AWS Config monitors that your resources comply with the rules (subchapter 23.2). But there is another dimension of security: detecting ongoing attacks and malicious behaviors. What if someone steals credentials? What if a server is being used to mine cryptocurrencies without your knowledge? To detect these active threats there is GuardDuty, AWS's intelligent threat detection system.
The problem: attacks don't announce themselves
Compliance rules (Config) detect misconfigurations, but they don't detect an attacker in action. An attacker who has obtained valid credentials can, technically, be "complying with the rules" while stealing data. You need something that detects suspicious behaviors, patterns that reveal an attack:
- Someone accessing from a country where you never operate, at 4 a.m.
- A server communicating with addresses known to be malicious.
- Massive access attempts or attempts to discover your infrastructure.
- A resource behaving abnormally (suddenly mining cryptocurrencies).
What is GuardDuty
GuardDuty is a threat detection service that continuously analyzes the activity of your AWS account for malicious or suspicious behaviors. It uses intelligence (machine learning, known threat lists, pattern analysis) to detect what a human could not monitor.
Your AWS account activity (logs, network, accesses...)
│
▼
GuardDuty (analyzes with AI and threat lists)
│
├─ normal activity → nothing to report
└─ suspicious activity → generates a "finding" with alertAnalogy: GuardDuty is like an intelligent alarm system with cameras for your house. It doesn't just detect an open door (that would be Config); it detects suspicious behaviors: someone lurking at night, a forced window, movement where there shouldn't be any. And it learns what is "normal" in your house to distinguish the anomalous. When it sees something strange, it alerts you.
What GuardDuty analyzes
GuardDuty examines several sources of information from your account, without you having to install anything on your servers (this is called "agentless"):
- API activity logs (CloudTrail): who does what in your account.
- Network traffic (VPC Flow Logs): who your resources communicate with.
- DNS queries: which domains your resources access (reveals connections to malicious sites).
- And more sources, depending on the protections you enable.
From there, it cross-references that information with threat intelligence (lists of known malicious IPs and domains) and with behavioral models that learn what is normal in your account.
Types of threats it detects
GuardDuty generates findings when it detects something suspicious. Typical examples:
| Finding | What it means |
|---|---|
| Compromised credentials | A key used from an unusual location or in an unusual way (possible theft) |
| Communication with malicious IP | A resource "talks" to an address known for malicious activity |
| Reconnaissance | Someone exploring your infrastructure looking for vulnerabilities |
| Cryptocurrency mining | A server mining crypto (typical sign it was compromised) |
| Data exfiltration | Patterns suggesting data is being stolen |
| Anomalous access | Logins from unusual places or at unusual times |
Each finding comes with a severity level (low, medium, high) so you can prioritize.
A big advantage: enabled with one click, no changes needed
What's remarkable about GuardDuty is how easy it is: you enable it and it starts monitoring automatically, without installing agents on your servers, without configuring complex rules, and without affecting the performance of your resources (it analyzes logs and metadata, does not interfere with your application). AWS takes care of all the intelligence.
Enable GuardDuty = one click (or a few lines of Terraform) → starts analyzing and detecting threats immediately → no agents, no complex configuration, no performance impact
What to do with the findings
Detection is the first step; then you have to react. GuardDuty findings can be connected with other services for an automatic response:
GuardDuty detects threat
→ EventBridge (subcap. 15.3) receives the finding
→ triggers a Lambda (Cap. 14) that reacts automatically:
- isolates the compromised resource
- revokes the suspicious credentials
- alerts the security team (Slack, email)This enables automatic incident response: for example, if GuardDuty detects a compromised server mining crypto, a Lambda can isolate it from the network instantly, containing the attack before a human even reads the alert.
Real-world example: a startup enables GuardDuty in its account. One night, an attacker obtains an access key leaked by mistake in a public repository. They try to use it to launch dozens of expensive servers (to mine crypto). GuardDuty detects the anomalous pattern—"these credentials had never launched so many servers, and from an unknown IP"—and generates a high severity finding. An automation revokes the key and alerts the team. The attack is contained in minutes, avoiding a huge bill and a security breach.
GuardDuty in defense in depth
GuardDuty adds the layer of active threat detection to your security. Remember the layers we've covered:
SCP (Cap. 23.1) → maximum limits for the organization IAM (Cap. 7) → who can do what WAF (Cap. 16.4) → filters web attacks Config (Cap. 23.2) → monitors rule compliance GuardDuty (this) → DETECTS attacks and malicious behaviors
Each covers something different. GuardDuty is the "eyes" watching if someone is attacking right now.
What you should remember
- Compliance rules (Config) detect misconfigurations, but not an attacker in action; that's what threat detection is for.
- GuardDuty continuously analyzes the activity of your account (accesses, network traffic, DNS) with intelligence (machine learning + threat lists) to detect malicious or suspicious behaviors. Like an intelligent alarm with cameras that learns what is normal.
- It detects threats like compromised credentials, communication with malicious IPs, reconnaissance, crypto mining, and data exfiltration, generating findings with severity level.
- It is enabled with one click, no agents, no complex configuration, and no impact on performance: AWS provides all the intelligence.
- Findings are connected with EventBridge + Lambda for automatic incident response (isolate a resource, revoke credentials, alert).
- It adds the layer of active attack detection to defense in depth.
In the next subchapter, we will see how to have a centralized view of all your security with Security Hub.
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
