Module 10.11: Cloud Custodian -- Policy-as-Code Governance Across Multi-Cloud

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Complexity: [COMPLEX]

Time to Complete: 45-60 minutes

Prerequisites: Read Module 10.2: Cloud Governance & Policy as Code and Module 10.3: Continuous Compliance & CSPM first. Familiarity with Terraform or OpenTofu is helpful.

What You’ll Be Able to Do

After completing this module, you will be able to:

Design a declarative Cloud Custodian policy set that separates selection, evidence, and remediation across AWS and Azure.
Implement safe idle-resource governance policies that tag first, stop later, and preserve accountable exception paths.
Compare Cloud Custodian with OPA Gatekeeper and Kyverno, then choose the correct enforcement layer for a production governance problem.
Diagnose production failure modes in Custodian rollouts, including IAM gaps, event-mode drift, noisy metrics, and multi-account blast radius.
Evaluate the cost and operational tradeoffs of running Custodian at moderate multi-cloud scale.

Why This Module Matters

Hypothetical scenario: A platform team inherits three AWS organizations, five Azure subscriptions, and a Kubernetes fleet that spans business units acquired over several years. The security team has a spreadsheet that says every EC2 instance and Azure VM must have an owner tag, every S3 bucket must use a lifecycle rule, and every idle server must be reviewed before the next quarter. The spreadsheet is accurate for exactly one day. By the end of the week, a Terraform apply creates twelve untagged instances, a break-glass admin disables lifecycle on a log bucket to debug retention, and an old analytics VM keeps running because no one remembers who owns it.

That is the moment when “cloud governance” stops being a policy document and becomes an operational system. Module 10.2 showed you the pyramid: organization policies, IaC validation, Kubernetes admission control, and runtime detection. Module 10.3 showed you why evidence has to be continuous instead of audit-day theater. Cloud Custodian fills a specific gap between those two ideas: it lets you inspect the current cloud estate, filter resources by real cloud state, and take controlled actions without writing a custom script for every service and provider.

This module is not a catalog of Custodian actions. You can get that from the reference docs. The goal here is to build the mental model you need in a design review: when declarative governance is safer than ad-hoc automation, how the policy engine thinks, how AWS and Azure differ in syntax, and how to operate the system without turning it into an expensive fleet-wide cron job that surprises application teams.

Why Declarative Governance Beats Ticket-Driven Cleanup

Start with the failure mode. Manual cloud cleanup looks reasonable when the environment is small. Someone runs a monthly report, sends messages to resource owners, and deletes the obvious waste. That works until ownership, cloud provider behavior, and resource lifecycles stop fitting into one person’s short-term memory.

The key production problem is not just drift. It is drift with weak evidence. A VM can be idle because it is abandoned. A VM can also be idle because it is a warm standby, a quarterly reporting node, or a licensed application that must stay provisioned even when CPU is low. A bucket without lifecycle can be a mistake. It can also be a litigation-hold exception. Governance automation has to distinguish “this is obviously wrong” from “this needs a human decision”.

Cloud Custodian gives you a policy loop that is closer to a building inspection than a demolition crew. Each policy describes the type of resource to inspect, the tests that narrow the list, and the action to take on the final set. That action can be gentle, like tagging for review. It can be evidentiary, like notifying a queue or writing a report. It can be corrective, like enabling encryption or stopping an instance. The important part is that each rule is stored as code, reviewed like code, and executed repeatedly against the real cloud control plane.

Ticket-driven cleanup
  report -> spreadsheet -> chat message -> manual change -> forgotten exception

Declarative governance
  policy -> cloud query -> filtered evidence -> tagged decision -> controlled action

The difference is accountability. A ticket tells you that someone intended to clean up a resource. A policy tells you the exact predicate that selected the resource, the action that was allowed, the identity that executed it, and the output captured at the time. That makes governance debuggable.

Cloud Custodian is especially useful in multi-cloud estates because it creates a repeated pattern across providers without pretending the providers are identical. AWS resources use AWS-shaped names and permissions. Azure resources use Azure-shaped resources and Monitor metrics. The policy grammar stays familiar, but the provider details remain explicit. That is a useful compromise: shared operating model, provider-native execution.

Hypothetical scenario: A central platform team wants one rule for “idle compute must be reviewed”. On AWS, the signal might be EC2 CPUUtilization from CloudWatch plus an Owner tag check. On Azure, the signal might be Azure Monitor Percentage CPU on azure.vm. The governance intent is the same, but the metric names, identity model, and stop behavior differ. Custodian lets you make that difference visible in code instead of hiding it in one generic script.

Pause and predict: If you immediately stop every VM below 5% CPU, which legitimate workloads are most likely to be harmed? Name at least two before reading on.

The safer pattern is usually a two-stage action. First, mark the resource for review with a deadline, owner-facing message, and ticket context. Second, run a separate policy that acts only on resources whose deadline has passed and whose exception signal is absent. That two-stage shape is how you turn automation from a surprise into a visible governance process.

flowchart LR
    A["Cloud inventory"] --> B["Filter: idle signal"]
    B --> C["Filter: no approved exception"]
    C --> D["Action: tag for review"]
    D --> E["Owner can fix, justify, or remove"]
    E --> F["Later policy checks expired tag"]
    F --> G["Action: stop or notify escalation"]

The loop also makes cost visible. An idle-instance report that runs once is a snapshot. A policy that runs every day creates a trend: which teams repeatedly leave resources idle, which business units carry exceptions, and which cleanup actions actually reduce spend. That trend is what turns governance from punishment into capacity planning.

The tradeoff is that Cloud Custodian is not a preventive admission controller for every cloud action. It can run in event-driven modes for some providers and resources, but many useful policies still operate as scheduled scans. That means a bad resource might exist briefly before Custodian tags or remediates it. For hard prohibitions, use organization policies, IAM/SCP boundaries, Azure Policy deny effects, or Kubernetes admission control. Use Custodian when the decision depends on current resource state, historical metrics, ownership tags, or remediation workflows that are too nuanced for a single deny rule.

