We arrive at one of the most important services in the entire cloud: IAM. It controls who can do what in your AWS account. If the VPC was your network security, IAM is the security of your identities and permissions. Mastering its four basic concepts—users, groups, roles, and policies—is essential for working securely with AWS.
What is IAM
IAM stands for Identity and Access Management. It is the service that manages:
- Who can access your AWS account (identities).
- What each one can do (permissions).
Two important facts to start with:
- IAM is free.
- IAM is global: it does not belong to a specific region; your users and permissions are valid throughout your account.
General analogy: IAM is the security system and access cards of an office building. It decides who has a card, which floors each person can enter, and which doors they can open. Without properly configured IAM, either no one can work, or anyone can go anywhere (dangerous).
The Four Key Concepts
- Users: an identity for a person or app
An IAM user represents a specific person (or sometimes an application) that needs to access AWS. It has its own credentials:
- Username and password to log in to the web console.
- Access keys to use the command line or program against AWS.
Analogy: An IAM user is like an employee's ID card. It is individual and non-transferable: each employee has their own.
Best practice: each person should have their own user. Never share a user among several people, because then you wouldn't know who did what (traceability is lost).
- Groups: organize users by function
A group is a set of users who share the same permissions. Instead of giving permissions one by one to each person, you give them to the group and add people to it.
Analogy: A group is like a department. Instead of giving keys to each person in the accounting department separately, you decide "everyone in accounting can enter the finance room" and then add or remove people from the department.
Example: You create a
Developersgroup with permissions to manage test servers. When a new developer joins, you add them to the group and they inherit all permissions automatically. When they leave, you remove them and they lose access. Much easier than managing permissions person by person.
Groups do not have their own credentials; they only group users. And a user can be in several groups at once.
- Roles: temporary permissions that are "assumed"
A role is an identity with permissions, but without fixed credentials: it does not belong to a specific person, but is assumed temporarily when needed. It can be assumed by people but, above all, by services and applications.
Analogy: A role is like an "authorized visitor" vest or a uniform that you put on temporarily to do a task. While you wear it, you have certain permissions; when you finish, you take it off. It is not yours: you use it while you need it.
This is probably the concept that is hardest at first, but it is the most important for security. Its typical use:
Key example: You have an EC2 instance that needs to read files from an S3 bucket. How do you give it permission? Bad idea: put access keys inside the server (if someone compromises it, they steal the keys). Good idea: assign a role to the EC2 instance. The instance "assumes" the role and obtains temporary permissions to read S3, without any key stored anywhere. AWS rotates those credentials automatically.
Roles are the foundation of modern security in AWS. We will see them in more detail in subchapter 7.4 (temporary credentials) and they are featured in many later chapters.
- Policies: the document that defines permissions
A policy is a document (in JSON format) that defines exactly what is allowed or denied. Policies are what actually contain the permissions; users, groups, and roles simply have policies attached.
Analogy: A policy is the detailed list of permissions written on paper: "can open doors A and B, but not C; can read archive documents, but not modify them."
A policy answers four questions:
- Effect: allow or deny?
- Action: what action? (read a bucket, launch an instance…)
- Resource: on which resource?
- (Optional) Condition: under what conditions?
We will see policies in detail in subchapter 7.3.
How the Four Fit Together
┌─────────────┐
│ POLICY │ ← defines the permissions (the "what can be done")
└──────┬──────┘
│ is attached to...
┌───────────┼───────────┐
▼ ▼ ▼
USER GROUP ROLE
(person) (set of (assumed
users) temporarily:
services, apps)- Policies contain the permissions.
- They are attached to users (people), groups (sets of users), or roles (temporary identities).
- Groups make it easier to manage permissions for many people at once.
- Roles give temporary permissions without fixed credentials (ideal for services).
A Warning: the Root User
When you create an AWS account, you get the root user (the email you registered with). This user has absolute power over everything, including billing. It is so powerful that it is dangerous to use it daily.
Golden rule: Do not use the root user except for the very few tasks that require it. Create a normal IAM user (with administrator permissions if needed) for your daily work, and protect the root with MFA (subchapter 7.4). We will see this more in subchapter 7.5.
What You Should Remember
- IAM controls who can do what in your account. It is free and global.
- User: an identity for a person or app (with its credentials). One per person, never shared.
- Group: set of users who share permissions. Makes management easier.
- Role: identity with temporary permissions that is assumed (ideal for services like EC2, avoids storing keys).
- Policy: JSON document that defines permissions and is attached to users, groups, or roles.
- Do not use the root user for daily work.
In the next subchapter we will see the most important principle in all of cloud security: least privilege.
