows
AWSDevOps using AWSNative AWS pipeline tools▶
CodeCommitCodeBuildCodeDeployCodePipelineECS / EKS
🎯 Build full DevOps pipelines with AWS-native services
AzureDevOps using AzureAzure DevOps services▶
Azure ReposAzure PipelinesAzure ArtifactsAKSAzure Monitor
🎯 Build full DevOps pipelines with Azure DevOps services
FinalAI + Sec + Dev + OpsFull convergence▶
AI-driven securityML in pipelinesAutomated complianceIntelligent observability
Career Roles
DevOps EngineerCloud EngineerSREPlatform EngineerDevSecOps EngineerMLOps Engineer
🎯 Master the convergence of AI, security, dev, and ops

Cyber Security Roadmap: From Zero to Professional

Break It. Defend It. Master It.

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From zero to job-ready. Build foundations, master offensive and defensive tools, then specialise as a hacker or defender.
Phase 1 Pre-Security (SEC0) No experience needed ▶
Intro to Cyber Security
Offensive security introDefensive security introCareers in cyber
Network Fundamentals
What is networking?Intro to LANOSI modelPackets & framesExtending your network
How the Web Works
DNS in detailHTTP in detailHow websites workPutting it all together
Computer Fundamentals
Inside a computer systemComputer typesClient-server basicsVirtualisation basicsCloud computing fundamentals
OS Basics
OS introductionWindows basicsLinux CLI basicsWindows CLI basicsOS security
Software Basics
Data representationData encodingPython demoJavaScript demoSQL basics
Attacks & Defenses
CIA triadCryptography concepts
🎯 Goal: Understand how computers, networks & the internet actually work — before touching security tools
Phase 2 Cyber Security 101 (SEC1) Core practical skills ▶
Linux & Windows Deep Dive
Linux fundamentals 1–3Windows fundamentals 1–3Active Directory basicsWindows CMDPowerShellLinux shells
Networking
Networking conceptsCore protocolsSecure protocolsWiresharkTcpdumpNmap
Cryptography
Crypto basicsPublic key cryptographyHashing basicsJohn the Ripper
Exploitation Basics
CVE-2024-21413Metasploit introMetasploit exploitationMeterpreter
Web Hacking
Web app basicsJavaScript essentialsSQL fundamentalsBurp Suite
Offensive Tools
HydraGobusterSQLMapShells overview
Defensive Security
SOC fundamentalsDigital forensicsIncident responseLogs fundamentalsSIEM introFirewall fundamentalsIDS fundamentals
Defensive Tools
CyberChefCAPAREMnuxFlareVM
OWASP Top 10 (2025)
IAAA failuresApplication design flawsInsecure data handling
🎯 Goal: Gain practical offensive + defensive skills across OS, networking, web hacking & SOC
Phase 3 Security Engineer Path Design & defend systems ▶
Foundations
Security engineer introSecurity principlesCryptographyIdentity & access management
Threats & Risks
Governance & regulationThreat modellingRisk managementVulnerability management
Network & System Security
Secure network architectureLinux hardeningWindows hardeningAD hardeningNetwork device hardeningCloud security introAuditing & monitoring
Software Security
OWASP API Top 10SSDLCSASTDASTDevSecOps intro
Incident Management
IR & IM introLogging for accountabilityBecoming a first responderCyber crisis management
🎯 Goal: Design secure systems, manage risk, and lead incident response
Career Roles
Security engineerSecurity analystPenetration testerSOC analystEthical hacker
AI is the new attack surface. Learn to break it, defend it, and secure the full AI lifecycle — from models to data pipelines.
Phase 1 AI Fundamentals Understand how AI works ▶
AI/ML security threatsAI models & dataPrompt engineeringAI forensicsContAInment strategies
🎯 Goal: Understand how AI systems function and where they can be exploited
Phase 2 Securing AI Systems Protect real AI environments ▶
Securing AI systemsLLM security risksAI threat modellingAI system reconnaissanceRisk assessment & mitigation
