If you take away one idea from the entire security chapter, let it be this: the principle of least privilege. It is the golden rule of cloud security (and all of computing). Understanding and applying it will save you from serious incidents and is what distinguishes a professional from a careless beginner.
What is Least Privilege
The principle of least privilege says:
Give each user, service, or application ONLY the permissions they need to do their job, and not one more.
No extra permissions “for convenience,” nor more “just in case.” Exactly what’s needed, nothing extra.
Analogy: Imagine a hotel. You give the cleaning staff a card that opens the rooms, but not the safe or the manager’s office. The cook gets access to the kitchen, but not to the rooms. Each person has access only to what they need for their job. You don’t give everyone a master key “to simplify,” because that would be a huge risk.
Why It’s So Important
The reason is simple: reduce the damage when something goes wrong. And something, sooner or later, will go wrong.
Imagine two scenarios with a user whose credentials are stolen by an attacker:
| User with minimum permissions | User with administrator permissions | |
|---|---|---|
| What they could do | Only read a specific bucket | Everything in the account |
| Damage if stolen | Minimal: the attacker only reads that bucket | Catastrophic: deletes everything, steals data, mines cryptocurrency at your expense |
Least privilege limits the blast radius. If a credential is compromised (and it happens more than you think), the attacker can only do the little that identity could do.
Real example (common pattern): A developer accidentally uploads their access keys to a public GitHub repository. Automated bots detect them in seconds and use them. If those keys had administrator permissions, the attacker launches dozens of expensive servers to mine cryptocurrency and the victim receives a bill for thousands of euros. If the keys had minimum permissions (only read a bucket), the attacker can do almost nothing. Least privilege turns a disaster into a scare.
How to Apply It in Practice
Applying least privilege is a mindset more than a button. Here are the guidelines:
- Start by denying everything
In AWS, by default, everything is denied until you explicitly allow it. Take advantage of this: start from zero and add only the permissions that are proven necessary, instead of giving a lot and then taking away.
- Be specific with resources
Don’t give permission over “all buckets” if only one is needed. Don’t give permission over “all actions” if only reading is needed.
Bad (too broad): “Can do anything with any S3 bucket.” Good (minimum): “Can read objects only from the
informes-2026bucket.”
- Avoid administrator permissions
Giving AdministratorAccess (full permission) is convenient but dangerous. Reserve it for the very few identities that truly need it. Most users and services need much less.
- Use roles for services
As we saw in subchapter 7.1, give each service (an EC2 instance, a Lambda function) a role with exactly the permissions it needs, no more.
- Review and adjust over time
Permissions tend to accumulate (“permission creep”). Review them periodically and remove what is no longer used. AWS has tools (IAM Access Analyzer, which we’ll see in subchapter 7.5) that tell you which permissions are granted but never used.
The Balance: Security vs Convenience
Let’s be honest: least privilege takes more work than giving broad permissions. It’s tempting to just hand out an AdministratorAccess and forget about it. But that shortcut is exactly the source of most serious security incidents.
Correct mindset: a bit of inconvenience now (setting up tight permissions) in exchange for avoiding a disaster later. Professionals accept that small friction as a normal part of a job well done.
A practical trick to find the balance: start with the permissions you think are needed, run the application, and if it fails due to lack of permissions, add exactly the one that’s missing. That way you reach the real minimum without going overboard.
Least Privilege Beyond IAM
This principle is not just for IAM; you’ve already seen it throughout the book:
- Security Groups (Chapter 4): open only the necessary ports.
- Private subnets (Chapter 6): don’t expose to the internet what doesn’t need it.
- S3 bucket policies (Chapter 5): give access only to those who should have it.
It’s a cross-cutting philosophy for all cloud security.
What You Should Remember
- Least privilege: give each identity only the permissions it needs, not one more.
- Its goal is to limit the damage if a credential is compromised (reduce the “blast radius”).
- In practice: start from zero, be specific with actions and resources, avoid administrator permissions, use roles for services, and review periodically.
- It takes a bit more work than giving broad permissions, but that small effort prevents disasters.
- It’s a cross-cutting philosophy: it also applies to networks, Security Groups, and S3 policies.
In the next subchapter, we’ll dive deeper into how permissions are written: identity-based vs resource-based policies.
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
