Detecting multi-stage attacks on AWS: A guide to cross-service signal correlation

A single alert from one security service tells you something happened. Read that signal alongside activity from other services and your own business context, and you will know whether what happened is part of a multi-stage attack.

Consider a short sequence. An identity calls GetCallerIdentity from a source address it hasn’t previously used. Within minutes, that same identity runs a burst of List and Describe calls across several services, and some of them fail with AccessDenied. Soon after, a large volume of data leaves your environment toward a domain that was registered last week. Amazon GuardDuty might already flag pieces of this, such as the reconnaissance from an unfamiliar source, through finding types like Recon:IAMUser/* or Discovery:S3/*. What you gain from correlating the pieces yourself is a single view of the sequence, tied to your own business context, so you can act on the whole rather than triaging findings one at a time.

This post is for security engineers and security operations teams who run Amazon Web Services (AWS) detection services and want to catch patterns specific to their environment. You will see how AWS detection and your business context fit together, and how to build correlations that use that context. The examples run in Amazon CloudWatch Logs Insights so you can try them today, and the closing section describes how to grow them into an automated pipeline. The walkthrough later in this post lists the prerequisites for these queries.

Begin with the AWS detection services. They cover the threats common across customers, and everything in this post is built on them.

• Amazon GuardDuty analyzes activity across AWS CloudTrail, Amazon Virtual Private Cloud (Amazon VPC) Flow Logs, DNS query logs, Amazon Simple Storage Service (Amazon S3) data events, and more and it produces high confidence findings out of the box.
• Amazon Detective (a security investigation service that visualizes relationships between resources and findings) lets you investigate findings with graph-based context across services and accounts.
• AWS Security Hub aggregates security findings into a single prioritized dashboard.
• Amazon Security Lake centralizes security data into a purpose-built data lake, stored in the Open Cybersecurity Schema Framework (OCSF) format for long-term analysis.
• Amazon Inspector (a vulnerability management service that scans workloads for software vulnerabilities) correlates vulnerability data with workload context.

Turn these on and tune them before you build anything custom. Tuning means adjusting sensitivity to reduce false positives for your environment, choosing which data sources each service monitors, and suppressing findings for known-good patterns.

Before you build anything by hand, see what GuardDuty already does for you. Amazon GuardDuty Extended Threat Detection correlates signals across multiple data sources including AWS CloudTrail, Amazon S3 data events, runtime monitoring, Amazon Elastic Kubernetes Service (Amazon EKS) audit logs, and more, then raises a single critical severity attack sequence finding when it spots a multi-stage pattern. It recognizes sequences such as credential compromise followed by data exfiltration, maps them to MITRE ATT&CK tactics, and attaches a timeline and remediation guidance. If you have GuardDuty enabled today, then GuardDuty Extended Threat Detection is already enabled by default and needs no queries from you. For details on how GuardDuty charges apply, see Amazon GuardDuty pricing.

The credential compromise sequence in the opening example is the kind of universal pattern GuardDuty Extended Threat Detection is built to catch, so rely on it for those. Attack sequence findings show up in the GuardDuty console next to your other findings, and they route to Security Hub and your response workflows the same way.

GuardDuty handles the threats that look the same in every account. What it doesn’t have is the context that makes a given action suspicious in your account. That’s what you provide.

Business context is what only you know about your environment: which buckets hold sensitive data, which principals have a reason to touch which resources, which role chains your policy permits, and when your production change windows open. GuardDuty Extended Threat Detection learns from patterns common across customers, but it can’t answer these environment-specific questions. Express them as correlations and you add a detection layer tuned to your environment. Each of the following four patterns turns one of these facts into a query.

Run these queries in the AWS Management Console for CloudWatch by choosing Logs, then Logs Insights, using the CloudWatch Logs Insights query language. Most read CloudTrail events from a CloudWatch Logs log group that your trail delivers to. If your trail writes only to Amazon S3, add CloudWatch Logs delivery on the trail, or run equivalent queries in Amazon Athena (a serverless query service for analyzing data in Amazon S3 using SQL).

Note: The queries and code in this post use placeholder values. Replace them with your own before running: your-sensitive-bucket (your S3 bucket name), your-key-id (your AWS KMS key ID), region (your AWS Region, such as us-east-1), account-id (your 12-digit AWS account ID), and aws-cloudtrail-logs-my-trail (your CloudTrail log group name).

A note on multi-account environments. In AWS Organizations, an

Key Takeaways

• Correlating GetCallerIdentity anomalies with burst of API calls and data exfiltration reveals multi-stage attacks.
• Unexpected source addresses followed by failed access attempts signal an attacker probing for vulnerabilities.
• Security teams must cross-reference identity, API, and network logs to detect advanced persistent threats.