The Cloud Custodian Policy Model

Cloud Custodian policies are YAML documents with a top-level policies list. Each policy usually has a name, a resource, optional filters, and optional actions. That looks simple enough to mistake for a loop over resources. It is not just a loop. The engine owns provider discovery, schema validation, resource augmentation, output, metrics, and execution mode wiring.

policies:
  - name: ec2-tag-idle-for-review
    resource: aws.ec2
    filters:
      - "tag:DoNotStop": absent
      - type: metrics
        name: CPUUtilization
        days: 7
        period: 86400
        value: 5
        op: less-than
    actions:
      - type: mark-for-op
        tag: custodian_status
        op: stop
        days: 7
        msg: "Idle for 7 days; stop scheduled after owner review window."

Read the policy from top to bottom as a sentence: “For EC2 instances, ignore resources with a DoNotStop tag, find instances whose average CPU was under 5% for seven days, and mark them for a future stop operation.” That sentence matters because a good Custodian policy should be explainable to the application owner who receives the tag or notification.

The resource field is the boundary of the query. It tells Custodian which provider API shape to load, which filters are legal, and which actions are safe for that kind of object. aws.ec2, aws.s3, and azure.vm are not interchangeable strings. Each resource type has provider-specific filters and actions because clouds do not expose identical control planes.

The filters field is the policy’s reasoning layer. Simple value filters inspect fields returned by the provider. Metric filters pull time-series data from CloudWatch or Azure Monitor. Tag filters encode ownership and exception rules. Boolean groups let you make the decision more precise. This is where most policy quality lives. A weak filter creates noisy remediation; a precise filter creates trust.

The actions field is the side-effect layer. Some actions only annotate, such as tag or mark-for-op. Some notify, report, or call another workflow. Some change infrastructure directly, such as stop, delete, set-bucket-encryption, or configure-lifecycle. Production teams should treat actions with the same care they apply to database migrations: start read-only, tag or notify next, then remediate only after the predicate has been proven.

+----------------------- Cloud Custodian Policy -----------------------+
| name      | Human-readable contract and stable reporting identity     |
| resource  | Provider API family: aws.ec2, aws.s3, azure.vm, ...        |
| filters   | Evidence and predicates: tags, metrics, state, config      |
| actions   | Side effects: tag, notify, mark, stop, delete, configure    |
| mode      | Execution trigger: pull, schedule, cloud event, container   |
+---------------------------------------------------------------------+

The part that makes this more than a for-each loop is resource augmentation. For example, S3 bucket governance often needs more than the bucket name. Policies may need the bucket policy, tags, lifecycle configuration, public access settings, encryption, logging, and replication state. Custodian includes resource-specific logic to fetch those subdocuments for the resource type. That saves you from writing one brittle script that calls list-buckets, then get-bucket-policy, then get-lifecycle-configuration, then handles every access-denied edge case by hand.

The second difference is output. A local script often prints a list and disappears. Custodian writes resources, logs, and policy output to a selected output directory or cloud storage location. That gives you evidence for audit and debugging. If a policy stopped an instance, you want the exact resource document that matched, not just a Slack message saying “cleanup happened”.

The third difference is mode. A policy can run as a local pull scan, a periodic job, or an event-driven function depending on provider support and the policy’s risk profile. The same policy intent can move from custodian run in CI to scheduled production execution without rewriting the selection logic. That is a major operational advantage over one-off scripts.

Pause and predict: Which part of a policy should change most often: the resource type, the filters, or the actions? Why?

In healthy programs, filters change more often than resource types and actions. The governance intent tends to stay stable: “idle compute is reviewed”, “public buckets are blocked”, “owner tags are required”. What changes is the evidence threshold, exception tag, allowed teams, or review window. If actions change constantly, the organization probably has not agreed on the operating model yet.

Policy anatomy by responsibility

Policy part	Design question	Failure if rushed	Production habit
`name`	Can humans identify this rule in logs and reports?	Ambiguous audit trails	Use stable names with domain and action
`resource`	Which provider object is being governed?	Wrong API assumptions	Keep provider names explicit
`filters`	What evidence proves this resource is in scope?	False positives	Start with read-only reports
`actions`	What is the least risky useful side effect?	Surprise outages	Tag before stop, stop before delete
`mode`	When should the policy run?	Cost spikes or stale enforcement	Match cadence to risk
output	Where does evidence land?	No audit trail	Centralize logs and resources

The policy file should also be validated before execution. custodian validate catches schema errors that a YAML parser cannot. YAML can be syntactically valid while still asking for an invalid action on a resource type. That distinction is important in CI: you want both “is this YAML valid?” and “is this a valid Custodian policy?”

python -m venv .venv
.venv/bin/pip install c7n

custodian validate policies/aws-idle-compute.yml
custodian run --dryrun -s output policies/aws-idle-compute.yml

The dry run is not a ceremonial step. It is the first time you see the actual resources that match. If a policy expected four idle instances and matches four hundred, the failure is not in the action. The failure is in your mental model of the estate.

AWS Worked Example: Idle Compute, S3 Lifecycle, and Untagged Cleanup

The first AWS pattern is idle EC2 governance. The mature version is not “stop anything quiet”. It is “identify low-utilization candidates, give owners a review window, then stop only resources that remain idle and unexceptioned”.

Exercise scenario: You operate an AWS account used by several product teams. You suspect long-running development instances are consuming budget, but you do not want to break standby systems or batch workers. Your governance rule is: mark running EC2 instances for review when average CPU is under 5% for seven days, exclude known protected instances, and stop them only after the review window expires.

policies:
  - name: ec2-idle-mark-for-review
    resource: aws.ec2
    description: |
      Mark running EC2 instances for review when CPU stays below 5 percent
      for seven days and no explicit protection tag is present.
    filters:
      - "tag:DoNotStop": absent
      - "tag:custodian_status": absent
      - type: value
        key: State.Name
        value: running
      - type: metrics
        name: CPUUtilization
        days: 7
        period: 86400
        value: 5
        op: less-than
        missing-value: 0
    actions:
      - type: mark-for-op
        tag: custodian_status
        op: stop
        days: 7
        msg: "Idle EC2 review: CPU < 5% for 7 days. Remove this tag or add DoNotStop=true with an approved ticket if this instance must remain running."