Cloud, AWS & Terraform — From Zero to Expert
Chapter 1 · What is cloud computing
- 1.1 The traditional client-server model
- 1.2 Problems the cloud came to solve
- 1.3 On-premise vs cloud vs hybrid
- 1.4 The three service models: IaaS, PaaS, SaaS
- 1.5 The five pillars of cloud (according to NIST)
- 1.6 Real advantages: elasticity, pay-as-you-go, global availability
Chapter 2 · The cloud market and major providers
- 2.1 AWS, Azure and GCP: differences and market share
- 2.2 Why learn AWS first
- 2.3 Concepts that are universal among providers
Chapter 3 · Regions, availability zones and edge
- 3.1 What is an AWS region and how to choose it
- 3.2 Availability Zones: high availability by design
- 3.3 Edge locations and CloudFront
- 3.4 Latency, resilience and data sovereignty
Chapter 4 · Compute: EC2
- 4.1 Instances: types, families and when to choose each
- 4.2 AMIs, key pairs and Security Groups
- 4.3 Instance lifecycle
- 4.4 Elastic IPs and Placement Groups
- 4.5 Savings Plans vs Reserved vs On-Demand vs Spot
Chapter 5 · Storage: S3
- 5.1 Buckets, objects and keys
- 5.2 Storage classes (Standard, IA, Glacier…)
- 5.3 Versioning and object lifecycle
- 5.4 Bucket policies and ACLs
- 5.5 Static website hosting
Chapter 6 · Networking: VPC
- 6.1 What is a VPC and why you need it
- 6.2 Public and private subnets
- 6.3 Internet Gateway and NAT Gateway
- 6.4 Route Tables and Network ACLs
- 6.5 VPC Peering and endpoints
Chapter 7 · Identity and access: IAM
- 7.1 Users, groups, roles and policies
- 7.2 The principle of least privilege
- 7.3 Identity-based vs resource-based policies
- 7.4 MFA and temporary credentials (STS)
- 7.5 IAM security best practices
Chapter 8 · Managed databases
- 8.1 RDS: engines, Multi-AZ and read replicas
- 8.2 Aurora and its advantages over vanilla RDS
- 8.3 DynamoDB: key-value / document model
- 8.4 ElastiCache for in-memory cache
- 8.5 When to use each type of database
Chapter 9 · Why Infrastructure as Code
- 9.1 Problems with manual provisioning
- 9.2 Declarative vs imperative IaC
- 9.3 Terraform vs CloudFormation vs Pulumi vs CDK
- 9.4 The plan → apply → destroy cycle
Chapter 10 · HCL: the Terraform language
- 10.1 Resource, variable, output, locals blocks
- 10.2 Data types: string, number, bool, list, map, object
- 10.3 Expressions, references and built-in functions
- 10.4 Conditionals and loops (count, for_each, for)
Chapter 11 · Providers and state
- 11.1 How the AWS provider works
- 11.2 The terraform.tfstate file and its importance
- 11.3 Local state vs remote state (S3 + DynamoDB)
- 11.4 Essential commands: init, plan, apply, destroy, fmt, validate
Chapter 12 · Your first real infrastructure in Terraform
- 12.1 Create a VPC with subnets from scratch
- 12.2 Launch a public EC2 instance
- 12.3 Associate a Security Group and an Elastic IP
- 12.4 Outputs and references between resources
- 12.5 Team workflow: PR review of plans
Chapter 13 · Load balancing and auto scaling
- 13.1 Application Load Balancer vs Network Load Balancer
- 13.2 Target Groups, listeners and rules
- 13.3 Auto Scaling Groups: policies and metrics
- 13.4 Warm pools and lifecycle hooks
Chapter 14 · Serverless with Lambda
- 14.1 The Lambda execution model
- 14.2 Triggers: API Gateway, S3, DynamoDB Streams, SQS
- 14.3 Dependency management and layers
- 14.4 Cold starts and strategies to reduce them
- 14.5 Limits and anti-patterns
Chapter 15 · Messaging and events
- 15.1 SQS: standard vs FIFO queues, DLQ
- 15.2 SNS: topics, subscriptions, fan-out
- 15.3 EventBridge: event buses and rules
- 15.4 Patterns: pub/sub, decoupling, saga
Chapter 16 · Content delivery and DNS
- 16.1 Route 53: record types and routing policies
- 16.2 CloudFront: distributions, caches and origins
- 16.3 ACM: free SSL/TLS certificates
- 16.4 WAF integrated with CloudFront
Chapter 17 · Containers on AWS
- 17.1 Docker: quick review of key concepts
- 17.2 ECR: private image registry
- 17.3 ECS: task definitions, services, Fargate vs EC2
- 17.4 EKS: when Kubernetes and when not
Chapter 18 · Modules: reuse and composition
