Cloud Automation Glossary

Using admission controllers (e.g., Kubernetes admission webhooks, OPA/Gatekeeper, Kyverno) to automatically validate, mutate, or reject configuration changes before they are applied.

Using machine learning to automate anomaly detection, root cause analysis, and remediation.

Grouping related alerts using automation to reduce noise.

Using ML to detect patterns indicating upcoming failures.

Using cloud provider APIs for automating provisioning and operations.

Automatically promoting built artifacts (images, packages, Helm charts) across dev → test → staging → production based on passing tests, approvals, and policy checks.

Automatically generating logs, compliance checks, and evidence for audits.

Automated restarts of failed containers using orchestrators like Kubernetes.

LBs that automatically replace unhealthy backends.

Automatically creating architecture diagrams, inventories, or compliance reports.

Automated detection and correction of issues such as misconfigurations or failed services.

Automatically reverting to a stable version when failures occur.

Running automated tests (unit, integration, performance) as part of CI/CD workflows.

Systems capable of making automated optimization or remediation decisions end-to-end.

Automatically powering down non-production environments during off-hours.

Automating backup creation, retention, verification, and cataloging for databases, volumes, and object storage according to policy.

Switching traffic between two identical environments for safe releases.

Predefined patterns for automated environment creation (e.g., VPC, networking stacks).

Routing a small percentage of traffic to new versions for controlled rollout.

Integrating CI/CD and IaC pipelines with ITSM tools (e.g., ServiceNow, Jira) to auto-create, update, and close change tickets and approvals as part of deployments.

Systems that execute automated resilience experiments.

Automatically injecting controlled failures to test resilience.

Executing automation tasks through chat platforms integrated with CI/CD or cloud APIs.

Using software, scripts, and orchestration tools to provision, configure, scale, secure, and manage cloud resources with minimal manual work.

Unifying event-driven automation across clouds and environments.

Running automated jobs based on cron or time-based rules.

Automatically adjusts the number of nodes in a Kubernetes cluster based on demand.

Continuously validating infrastructure against regulatory and internal policies.

Ensuring consistent configuration across systems using tools like Ansible, Puppet, or Chef.

Automating deployment and operations of containers using systems like Kubernetes.

Preparing application deployments automatically after CI validation.

Automatically deploying code to production without manual approvals.

Automatically building and testing code whenever changes are committed.

Kubernetes pattern where controllers automate reconciliation between desired and actual state.

Automated detection of unexpected increases in cloud spend.

Automated rightsizing, scheduling, and resource management to reduce spend.

Kubernetes extension that defines new resources managed through automation.

Kubernetes-based automation for provisioning cloud resources across multiple providers.

Defines the desired end state, and the system determines how to reach it.

A controller that ensures actual system state matches the declared configuration.

Automating failover, failback, and DR tests across regions or environments using runbooks, orchestration workflows, and infrastructure-as-code.

Automatically restoring systems to the desired state.

Identifying configuration differences between actual and declared state.

Automatically creating dev, test, staging, or production environments using IaC and pipelines.

Automatically creating and tearing down short-lived, full-stack environments (per PR/feature branch) for testing and preview, driven by Git events and IaC.

Automation triggered by changes in logs, resource states, alarms, or external events.

A preview of infrastructure changes generated by IaC tools before execution.

Controlling feature rollout independently of deployments using automated toggles.

Automating lifecycle, policy, and configuration across multiple clusters.

Managing infrastructure and deployments using Git as the source of truth.

A standardized, preconfigured machine image used for consistent deployments.

A standardized, approved CI/CD pipeline template used across teams.

Automatically enforcing preventative controls (such as org policies, SCPs, or restrictive IAM baselines) at resource creation time so non-compliant changes are blocked before they land.

Automatically scales pod replicas based on CPU, memory, or custom metrics.

Automating workflows that span on-premises and cloud environments.

Catalogs reusable IaC modules for consistent deployments.

Automatically provisioning, modifying, or revoking cloud access based on policies.

An automation property where repeating an operation produces the same final state without side effects.

Infrastructure replaced rather than modified, preventing drift and configuration errors.

Explicitly defines each step required to achieve a target configuration.

Managing and provisioning infrastructure using code rather than manual processes.

A self-service automation platform enabling developers to deploy and manage infrastructure safely.

Automatically discovering and cataloging all cloud resources.

Scales applications based on external event sources.

Automatically rotating cryptographic keys on a schedule.

Automated rules for retention, archival, or deletion of cloud resources.

Automatically collecting, routing, indexing, or archiving logs.

Automatically deploying dashboards, alerts, and metric collectors for new resources.

Automating resource provisioning and management across more than one cloud provider.

Infrastructure updated in place through patches or configuration changes.

Automates complex application or service lifecycle tasks using Kubernetes operators.

Coordinated execution of automated tasks across applications, infrastructure, and cloud services.

Automatically detecting, scheduling, and applying OS, container base image, and middleware patches across fleets, with health checks and rollback if issues are detected.

Defining CI/CD pipelines using version-controlled configuration files.

Writing governance and compliance rules as code and enforcing them automatically.

Using ML to scale resources ahead of demand spikes.

Automatically creating compute, storage, networking, and other cloud resources.

Automation actions triggered before or after resource creation or updates.

Automatically preventing or limiting resource creation beyond allowed quotas.

Continuous evaluation that corrects drift between intended and actual state.

Automatically synchronizing configurations or data across regions or clouds.

Gradually updating application versions with no downtime.

Automated identification of underlying causes behind failures.

Automating operational tasks like restarts, scaling, patching, or failover.

Automating cloud tasks using programming languages and cloud SDKs.

Managing passwords, certificates, and tokens using automated injection and rotation.

Automatically scanning repositories, images, and configuration for hard-coded secrets and triggering rotation, revocation, or alerts when exposure is detected.

Continuously scanning cloud configurations against security benchmarks (e.g., CIS, NIST) and auto-creating findings or remediation tasks for misconfigurations.

Systems that automatically detect failures and recover without human input.

Developer-triggered automated provisioning of environments or resources.

Using serverless functions to perform tasks without managing infrastructure.

Automatically provisioning pre-approved infrastructure bundles.

Adjusting rollout speed, triggering automated rollback, or scaling resources based on SLOs, error budgets, and real-time reliability signals instead of only infrastructure metrics.

Preventing concurrent infrastructure changes to maintain state consistency.

Automating workflows using defined states and transitions.

Running scripted tests automatically to validate performance and uptime.

Automatically applying mandated metadata tags during resource creation.

Automatically steering traffic between versions, regions, or user groups.

Automatically adjusts CPU and memory allocations for pod containers.

Coordinating automated tasks using rules, schedules, or event triggers.