  - name: ec2-idle-stop-after-review
    resource: aws.ec2
    description: |
      Stop EC2 instances whose Custodian review window has expired.
    filters:
      - type: marked-for-op
        tag: custodian_status
        op: stop
      - "tag:DoNotStop": absent
      - type: value
        key: State.Name
        value: running
    actions:
      - type: stop

The missing-value: 0 choice deserves a design review. It tells the metric filter to treat absent metric data as zero. That is useful for idle detection because a stopped instance or one without metrics should not silently escape the policy. It is also dangerous if you apply it to a resource where metrics are absent because the resource is new, misconfigured, or outside the metric namespace. For production, add a second filter that excludes very new instances or requires a minimum age before evaluating CPU.

  - name: ec2-idle-mark-for-review-safer
    resource: aws.ec2
    filters:
      - "tag:DoNotStop": absent
      - "tag:custodian_status": absent
      - type: instance-age
        days: 14
        op: greater-than
      - type: value
        key: State.Name
        value: running
      - type: metrics
        name: CPUUtilization
        days: 7
        period: 86400
        value: 5
        op: less-than
        missing-value: 0
    actions:
      - type: mark-for-op
        tag: custodian_status
        op: stop
        days: 7

This small addition prevents the classic false positive where a newly launched instance has not accumulated enough metrics yet. The core lesson is that metric policies are never just metric policies. They are metric policies plus age, ownership, state, exception, and review-window logic.

Before running this: How many instances do you expect the dry run to match, and which tags would convince you the result is safe?

Use dry run before enabling the stop policy. The first command validates syntax and schema. The second collects matched resources without applying actions. The output directory becomes your review artifact.

custodian validate policies/aws-idle-ec2.yml
custodian run --dryrun -s output/aws-idle-ec2 policies/aws-idle-ec2.yml
find output/aws-idle-ec2 -name resources.json -print

The second AWS pattern is S3 lifecycle enforcement. Lifecycle is a good Custodian use case because the desired outcome is a resource configuration, not a one-time cleanup. Your rule might be: versioned buckets must transition old noncurrent versions and expire incomplete multipart uploads unless the bucket has an approved retention exception.

policies:
  - name: s3-enforce-standard-lifecycle
    resource: aws.s3
    description: |
      Apply a baseline lifecycle rule to versioned buckets that do not carry
      an approved retention exception.
    filters:
      - "tag:RetentionException": absent
      - type: value
        key: Versioning.Status
        value: Enabled
      - or:
          - Lifecycle.Rules: absent
          - type: value
            key: "length(Lifecycle.Rules[?ID=='kdojo-standard-lifecycle'])"
            value: 0
    actions:
      - type: configure-lifecycle
        rules:
          - ID: kdojo-standard-lifecycle
            Status: Enabled
            Filter:
              Prefix: ""
            NoncurrentVersionExpiration:
              NoncurrentDays: 35
            AbortIncompleteMultipartUpload:
              DaysAfterInitiation: 7

The S3 example also shows why Custodian’s resource model matters. Lifecycle state is not returned by a simple bucket list. The engine has to augment each bucket with additional configuration before your filter can inspect it. That is convenient, but it is not free. Every extra subdocument can mean more provider API calls. At small scale this is invisible. At hundreds or thousands of buckets across many accounts, unnecessary augmentation becomes latency, throttling, and cost noise.

For S3 policies, narrow the inventory before you ask Custodian to inspect expensive subdocuments. Use account selection, policy-directory boundaries, naming conventions, cheap metadata, and report-only canaries to reduce the bucket set first. Then inspect lifecycle, encryption, logging, policy, or tagging details only when the rule truly needs those fields. The operator habit is the important part: treat augmentation as a budgeted dependency, not a free background detail. If you want a provider-specific optimization knob, verify it against the exact Cloud Custodian resource documentation and validate the policy before adding it to a lab.

The third AWS pattern is untagged-resource cleanup. Here the action should be slower and more conservative than the selection. Missing owner tags often mean weak process, not disposable infrastructure. Treat tag enforcement as a path to accountability first.

policies:
  - name: ec2-untagged-owner-mark
    resource: aws.ec2
    filters:
      - type: value
        key: State.Name
        value: running
      - "tag:Owner": absent
      - "tag:CostCenter": absent
      - "tag:custodian_status": absent
    actions:
      - type: mark-for-op
        tag: custodian_status
        op: stop
        days: 10
        msg: "Missing Owner and CostCenter tags. Add tags or approved exception before the review window expires."

  - name: ec2-untagged-owner-stop
    resource: aws.ec2
    filters:
      - type: marked-for-op
        tag: custodian_status
        op: stop
      - "tag:Owner": absent
      - "tag:CostCenter": absent
      - type: value
        key: State.Name
        value: running
    actions:
      - type: stop

Notice that this policy does not terminate anything. Stopping is reversible for many EC2 workloads. Termination is a destructive action with data-loss risk. If your governance program starts with termination, application teams will route around it. If it starts with visible tags, reports, and reversible actions, teams are more likely to fix ownership upstream.

AWS production notes

Concern	What to decide	Why it matters
IAM role	Which permissions does each policy need?	Least privilege is per action, not per tool
Metric window	How many days and what statistic?	Short windows catch noise; long windows hide waste
Exception tag	Who can set it and with what ticket?	A self-service bypass becomes permanent drift
Output storage	Where do `resources.json` and logs go?	Audit and rollback need evidence
Schedule	Daily, hourly, or event-driven?	Cadence drives cost, API pressure, and reaction time

Azure Worked Example: Same Pattern, Different Provider Shape

The Azure version should feel familiar but not identical. The top-level policies list is the same. The provider resource is different. The metric filter uses Azure Monitor terminology. The stop action deallocates the VM when you use the stop action for azure.vm. The exception and ownership tags are still your operating model.

Exercise scenario: Your Azure subscription contains development VMs used by several teams. The FinOps lead wants low-CPU VMs tagged after seven days and stopped after fourteen days unless a DoNotStop tag is present. The Azure platform team also wants a variant that reports VMs missing required ownership tags without immediately stopping or deleting anything.

policies:
  - name: azure-vm-idle-mark-for-review
    resource: azure.vm
    description: |
      Mark Azure VMs for review when average Percentage CPU is below 5
      for seven days and no protection tag is present.
    filters:
      - type: value
        key: "tags.DoNotStop"
        value: absent
      - type: value
        key: "tags.custodian_status"
        value: absent
      - type: metric
        metric: Percentage CPU
        aggregation: average
        op: le
        threshold: 5
        timeframe: 168
        no_data_action: to_zero
    actions:
      - type: mark-for-op
        tag: custodian_status
        op: stop
        days: 7
        msg: "Idle Azure VM review: CPU <= 5% for 7 days. Remove this tag or add DoNotStop=true with an approved ticket if this VM must remain running."