- 18.1 Anatomy of a Terraform module
- 18.2 Input variables, outputs and dependencies
- 18.3 Local modules vs Terraform Registry modules
- 18.4 Module versioning with Git tags
- 18.5 Design of generic vs domain-specific modules
Chapter 19 · Workspaces and environment management
- 19.1 Terraform workspaces: use cases and limitations
- 19.2 Directory strategy per environment (dev/stg/prod)
- 19.3 Terragrunt: DRY for environment configurations
- 19.4 Environment variables and .tfvars files
Chapter 20 · Remote backends and locking
- 20.1 Configure S3 + DynamoDB as backend
- 20.2 State locking: avoiding team corruption
- 20.3 State migration between backends
- 20.4 terraform import: bring existing resources into state
Chapter 21 · Infrastructure testing
- 21.1 Terraform validate and fmt in CI
- 21.2 Checkov and tfsec: static security analysis
- 21.3 Terratest: integration tests in Go
- 21.4 Contract testing between modules
Chapter 22 · Terraform in CI/CD
- 22.1 Basic pipeline: lint → plan → apply in GitHub Actions
- 22.2 Atlantis: GitOps for Terraform
- 22.3 Terraform Cloud / HCP Terraform
- 22.4 Drift detection and automatic reconciliation
Chapter 23 · Defense in depth
- 23.1 AWS Organizations and Service Control Policies
- 23.2 AWS Config: continuous compliance
- 23.3 GuardDuty: threat detection
- 23.4 Security Hub: centralized view
- 23.5 KMS: key management and rotation
- 23.6 Secrets Manager vs Parameter Store
Chapter 24 · Observability: logs, metrics and traces
- 24.1 CloudWatch Logs, metrics and alarms
- 24.2 CloudWatch Dashboards and Contributor Insights
- 24.3 X-Ray: distributed tracing
- 24.4 OpenTelemetry on AWS
- 24.5 Managed Grafana and Managed Prometheus
Chapter 25 · Cost optimization
- 25.1 AWS Cost Explorer and budgets with alerts
- 25.2 Trusted Advisor and Compute Optimizer
- 25.3 Rightsizing: how to detect overprovisioning
- 25.4 Savings Plans vs Reserved Instances: strategic decision
- 25.5 FinOps: culture and processes to control spending
Chapter 26 · High availability and disaster recovery
- 26.1 RTO and RPO: defining objectives
- 26.2 Strategies: backup/restore, pilot light, warm standby, multi-site
- 26.3 Route 53 health checks and automatic failover
- 26.4 AWS Backup: centralized backup policy
Chapter 27 · AWS Well-Architected Framework
- 27.1 The six pillars: operational excellence, security, reliability, performance efficiency, cost optimization, sustainability
- 27.2 Well-Architected Tool: formal reviews
- 27.3 How to apply the framework in design decisions
Chapter 28 · Serverless architectures at scale
- 28.1 Event-driven architecture with Lambda + EventBridge
- 28.2 Saga pattern for distributed transactions
- 28.3 Step Functions: orchestration of complex workflows
- 28.4 Lambda@Edge and CloudFront Functions
Chapter 29 · Data platforms on AWS
- 29.1 Data Lake with S3, Glue and Athena
- 29.2 Kinesis Data Streams and Firehose for streaming
- 29.3 Redshift: data warehousing at scale
- 29.4 Lake Formation: data governance
Chapter 30 · Multi-account and landing zones
- 30.1 Why separate workloads into different accounts
- 30.2 AWS Control Tower and Account Factory
- 30.3 Centralized log and security management
- 30.4 Terraform at multi-account scale with shared modules
Chapter 31 · Platform Engineering and Internal Developer Platform
- 31.1 Golden paths and abstractions over Terraform
- 31.2 AWS Service Catalog
- 31.3 Backstage as a developer portal
- 31.4 Terraform modules as internal product
Chapter 32 · Relevant AWS certifications
- 32.1 Cloud Practitioner: is it worth it?
- 32.2 Solutions Architect Associate → Professional
- 32.3 DevOps Engineer Professional
- 32.4 Specialty: Security, Database, Networking
- 32.5 HashiCorp Terraform Associate
Chapter 33 · Projects to consolidate what you've learned
- 33.1 Project 1: serverless blog (S3 + CloudFront + Lambda + DynamoDB)
- 33.2 Project 2: REST API with ECS Fargate + RDS + ALB
- 33.3 Project 3: data platform with Glue + Athena + Redshift
- 33.4 Project 4: multi-account landing zone with Terraform and Control Tower
