We already have an organization with many accounts (subchapter 30.1), created and governed with Control Tower (subchapter 30.2). But this multi-account structure creates a new challenge: if you have security and logs spread across dozens of accounts, how do you monitor and manage them together? Reviewing account by account would be impossible. The solution is to centralize security and logs: bring everything together in a common place to view and control it globally. In this subchapter, we’ll see why and how.
The problem: security and logs scattered across many accounts
Remember all the security and observability we covered (Chapters 23 and 24): activity logs, threat findings, audit records... If each of your 50 accounts generates its own security and logs separately, monitoring everything becomes unfeasible:
50 accounts, each with ITS own logs and ITS own security separately: ❌ Review the logs of 50 accounts one by one? Impossible ❌ Detect an attack that touches several accounts? You wouldn’t see it ❌ Prove to an auditor the global state? Very difficult ❌ If someone deletes the logs from their account, who finds out?
You need a central view and management of the security and logs of the entire organization, not fragmented account by account.
The solution: centralize (specialized accounts)
The recommended practice is to centralize security and logs in dedicated accounts. It’s very common to have:
- A log account (log archive): where logs from all accounts are gathered, stored securely.
- A security account (security/audit): from where the security team monitors and manages the security of the entire organization.
Account A ─┐
Account B ─┼──► LOG ACCOUNT (all logs together, secure)
Account C ─┘ │
└──► SECURITY ACCOUNT (the security team
monitors the ENTIRE organization from here)Remember that Control Tower (subchapter 30.2) usually creates these specialized accounts automatically as part of the landing zone.
Analogy: centralizing security and logs is like having a security headquarters and a central archive for an entire chain of stores. Instead of each store keeping its own camera recordings and having its own isolated guard (with no one seeing the whole picture), all cameras send their feed to a single security center, and all recordings are stored in a protected central archive. This way, a central team monitors all stores at once, detects patterns that span several, and the recordings are safe even if someone tampers with a specific store.
Why centralize logs
Gathering logs from all accounts in a dedicated log account provides:
- Complete visibility
You have all records in one place, allowing you to analyze the activity of the entire organization together (for example, with the observability tools from Chapter 24, or a log data lake—see Chapter 29).
- Log protection (tamper-proof)
If the logs from each account are stored outside that account (in the central log account, which regular teams can’t delete from), no one can tamper with or delete the records from their own account to hide something. This is key for auditing and security: the logs are reliable and intact.
Logs stored in the central account (not in the source account) → even if someone compromises an account, they CANNOT delete its logs → the records remain safe as evidence
- Compliance and auditing
Having all records centralized and protected makes it much easier to demonstrate compliance to auditors and regulators (see Chapter 23): there’s a single, secure place with the entire trail.
Why centralize security
Managing security from a central security account allows:
- Global monitoring
The security team sees and manages the security of all accounts from one place. Remember Security Hub (subchapter 23.4) and GuardDuty (subchapter 23.3): they can be configured to aggregate findings from the entire organization in the security account, providing that central view we saw as so valuable.
GuardDuty and Security Hub from ALL accounts → aggregated in the central security account → the team sees threats to the entire organization in one place
- Detection of threats that cross accounts
Some attacks touch multiple accounts. Only by seeing them together (from the central account) can you detect those patterns that, account by account, would go unnoticed.
- Coordinated response
In the event of an incident, the central security team can coordinate the response across affected accounts, instead of acting blindly in each one.
The key idea: central governance, distributed operation
The resulting model is very powerful: each team operates autonomously in its account (freedom to work), but security and logs are governed centrally (control and global visibility). The best of both worlds: autonomy for teams and control for the organization.
Teams: autonomous in their accounts (distributed operation) Security and logs: centralized (central governance) → freedom + control at the same time
Real-world example: a company with 40 accounts centralizes its security and logs. All records from the 40 accounts are sent to a protected log account, which product teams cannot delete from. All threat detection (GuardDuty, Security Hub) from the 40 accounts is aggregated in the security account, where the security team monitors everything. One day, an attacker compromises a team’s credentials and tries to move to other accounts. Because security is centralized, the team detects the pattern (suspicious activity crossing accounts) that would have gone unnoticed in isolated accounts, and responds in a coordinated way. Also, the attacker cannot delete the logs from the compromised account (they’re in the central account), so the entire trail remains for investigation. Centralization was decisive.
What you should remember
- With many accounts, having security and logs scattered in each one makes it impossible to monitor them together, detect attacks that cross accounts, or demonstrate global compliance.
- The solution is to centralize in dedicated accounts: a log account (gathers logs from all accounts) and a security account (from which the whole organization is monitored). Control Tower creates them (subchapter 30.2). Like a security headquarters and central archive for a chain of stores.
- Centralizing logs provides: complete visibility, tamper-proof protection (no one deletes logs from their own account, they remain safe as evidence), and easier compliance.
- Centralizing security provides: global monitoring (GuardDuty/Security Hub aggregated, Ch. 23), detection of threats that cross accounts, and coordinated response.
- The resulting model: central governance (security and logs) + distributed operation (autonomous teams in their accounts) = freedom and control at the same time.
In the last subchapter of the chapter, we’ll see how to manage this entire multi-account structure with Terraform, that is, Terraform at multi-account scale.
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