  - name: azure-vm-idle-stop-after-review
    resource: azure.vm
    filters:
      - type: marked-for-op
        tag: custodian_status
        op: stop
      - type: value
        key: "tags.DoNotStop"
        value: absent
    actions:
      - type: stop

The metric window uses hours, not days. Seven days is 168 hours. That difference looks small, but it is exactly the kind of provider syntax mismatch that causes bad copy-paste governance. Do not hide it behind a generic abstraction unless the abstraction is tested and owned. In most teams, explicit provider policy files are easier to review.

The no_data_action: to_zero choice is the Azure sibling of AWS missing-value: 0. It helps include quiet VMs whose metrics are absent. It can also create false positives. Before enabling the stop policy, run a report-only policy and inspect the matched resources.

custodian validate policies/azure-idle-vm.yml
custodian run --dryrun -s output/azure-idle-vm policies/azure-idle-vm.yml

The missing-tag report shows a different governance posture. Required tags can be enforced with Azure Policy for new resources. Custodian is useful for finding and routing existing drift, especially VMs that predate the rule or arrived through import and migration paths. You might tag, notify, or open a ticket before blocking future creation with preventive controls.

policies:
  - name: azure-vms-missing-required-tags-report
    resource: azure.vm
    description: |
      Identify VMs missing required ownership tags so the platform team can
      route them to the right application owners before enforcement.
    filters:
      - or:
          - type: value
            key: "tags.Owner"
            value: absent
          - type: value
            key: "tags.CostCenter"
            value: absent
    actions:
      - type: tag
        tag: tag-review
        value: required

Which approach would you choose here and why: Azure Policy deny for missing required tags on new VMs, Custodian report for existing missing tags, or both?

The answer is usually both. Azure Policy is better for preventing new non-compliant resources at the ARM control plane. Custodian is better for scanning what already exists, attaching ownership context, and orchestrating remediation over time. The two controls should not compete. They should cover different lifecycle moments.

AWS and Azure syntax comparison

Intent	AWS shape	Azure shape	Design note
Resource	`resource: aws.ec2`	`resource: azure.vm`	Keep provider explicit
CPU metric	`name: CPUUtilization`	`metric: Percentage CPU`	Metric names are provider-native
Time window	`days: 7` plus `period`	`timeframe: 168` hours	Review units carefully
Missing data	`missing-value: 0`	`no_data_action: to_zero`	Use only with age safeguards
Stop	`type: stop`	`type: stop`	Same action name, provider behavior differs
Multi-account	`c7n-org` accounts	`c7n-org` subscriptions	One runner, different config

This is the main mental model for multi-cloud Custodian work: standardize the governance intent, not every line of syntax. If your organization says “idle compute gets a review tag after seven days,” that sentence should be the same across providers. The implementation should remain honest about each provider’s metrics, identity, API limits, and stop semantics.

Cloud Custodian vs OPA, Gatekeeper, and Kyverno

Cloud Custodian, OPA Gatekeeper, and Kyverno are all policy-as-code tools, but they do not occupy the same layer. Confusing them leads to weak designs. The right question is not “which policy engine is best?” The right question is “where in the lifecycle does this decision need to happen?”

Cloud Custodian is strongest when the policy needs cloud inventory, provider metrics, historical state, or remediation actions against cloud resources. It can inspect existing resources and change them. It is a governance worker.

OPA is a general-purpose policy engine. Gatekeeper is the Kubernetes-native project that uses OPA/Rego with constraints and constraint templates for admission control and audit. It is strongest when you need complex policy logic at the Kubernetes API boundary or when your organization already uses Rego across APIs, CI, and infrastructure checks.

Kyverno is Kubernetes-focused and uses Kubernetes-style policy resources. It is strongest when platform teams want validation, mutation, generation, image verification, exceptions, and policy reports inside the cluster without teaching every contributor Rego.

+---------------------+------------------------+--------------------------+
| Question            | Better layer           | Example                  |
+---------------------+------------------------+--------------------------+
| Should this cloud   | Cloud provider policy  | Deny public AKS creation |
| API request happen? | or organization guard  | with Azure Policy        |
+---------------------+------------------------+--------------------------+
| Does existing cloud | Cloud Custodian        | Tag idle EC2 for review  |
| state need cleanup? |                        | after metric evidence    |
+---------------------+------------------------+--------------------------+
| Should this K8s     | Kyverno or Gatekeeper  | Reject privileged Pods   |
| object be admitted? |                        | at API admission         |
+---------------------+------------------------+--------------------------+
| Does one rule need  | OPA/Rego ecosystem     | Share authorization      |
| cross-system logic? |                        | policy across services   |
+---------------------+------------------------+--------------------------+

Decision comparison

Need	Cloud Custodian	OPA Gatekeeper	Kyverno
Govern AWS/Azure/GCP resources already deployed	Strong	Weak unless external data is built	Weak outside Kubernetes
Enforce Kubernetes admission decisions	Limited and specialized	Strong	Strong
Mutate Kubernetes manifests on admission	Not the main job	Possible with Gatekeeper mutation	Strong
Generate Kubernetes resources	Not the main job	Not a core strength	Strong
Use historical cloud metrics	Strong	Not natural	Not natural
Remediate cloud resources	Strong	Not natural	Not natural
Reuse one language outside Kubernetes	Moderate DSL reuse	Strong with Rego and OPA	Limited to Kyverno ecosystem
Entry barrier for platform YAML users	Moderate	Higher because Rego	Lower for Kubernetes teams

The important distinction is runtime authority. Gatekeeper and Kyverno sit in the Kubernetes admission path. If they deny a Pod, the Pod never exists. Custodian often works after resources exist, unless deployed in an event-driven mode for specific cloud events. That is not a weakness. It is a different safety property.