🎯 Goal: Identify weaknesses and design more secure AI architectures
Phase 3 Prompt Security (LLM Attacks) Attack & defend LLMs ▶
Prompt injection attacksJailbreaking techniquesPrompt defence strategiesLLMborghini (lab)White Rabbit (lab)
🎯 Goal: Understand how attackers manipulate AI behavior — and how to stop them
Phase 4 AI Supply Chain Security Secure the full AI pipeline ▶
AI supply chain overviewSupply chain attack vectorsSecuring AI pipelinesModel & dependency risksPayload (lab)Checkpoint (lab)
🎯 Goal: Protect the full lifecycle — data, models, dependencies, and deployment
Phase 5 Data Poisoning & AI Attacks Protect AI training data ▶
RAG security fundamentalsData poisoning in RAGSensitive data leakageHidden/unindexed data exposureUnIndexed (lab)Lockdown (lab)
🎯 Goal: Defend against data manipulation attacks targeting AI systems
Career Roles
AI security engineerLLM security specialistAI red teamerML security analyst
Cloud security is one of the most in-demand skills today. Learn to attack and defend the world’s most widely used cloud platform.
Phase 1 AWS Fundamentals How cloud systems work ▶
AWS Cloud 101AWS basic conceptsShared responsibility modelCloud services overview
🎯 Goal: Understand how AWS and cloud computing work at a fundamental level
Phase 2 Identity & Access Management (IAM) Control who accesses what ▶
IAM introIAM principalsIAM permissions & policiesIAM credentialsLeast privilege principleSTS credentials lab
🎯 Goal: Master access control — the #1 source of cloud misconfigurations
Phase 3 Core Services — Attack & Defense Secure S3, EC2, VPC ▶
AWS S3 attack & defenseAmazon EC2 attack & defenseAWS VPC securityVPC data exfiltrationCloud misconfigurations
🎯 Goal: Understand real-world attack techniques on core AWS services — and how to stop them
Phase 4 Serverless Security Lambda, APIs & beyond ▶
AWS Lambda securityLambda data exfiltrationAWS API GatewayLambda authorizer abuse
🎯 Goal: Secure serverless infrastructure, which is often overlooked by defenders
Phase 5 IAM Privilege Escalation Advanced attack techniques ▶
IAM enumerationIAM initial accessPrivilege escalation techniquesCloud monitoring & logging
🎯 Goal: Think like an attacker to build stronger cloud defenses
Career Roles
Cloud security engineerAWS security specialistCloud SOC analystDevSecOps cloud engineer
Integrate security into every stage of software development — from writing code to deploying containers and managing infrastructure.
Phase 1 Secure Software Development Security from day one ▶
DevSecOps introSDLC overviewSecure SDLC (SSDLC)Secure coding practicesShift-left security
🎯 Goal: Understand how secure software development differs from standard dev practice
Phase 2 Security of the Pipeline Protect code delivery ▶
Pipeline automation introSource code securityCI/CD securityBuild securityCredential hygiene
🎯 Goal: Secure the code’s entire journey from commit to deployment
Phase 3 Security in the Pipeline Find & fix vulnerabilities early ▶
Dependency managementStatic analysis (SAST)Dynamic analysis (DAST)Code security analysisMother’s Secret (lab)
🎯 Goal: Catch vulnerabilities before they ever reach production
Phase 4 Container Security Docker & Kubernetes ▶
Intro to containerisationDocker basicsKubernetes introContainer vulnerabilitiesContainer hardening
🎯 Goal: Secure containerized applications from image to runtime
Phase 5 Infrastructure as Code (IaC) Secure cloud infrastructure ▶
IaC fundamentalsOn-premises IaC securityCloud-based IaCSecure deployment practices
🎯 Goal: Treat infrastructure like code — and secure it the same way
Career Roles
DevSecOps engineerSecurity automation engineerApplication security engineer

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