In the previous subchapter, we saw how to set limits with SCPs. But how do you know, at all times, if your resources comply with the rules your company requires? How do you detect a bucket that became public or an unencrypted database? For that continuous compliance monitoring there is AWS Config. It's like having a permanent auditor reviewing your infrastructure.
The problem: infrastructure changes constantly
In a real AWS account, things change all the time: resources are created, configurations are modified, someone adjusts a permission... With so many changes, it's very easy for something to end up not complying with security rules without anyone noticing:
- An S3 bucket that someone made public "temporarily" and forgot to close (remember the drift from subchapter 22.4).
- A database that was created without encryption.
- A Security Group with a dangerous port open.
- A resource without the company's required tags.
You need something to continuously monitor and alert you when something stops complying with the rules.
What is AWS Config
AWS Config is a service that records the configuration of your resources and monitors that they comply with the rules you define, continuously. It does three fundamental things:
1. RECORDS → saves how each resource is configured, and its history 2. EVALUATES → checks if each resource complies with certain rules 3. ALERTS → notifies when something does NOT comply (is "non-compliant")
Analogy: AWS Config is like a health inspector who lives in your restaurant and continuously checks that everything complies with hygiene rules. He doesn't come once a year: he's always watching, and the moment something is out of order (a fridge left open, a dirty surface), he notes it and alerts you. Plus, he keeps a journal of how everything has been over time.
The three functions in detail
- Configuration recording and history
Config saves how each resource is configured at every moment, and keeps a history of changes. This allows you to answer very valuable questions:
- "How was this Security Group configured last week?"
- "Who changed this configuration and when?"
- "What did my infrastructure look like on the day of the incident?"
This history is pure gold for investigating problems and for audits.
- Compliance rules (Config Rules)
You define rules that your resources must comply with, and Config continuously checks if they do. AWS offers many predefined rules, and you can create your own. Examples:
Rule: "all S3 buckets must have public access blocked" Rule: "all RDS databases must be encrypted" Rule: "no Security Group should have SSH open to 0.0.0.0/0" Rule: "all resources must have the 'project' tag"
Each resource is marked as "compliant" (meets the rule) or "non-compliant" (does not meet the rule). At a glance, you see the compliance status of your entire account.
- Alerts and remediation
When a resource becomes non-compliant, Config can alert (the security team, for example) and even automatically fix it (remediation). For example, if a bucket becomes public, a remediation action could automatically block it again.
Bucket becomes public → Config detects it → marks "NON-COMPLIANT" → alerts the security team → (optional) automatic remediation: blocks it again
Config vs static analysis from Chapter 21
You may wonder how this differs from Checkov/tfsec (subchapter 21.2). The difference is when they act:
| Static analysis (Checkov/tfsec) | AWS Config | |
|---|---|---|
| When | Before deployment (in code, CI) | After, on real resources, continuously |
| What it checks | The Terraform code | The resources that exist in AWS |
| Detects | Dangerous configurations before creating them | Resources that stopped complying (including drift) |
They complement each other: static analysis prevents insecure code from reaching production (prevention), and AWS Config monitors that what is already deployed continues to comply (continuous detection). Remember the drift from subchapter 22.4: Config is one way to detect that something was changed manually and stopped complying with the rules.
Real world example: a financial company has the rule "all databases must be encrypted" (by regulation). They configure a Config Rule to check this. One day, someone manually creates a test database without encryption. Config immediately marks it as 'non-compliant', alerts the security team, and an automatic remediation marks it for review. The non-compliance is detected in minutes, not at the annual audit. The company can demonstrate to regulators that it has continuous compliance monitoring.
Why it matters: continuous compliance
The key idea is continuous compliance. Instead of checking compliance once a year (in a manual, stressful audit that only gives a snapshot), Config checks it all the time, automatically. This is essential for companies with regulatory requirements (banking, healthcare, etc.), but useful for anyone who wants to keep their infrastructure secure and organized.
What you should remember
- In a real account, infrastructure changes constantly, and it's easy for something to end up not complying with security rules without anyone noticing.
- AWS Config monitors compliance continuously: it records resource configuration (with history), evaluates if they comply with rules, and alerts (or remediates) when something is non-compliant. Like an inspector living in your infrastructure.
- The configuration history allows you to investigate incidents and audit ("how was this last week?").
- Compared to static analysis (Chapter 21), which acts before deployment on code, Config acts after, on real resources and continuously (also detects drift). They complement each other.
- It provides continuous compliance: checks compliance all the time, instead of a one-off annual audit. Essential for regulated environments.
In the next subchapter, we will move from monitoring compliance to detecting active threats with GuardDuty.
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