Use a preventive layer when the organization cannot tolerate even temporary non-compliance. For example, denying a public production cluster endpoint should happen at the provider policy layer. Use Custodian when the policy needs evidence, review, staged remediation, or historical metrics. For example, “stop idle instances after owner review” cannot be expressed well as a simple deny rule. Use Kubernetes admission control when the object is a Kubernetes object and the decision must happen before persistence.

Hypothetical scenario: A team deploys a Kubernetes Service of type LoadBalancer that creates a public cloud load balancer. Where should governance live? Kyverno or Gatekeeper can reject the Kubernetes Service before the cloud load balancer appears. Cloud provider policy can deny public load balancer creation at the cloud API. Cloud Custodian can scan existing load balancers and tag or delete ones that escaped older controls. The best production answer may use all three, but each has a distinct job.

Pause and predict: If a Kyverno policy and a Custodian policy both try to fix the same problem, what symptoms tell you the boundary is wrong?

Look for repeated remediations, noisy alerts, and resources flipping between states. If Custodian keeps deleting objects that admission control could have blocked, move the rule earlier. If admission control blocks resources because it lacks context from cloud metrics or ownership systems, move that decision to a detective workflow with review.

Operating Custodian in Production

Production Custodian is not just custodian run. It is identity, scheduling, output, logging, metrics, CI validation, exception ownership, and multi-account rollout control. Treat the policies as product code and the runner as infrastructure.

Execution modes

Pull mode is the default mental model. You run Custodian from a workstation, CI job, container, or scheduled worker. The policy queries the cloud provider, filters resources, applies actions, and writes output. This is the easiest mode to debug and the safest place to start.

Periodic mode turns policies into scheduled cloud functions for providers that support it. On AWS, Custodian can deploy Lambda-backed policies with schedule or periodic semantics. This is useful for policies that need steady enforcement but do not require a constantly running host. It also means each policy function has runtime, memory, timeout, IAM, log, and deployment lifecycle concerns.

CloudTrail-driven mode on AWS subscribes policy Lambda functions to API events. That is valuable when a policy should react soon after resource creation or modification. The tradeoff is complexity. Event patterns, resource ID extraction, function deployment, and stale schedules need careful lifecycle management. If you change a policy from one mode to another, clean up old functions and schedules deliberately.

Azure has pull and Azure-specific event or container modes depending on deployment shape. Many teams still begin with a containerized runner in CI or a scheduled platform job because it centralizes credentials, output, and change control. That is fine. A simple boring runner is often better than many event functions that no one debugs well.

+----------------+---------------------------+----------------------------+
| Mode           | Best fit                  | Main risk                  |
+----------------+---------------------------+----------------------------+
| Pull           | CI, scheduled jobs, tests | Stale if cadence is too low |
| Periodic       | Regular enforcement       | Function sprawl, timeouts  |
| CloudTrail     | Event-driven AWS response | Event pattern mistakes     |
| Container job  | Central platform runner   | Broad credential blast     |
+----------------+---------------------------+----------------------------+

IAM and least privilege

Do not grant Custodian administrator access because “the policies are reviewed”. The policy action decides the permission boundary. A read-only report policy needs describe and list permissions. An EC2 stop policy needs stop permissions for the target resource. An S3 lifecycle policy needs lifecycle read and write permissions. Separate high-risk policy sets into separate roles.

This is where policy organization matters. Put report-only policies, tagging policies, and destructive policies in separate files or directories. Bind them to separate CI jobs or runner roles. That lets you approve a read-only policy quickly without also approving termination authority.

policy-repo/
  aws/
    report-only/
      s3-public-report.yml
      ec2-idle-report.yml
    reversible-actions/
      ec2-idle-stop.yml
      s3-lifecycle-enforce.yml
    destructive-actions/
      ebs-delete-unattached-after-review.yml
  azure/
    report-only/
    reversible-actions/

Secrets should not live in policy files. Use cloud-native identity wherever possible: IAM roles, workload identity, managed identity, or short-lived federated credentials from CI. If a webhook action is needed, route through a secret manager or a platform notification service instead of embedding a token in YAML. Keep example URLs fake and non-sensitive.

Logging, metrics, and evidence

Custodian emits policy output that should be treated as audit evidence. At minimum, store resources.json, logs, and policy reports in a central bucket or storage account with retention. For AWS, Custodian can publish policy metrics to CloudWatch, including resource count, timing, API calls, and action timing. Those metrics are operational signals, not vanity graphs.

Useful production dashboards answer these questions:

Question	Metric or artifact	Why it matters
Did the policy match more resources than usual?	`ResourceCount` trend	Detect runaway filters
Did provider API calls spike?	`ApiCalls` trend	Catch expensive scans
Did actions slow down?	`ActionTime`	Detect throttling or provider issues
Which resources were changed?	`resources.json`	Provide audit and rollback context
Which policies keep failing?	Logs by policy name	Fix IAM or schema drift

c7n-org for multi-account execution

At single-account scale, one scheduled runner can be enough. At enterprise scale, the problem becomes account and subscription fan-out. c7n-org runs policies across AWS accounts, Azure subscriptions, GCP projects, and OCI tenancies from a config file. That is powerful, so it needs guardrails.

accounts:
  - account_id: "111122223333"
    name: app-dev
    regions:
      - us-east-1
      - us-west-2
    role: arn:aws:iam::111122223333:role/CloudCustodianReadOnly
    tags:
      - env:dev
      - scope:standard

  - account_id: "444455556666"
    name: app-prod
    regions:
      - us-east-1
    role: arn:aws:iam::444455556666:role/CloudCustodianReversibleActions
    tags:
      - env:prod
      - scope:pci

c7n-org validate -c accounts.yml -u policies/aws/report-only
c7n-org run -c accounts.yml -s output/aws-report -u policies/aws/report-only --dryrun
c7n-org report -c accounts.yml -s output/aws-report -u policies/aws/report-only

Use account tags to control rollout. Start with one development account. Expand to a small production canary. Then expand by organizational unit or subscription group. Do not run a new stop policy across every account on day one just because the tool can.

