Cloud environments generate thousands of security-relevant events per hour across AWS, Azure, and GCP, each with its own logging format, terminology, and coverage gaps. This guide breaks down exactly what to monitor in each provider — CloudTrail, Activity Logs, Cloud Audit Logs — and explains why unified, AI-driven cross-cloud correlation catches coordinated attacks that single-provider tools miss entirely.
Cloud environments generate thousands of security-relevant events every hour. Knowing which ones matter — and monitoring them effectively across AWS, Azure, and GCP simultaneously — is one of the hardest challenges in modern security operations.
Cloud security monitoring started as a single-provider problem: organizations picked one of AWS, Azure, or GCP, and tools like AWS CloudTrail (introduced in 2013) were built to answer "who did what" within that one environment. As companies began running multi-cloud or hybrid-cloud architectures — often the result of acquisitions, regional compliance requirements, or simply choosing best-of-breed services from different providers — security teams found that each cloud's native tools could only ever see their own slice of the environment. That gap is what pushed the industry toward cloud-agnostic SIEM and, more recently, AI-native platforms that normalize and correlate signals across providers instead of requiring analysts to mentally reconcile three separate consoles and log schemas.
Each major cloud provider has its own audit logging format, terminology, and coverage gaps. AWS uses CloudTrail for API activity, Azure uses Activity Logs, and GCP uses Cloud Audit Logs — all with different schemas, different retention policies, and different alert mechanisms. Building unified detection across all three is a significant engineering challenge.
| Provider | Primary Log Source | Native Threat Detection | Highest-Signal Events |
|---|---|---|---|
| AWS | CloudTrail | GuardDuty | IAM changes, S3 policy edits, root account use |
| Azure | Activity Log | Microsoft Defender for Cloud | RBAC changes, conditional access edits, guest grants |
| GCP | Cloud Audit Logs | Security Command Center | IAM changes, service account key creation |
The most effective approach is using an AI SOC platform that natively ingests all three providers' logs into a unified data model — enabling cross-cloud correlation that individual cloud-native tools can't provide. When an IAM user is created in AWS, a new admin account in Azure AD, and an unusual GCP service account key is generated in the same 30-minute window, that's a coordinated attack pattern that only cross-cloud correlation surfaces. This kind of identity-plus-cloud correlation is the same approach used in identity threat detection, and it's especially effective against the early stages of supply chain compromise, where attackers pivot across cloud accounts using stolen service credentials.
Case study scenario: A 150-person logistics company runs production workloads split across AWS and Azure, with a separate analytics pipeline on GCP. An attacker compromises a developer's Azure AD credentials via a phishing kit, then uses the resulting session to create a new guest account with contributor-level RBAC permissions. Eighteen minutes later, CloudTrail logs in the company's AWS account show that same developer's federated role assuming access to a production S3 bucket it had never touched in its 12-month history, followed by a new GCP service account key generated in the analytics project. Viewed in isolation, each event scores as low-to-medium risk in its native console; correlated across all three providers within a single 30-minute window, the pattern is flagged as a coordinated cross-cloud compromise and triggers automatic session revocation.
Book a 30-minute demo and see AI-powered threat detection live in your real environment.