Professional Cloud DevOps Engineer

Configure and Manage Cloud Audit Logs for Deployment Activities

Cloud Audit Logs record "who did what, where, and when" across Google Cloud resources, giving teams operational visibility into every deployment action. These logs fall into several categories. Admin Activity logs track metadata changes like creating a project or modifying IAM policies; they are always enabled by default. Data Access logs record reads and writes to resource data, such as reading a container image or writing to a bucket, but they must be explicitly enabled because they can generate high volume. System Event logs capture administrative actions that Google systems trigger automatically rather than by a person. Policy Denied logs record access attempts that were blocked by IAM or other policies.

For containerized deployment pipelines, Cloud Audit Logs capture API requests made by orchestrators under the k8s.io service name. Operators can query logs for the k8s_cluster resource type to find rejected pod creation, dry-run validation attempts, or emergency breakglass overrides. This querying makes it possible to tie a deployment failure back to a specific API call.

To meet compliance and retention requirements, teams use sinks to route audit logs to external destinations. Aggregated sinks placed at the folder or organization level collect logs from many projects into a single repository. The sink can send logs to Cloud Storage for long-term, immutable archiving, to BigQuery for advanced SQL analysis, or to Pub/Sub for real-time streaming to third-party security tools. Configuring sinks before log generation begins prevents data loss.

Proactive monitoring relies on Cloud Monitoring alerting policies that warn administrators within minutes when deployment failures occur or log volume thresholds are breached. Access to audit logs is controlled by Identity and Access Management (IAM) roles. The standard Logs Viewer role grants access to basic logs, but reading Data Access logs requires the more restrictive Private Logs Viewer role because those logs may contain sensitive data.

Monitor and Audit Pipeline Execution in Cloud Build and Cloud Deploy

Monitoring and auditing CI/CD pipelines in Cloud Build and Cloud Deploy ensures security, compliance, and operational visibility for every change from code commit to deployment. Cloud Build provides detailed logs for each build step, enabling engineers to examine exactly why a build failed or what performance bottlenecks appeared. Cloud Deploy delivers pipeline metrics such as success and failure rates and rollout durations. Both services integrate with Cloud Logging, where Admin Activity and Data Access audit logs show who initiated each action and which resources were affected.

To maintain a governed path between environments, Cloud Deploy supports defining sequential target environments (such as dev, staging, and production) and enforcing approval gates before a release can promote. Each approval action and pipeline state change is logged, creating a verifiable audit trail of who approved a promotion and when. This prevents unauthorized changes from reaching production.

A critical advanced practice is correlating deployment pipeline logs with Cloud Audit Logs. Because pipeline service accounts perform actions, Cloud Audit Logs show the service account as the actor, not the individual developer or the pipeline run. To build a complete audit trail, operators can add identifiers like a pipeline run ID to API requests using the X-Goog-Request-Reason header or a custom User-Agent string. Conversely, the pipeline system should log the operation IDs returned by Google Cloud APIs. This bidirectional correlation allows tracing any suspicious activity back to the exact code change, commit, and pipeline execution.

Log retention also plays a role in forensic analysis. Different log types in Cloud Logging have different default retention periods, with Data Access logs having a shorter default. Teams should align the retention periods of pipeline logs and audit logs, customizing retention in Cloud Logging or exporting logs to Cloud Storage or BigQuery to ensure the complete set of correlated logs is available for as long as security and compliance policies require.

Implement Artifact Provenance and Security Scanning in Artifact Registry

Artifact Registry manages software artifacts such as Docker container images and works with Artifact Analysis to provide built-in security scanning. When the Container Scanning API is enabled, every new image pushed to Artifact Registry is automatically scanned for vulnerabilities in operating system packages and language dependencies (including Java and Go packages). This automatic on-push scanning identifies vulnerabilities by digest and creates vulnerability occurrences for that specific version. After the initial scan, continuous analysis checks for new vulnerabilities multiple times per day and updates metadata, but only for images that have been pushed or pulled within the last 30 days; after 90 days of inactivity, metadata is archived and no longer available.

For more flexible scheduling, On-Demand Scanning lets teams scan images locally on their computer or remotely in the registry at custom points in the CI/CD pipeline. It supports both synchronous (blocking until the scan finishes) and asynchronous modes, and you can choose a scanning location (US, Europe, or Asia) to minimize latency. This is useful when a build decision must be based on scan results before storing the artifact.

To track artifact lineage and build provenance, teams can generate a Software Bill of Materials (SBOM) for container images using the gcloud CLI. The SBOM lists every dependency inside the container, creating a complete inventory that can be reviewed for compliance and auditing. Artifact Analysis stores these SBOMs in Cloud Storage where they remain accessible for later reviews.

The integration between Cloud Build and Artifact Analysis surfaces security insights directly in the Cloud Build console, including vulnerability information, SLSA (Supply-chain Levels for Software Artifacts) levels, VEX (Vulnerability Exploitability eXchange) status, and SBOM data. Teams can enforce policies for artifact promotion by configuring the pipeline to fail builds when vulnerabilities exceed a severity threshold (such as CRITICAL or HIGH). This automated enforcement ensures that only artifacts meeting security requirements proceed through the deployment pipeline, protecting the software supply chain from known vulnerabilities.