CI integration

Policy CI should catch at least five problems before execution:

YAML syntax errors.
Custodian schema errors.
Accidental destructive actions in the wrong directory.
Missing owner metadata for new policies.
Changed policies without a dry-run artifact or reviewer approval.

name: custodian-policy-ci

on:
  pull_request:
    paths:
      - "policies/**/*.yml"
      - "policies/**/*.yaml"

jobs:
  validate:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: "3.12"
      - name: Install Cloud Custodian
        run: |
          python -m pip install --upgrade pip
          pip install c7n c7n-azure c7n-org
      - name: Validate policies
        run: |
          custodian validate policies/aws/**/*.yml policies/azure/**/*.yml

In this repository’s local instructions, commands should use .venv/bin/python. The workflow above is an illustrative GitHub Actions example running in a clean hosted environment. For KubeDojo local validation, follow the repo’s own .venv/bin/python rule.

Cost-lens checklist

Cloud Custodian is open source, but operating it is not cost-free. At moderate scale, the cost is usually from cloud API calls, function invocations, logs, metric ingestion, storage for evidence, and human review time. The software license is not the bill. The control-plane activity is the bill.

Use this checklist before promoting a policy:

What costs at moderate scale? Provider API calls, CloudWatch or Azure Monitor metric queries, Lambda or container runtime, log ingestion, output storage, notification delivery, and reviewer time.
What reduces cost? Narrow resource filters, provider-side query filters where documented, less frequent schedules, canary account rollouts, avoiding unnecessary S3 subdocument reads, centralized metrics with ignore_zero, and report-only dry runs before remediation.
What makes cost spike? Running broad policies across all accounts and regions, scanning high-cardinality resources every few minutes, fetching unnecessary S3 subdocuments, emitting every zero-value metric, retrying after IAM failures, and sending one notification per resource instead of batching.
What is the business value? Estimate idle resource savings, reduced audit-prep time, avoided policy drift, and fewer custom scripts before adding another recurring scan.
What is the rollback plan? Keep output artifacts, use reversible first actions, and define who can remove review tags or pause a policy.

Pause and predict: Which is more likely to create surprise cost: a stop action on ten EC2 instances, or a read-only S3 policy that scans thousands of buckets across every account every hour? Explain your reasoning.

The answer is often the read-only scan. Read-only does not mean free. Broad inventory scans can generate many API calls, logs, metrics, and retries. That is why production Custodian programs review selection cost as seriously as action risk.

Patterns & Anti-Patterns

Patterns are the operating habits that keep Custodian trusted. Anti-patterns are the shortcuts that make teams disable it.

Pattern	When to use	Why it works	Scaling consideration
Tag first, act later	Idle compute, missing ownership, ambiguous cleanup	Gives owners a review window and creates evidence	Requires tag ownership and expiry automation
Separate report, reversible, and destructive policies	Any multi-account program	IAM and review can match risk level	More directories and pipelines, but smaller blast radius
Canary by account tag	New policy rollout	Finds false positives before fleet-wide action	Needs account metadata in c7n-org config
Provider-native syntax with shared intent	Multi-cloud policies	Avoids fake abstractions over different APIs	Requires reviewers who can read provider details
Policy output as audit evidence	Compliance and FinOps programs	Shows exactly which resources matched and why	Needs retention, storage lifecycle, and access control
Metric plus age plus exception filters	Idle-resource policies	Reduces false positives from new or special resources	More policy lines, but much safer decisions

Anti-pattern	What goes wrong	Why teams fall into it	Better alternative
Admin role for every policy	One policy bug can mutate the whole account	Faster bootstrap during proof of concept	Separate roles by action class
Immediate delete on first match	Data loss and organizational backlash	Cleanup pressure and confidence from small tests	Mark, notify, stop, then delete only after retention review
One giant policy file	Hard reviews and accidental broad changes	Central teams want one source of truth	Split by provider, action risk, and domain
Generic multi-cloud wrapper hides provider behavior	Reviewers miss metric and stop semantics	Desire for one elegant abstraction	Standardize naming and intent, not every field
Audit-only forever	Drift becomes normalized	Teams fear breaking production	Time-box report mode with clear promotion criteria
No exception lifecycle	Permanent bypass tags accumulate	Exceptions unblock incidents quickly	Require owner, ticket, expiry, and automated reporting
Every policy runs hourly	API throttling and log cost spikes	”More frequent is safer” thinking	Match cadence to risk and resource volatility
Custodian replaces preventive controls	Bad resources exist repeatedly before cleanup	One tool feels simpler	Use SCP/Azure Policy/Kyverno for hard denies

The strongest pattern is staged trust. Run a report. Show owners the results. Tune filters. Tag resources. Measure the false-positive rate. Only then automate the reversible action. This takes longer than a heroic cleanup script, but it creates a system people can live with.

Decision Framework

Use this framework when deciding where a governance rule belongs. Start with the lifecycle moment, then choose the policy layer.

flowchart TD
    A["Governance requirement"] --> B{"Must this be blocked before creation?"}
    B -->|Yes| C{"Is it a cloud provider resource?"}
    C -->|Yes| D["Use SCP, Azure Policy, GCP Org Policy, or IAM guardrail"]
    C -->|No, Kubernetes object| E["Use Kyverno or OPA Gatekeeper admission"]
    B -->|No| F{"Does the rule need existing cloud state or metrics?"}
    F -->|Yes| G["Use Cloud Custodian"]
    F -->|No| H{"Is it mainly IaC before apply?"}
    H -->|Yes| I["Use OPA/Conftest, Checkov, tfsec, or Terraform policy checks"]
    H -->|No| J["Use monitoring, CSPM, or runtime detection"]
    G --> K{"Action risk high?"}
    K -->|Yes| L["Report -> tag -> notify -> reversible action -> destructive only after approval"]
    K -->|No| M["Automate with canary rollout and evidence retention"]

Design review questions

Question	Choose Custodian when…	Choose another layer when…
Is the decision based on historical usage?	CPU, request count, age, or resource state matters	Creation-time attributes are enough
Does the rule need remediation?	You need to tag, stop, configure, or notify	You only need to reject invalid input
Is temporary non-compliance acceptable?	A review window is acceptable	The resource must never exist
Is the object a Kubernetes resource?	The object is a cloud-side resource created by K8s	Admission can block the K8s object directly
Does the policy span accounts?	`c7n-org` can fan out safely with account tags	Provider organization policy can enforce centrally
Is the action destructive?	You have staged evidence and approval	Use manual workflow or break the task apart

Policy promotion ladder

Inventory: run read-only and collect matched resources.
Explain: share the predicate with resource owners and adjust false positives.
Mark: add review tags with owner-facing messages.
Notify: batch notifications to team channels or ticket queues.
Remediate: stop, configure, or lock resources when the review window expires.
Escalate: reserve delete or irreversible actions for approved, narrow cases.

This ladder prevents a common failure: the central team writes a technically correct policy, but teams experience it as random punishment. The ladder makes policy behavior legible before it becomes forceful.

Did You Know?

Cloud Custodian was created at Capital One in 2016, accepted into the CNCF Sandbox in 2020, and moved to CNCF Incubating status on September 14, 2022.
The CNCF project page describes Cloud Custodian as a YAML DSL for querying, filtering, and acting on cloud resources for security, cost optimization, and governance.
c7n-org can run the same policy runner pattern across AWS accounts, Azure subscriptions, GCP projects, and OCI tenancies from configuration files.
Cloud Custodian’s AWS metrics output can include policy exception count, resource count, resource time, action time, and API calls, which means governance scans can be monitored like production jobs.

Common Mistakes

Mistake	Why It Happens	How to Fix It
Treating Custodian as a one-off cleanup script	The first use case is often waste cleanup, so teams skip operating discipline	Put policies in Git, validate them in CI, and retain output artifacts
Using CPU alone as an idle signal	CPU is easy to query and explain	Combine CPU with age, tags, state, owner review, and exception rules
Granting one broad admin role	It avoids IAM debugging during early rollout	Create separate roles for report-only, reversible, and destructive policy sets
Running every policy everywhere	Multi-account tooling makes fan-out easy	Canary by account tag, region, resource type, and action risk
Hiding provider differences behind a generic template	Teams want one multi-cloud policy	Keep intent shared but leave provider metrics and actions explicit
Forgetting output and evidence retention	The policy “worked”, so logs seem secondary	Store `resources.json`, logs, and reports in controlled storage with retention
Letting exception tags live forever	Exceptions are granted during incidents and never revisited	Require ticket, owner, expiry, and a policy that reports expired exceptions
Promoting from dry run straight to delete	Cleanup pressure overrides safety	Use the promotion ladder: inventory, explain, mark, notify, remediate, escalate

Quiz

Question 1: Your first idle EC2 policy matches 320 instances when the FinOps team expected about 25. What do you check before changing the action?

Check the filters and the evidence output before touching the action. Look at resources.json to see whether the matches are new instances, stopped instances, instances missing metrics, or legitimate low-CPU standby systems. Then add age, state, ownership, and exception filters as needed. The action is not the root problem; the selection predicate is too broad for production trust.

Question 2: A security engineer wants to use Cloud Custodian to prevent creation of public AKS clusters. Is that the best layer?

Usually no. If the cluster must never be created with a public control plane, use Azure Policy or another provider-side preventive control at creation time. Cloud Custodian can still scan existing clusters for drift or tag resources created before the deny rule existed. The design is stronger when preventive controls block new violations and Custodian handles existing-state cleanup.

Question 3: Your Azure idle-VM policy uses `no_data_action: to_zero` and begins marking many recently created VMs. What is the likely failure mode?

The policy is treating missing metric data as zero CPU before the VMs have enough monitoring history. That makes new resources look idle even when the signal is incomplete. Add an age filter or a tag-based onboarding grace period, then rerun in dry-run mode. Missing data handling is useful, but only when paired with safeguards against immature metrics.

Question 4: A team asks why you use Kyverno for Kubernetes labels but Cloud Custodian for EC2 tags. How do you explain the split?

Kyverno sits in the Kubernetes admission path and can reject or mutate Kubernetes objects before they are stored. EC2 tags live on cloud resources outside the Kubernetes API, so Custodian is better suited to query AWS, evaluate existing state, and apply tag or stop actions. The split follows the lifecycle boundary. Use the engine closest to the resource and decision point.

Question 5: A Custodian S3 lifecycle policy is read-only during testing, but the API call count spikes across the organization. Why can read-only still be expensive?

Read-only policies can still query many resources and fetch many subdocuments. S3 governance often requires lifecycle, tagging, policy, encryption, or logging details beyond the basic bucket list. At large scale, those extra reads create latency, throttling pressure, logs, and metrics. Use provider-side narrowing where documented, scope the run to a smaller account or bucket set first, and inspect lifecycle, tagging, encryption, or logging details only when the decision truly needs those subdocuments.

Question 6: Your c7n-org run stopped resources in a production account that was supposed to be report-only. What process gap do you investigate?

Start with account selection, policy directory boundaries, and IAM role mapping. The production account may have been included by an account tag, the wrong policy directory may have been passed to c7n-org run, or the role may have allowed reversible actions in a report-only context. The fix is not only a policy edit. Separate report-only and action roles, require explicit account tags for rollout, and make CI block destructive actions in the wrong directory.

Question 7: An application owner says an idle instance is a quarterly reporting server and must remain provisioned. What should the exception process require?

The exception should be visible, scoped, and temporary. Require an owner, ticket, business justification, expiry date, and an approved tag such as DoNotStop=true plus a ticket reference. Then run a separate policy that reports expired exceptions. Do not rely on a Slack thread or undocumented tag value because those disappear from audit context.

Question 8: A platform team wants to write one generic "idle compute" policy generator for AWS and Azure. What design warning would you give them?

Standardize the governance intent, but do not hide provider behavior. AWS and Azure use different metric names, time-window fields, missing-data controls, identity models, and stop semantics. A generator can help if it emits explicit provider-native policies that reviewers can inspect. If it hides those differences, false positives become harder to diagnose.

Hands-On Exercise

Exercise scenario: You are adding the first Cloud Custodian policy pack for a platform team. Your goal is not to delete resources. Your goal is to design a safe idle-compute workflow that tags first, stops later, and can be expressed on both AWS and Azure.

You do not need live cloud credentials to complete the design portion. If you have a sandbox account or subscription, run only validate and --dryrun until you have inspected the matched resources. Do not run stop actions in a shared environment.

Setup

Create a local policy workspace:

mkdir -p custodian-lab/policies/aws custodian-lab/policies/azure custodian-lab/output
cd custodian-lab
python -m venv .venv
.venv/bin/pip install c7n c7n-azure c7n-org

If your shell cannot find custodian after installation, call it through the virtual environment path:

.venv/bin/custodian --help

Task 1: Design the AWS “mark for review” policy

Create policies/aws/idle-ec2-review.yml. The policy must find running EC2 instances with average CPU below 5% for seven days. It must exclude instances with DoNotStop. It must tag matching instances for review instead of stopping them immediately.

Solution

policies:
  - name: ec2-idle-mark-for-review
    resource: aws.ec2
    filters:
      - "tag:DoNotStop": absent
      - "tag:custodian_status": absent
      - type: value
        key: State.Name
        value: running
      - type: instance-age
        days: 14
        op: greater-than
      - type: metrics
        name: CPUUtilization
        days: 7
        period: 86400
        value: 5
        op: less-than
        missing-value: 0
    actions:
      - type: mark-for-op
        tag: custodian_status
        op: stop
        days: 7
        msg: "Idle EC2 review: CPU < 5% for 7 days. Stop scheduled after owner review unless DoNotStop=true is approved."

Task 2: Extend AWS to stop after the review window

Create a second AWS policy in the same file. It should stop only instances whose review marker has expired and that still do not have DoNotStop.

Solution

  - name: ec2-idle-stop-after-review
    resource: aws.ec2
    filters:
      - type: marked-for-op
        tag: custodian_status
        op: stop
      - "tag:DoNotStop": absent
      - type: value
        key: State.Name
        value: running
    actions:
      - type: stop

Task 3: Express the same idea for Azure VMs

Create policies/azure/idle-vm-review.yml. Use Azure Monitor’s Percentage CPU metric. The policy should mark VMs for stop after seven days of review, and a second policy should stop marked VMs after the marker expires.

Solution

policies:
  - name: azure-vm-idle-mark-for-review
    resource: azure.vm
    filters:
      - type: value
        key: "tags.DoNotStop"
        value: absent
      - type: value
        key: "tags.custodian_status"
        value: absent
      - type: metric
        metric: Percentage CPU
        aggregation: average
        op: le
        threshold: 5
        timeframe: 168
        no_data_action: to_zero
    actions:
      - type: mark-for-op
        tag: custodian_status
        op: stop
        days: 7
        msg: "Idle Azure VM review: CPU <= 5% for 7 days. Stop scheduled after owner review unless DoNotStop=true is approved."

  - name: azure-vm-idle-stop-after-review
    resource: azure.vm
    filters:
      - type: marked-for-op
        tag: custodian_status
        op: stop
      - type: value
        key: "tags.DoNotStop"
        value: absent
    actions:
      - type: stop

Task 4: Validate and dry-run safely

Validate the policy files. If you have sandbox credentials, run dry runs and inspect resources.json. If you do not have credentials, explain what evidence you would inspect before enabling actions.

Solution

.venv/bin/custodian validate policies/aws/idle-ec2-review.yml
.venv/bin/custodian validate policies/azure/idle-vm-review.yml

# Run only in a sandbox account or subscription:
.venv/bin/custodian run --dryrun -s output/aws-idle policies/aws/idle-ec2-review.yml
.venv/bin/custodian run --dryrun -s output/azure-idle policies/azure/idle-vm-review.yml

Before enabling actions, inspect the matched resource count, instance age, owner tags, DoNotStop coverage, metric history, and whether any standby or batch systems appear in the output. If the dry run finds unexpected production systems, tune the filters before discussing the action.

Task 5: Add production safeguards

Add at least three safeguards to your policy design. Examples include account canaries, separate IAM roles, exception expiry reports, output retention, or batched owner notifications.

Solution

A strong answer includes safeguards such as:

Run first in a development account selected by c7n-org account tags.
Use a read-only role for report policies and a separate reversible-action role for stop policies.
Store output in central cloud storage with retention and restricted access.
Require DoNotStop=true, ExceptionTicket, ExceptionOwner, and ExceptionExpires tags for exceptions.
Add a policy that reports expired exceptions without stopping resources automatically.
Batch notifications by owner or team to avoid one message per resource.

Task 6: Compare the layer choice

For each requirement below, choose Cloud Custodian, Azure Policy/SCP, Kyverno, or OPA Gatekeeper. Write one sentence explaining each choice.

Requirement	Your choice
New production S3 buckets must never be public
Existing EC2 instances under 5% CPU for seven days should be tagged for review
Pods must not run privileged containers
Existing Azure VMs missing `Owner` or `CostCenter` tags should be reported and routed
Terraform plans must not create unencrypted disks

Solution

Requirement	Good choice	Why
New production S3 buckets must never be public	SCP/IAM guardrail plus S3 public access controls	It should be blocked before or at creation, not cleaned up later
Existing EC2 instances under 5% CPU for seven days should be tagged for review	Cloud Custodian	The decision depends on current inventory, metrics, tags, and staged remediation
Pods must not run privileged containers	Kyverno or OPA Gatekeeper	The decision belongs in Kubernetes admission before the Pod exists
Existing Azure VMs missing `Owner` or `CostCenter` tags should be reported and routed	Cloud Custodian	It scans existing state and can tag or notify owners
Terraform plans must not create unencrypted disks	OPA/Conftest, Checkov, tfsec, or platform IaC policy checks	The decision should happen before infrastructure apply

Success Criteria

I wrote an AWS policy that finds EC2 instances with CPU below 5% for seven days and marks them for review.
I extended the AWS workflow so instances stop only after the review marker expires.
I expressed the same idle-compute idea for Azure VMs using Azure Monitor Percentage CPU.
I explained why missing metric data needs safeguards before automated stop actions.
I validated the policy files and described what to inspect in dry-run output.
I added at least three production safeguards for IAM, rollout, exceptions, logging, or notifications.
I compared Custodian, provider policy, Kyverno, and Gatekeeper for five governance requirements.
I can defend why “tag first, stop later” is safer than immediate cleanup.

Sources

Next Module

Return to the Enterprise & Hybrid Cloud overview to place Cloud Custodian beside landing zones, compliance, fleet management, GitOps, zero trust, and FinOps in the full enterprise governance path.