<user_query>
у нас развернут триви для цис бенчамарка
тимлид постаавил задачу Выработать удобный способ по сбору метрик с Trivy Operator.
</user_query>


Сначала быстро посмотрю, как `Trivy Operator` уже развернут у вас в репозитории, и сопоставлю это с доступными способами экспорта метрик, чтобы предложить практичный вариант без лишней сложности.

**Continuing in current mode**

I think I need to keep working since the user hasn't responded. It looks like the tool rejected my previous attempt, so I can't switch to another mode. First, I should update the commentary to acknowledge the request and outline the initial step. Then, I'll investigate the repository files.

Maybe I can use multi_tool_use.parallel to read the relevant files and run a search for the Trivy operator. Since the user asked about the strategy, checking the current setup makes sense. I might also want to look up the latest metrics documentation for Trivy Operator. I'll need to use WebSearch and WebFetch for that. Additionally, I should remember to cite the user rule yamllint if I do any editing later, though editing is unlikely at this point. I'll also need to read the README and any relevant values, so using parallel tasks sounds like the way to go.


Сейчас видно, что у вас уже есть зачаток интеграции с мониторингом: у pod выставлены `prometheus.deckhouse.io/*` аннотации, но часть самих метрик в `Trivy Operator` отключена. Дальше сверю это с официальными метриками `Trivy Operator`, чтобы предложить целевую схему без лишней кардинальности.

**Inspecting metrics setup**

I need to check the current deckhouse labels system. It seems like the Prometheus setup uses custom-target annotations for pods. A potential solution could be to enable the metrics endpoint and use Deckhouse annotations or ServiceMonitor to scrape metrics, routing the data to VictoriaMetrics or Grafana. I noticed that podLabels and podAnnotations are set to prometheus.deckhouse.io/custom-target trivy-operator. However, metricsFindingsEnabled is false, while metricsClusterComplianceInfo is true. I may want to fetch Trivy Operator documentation for the specific metric names. I'll do some web searches to gather that information.


**Evaluating documentation access**

I think I need to read the fetched file to check if it contains a relevant summary. It might be better to use an older version, like the v0.11 URL, but I'm also considering whether the web search snippet I found is sufficient. It's possible that I should fetch the old documentation as well to get a clearer understanding. These options could help me figure out the best way forward!


Картина складывается так: сбор на стороне мониторинга у вас уже стандартизован через `prometheus.deckhouse.io/*`, поэтому удобнее не городить отдельный exporter, а правильно включить именно нужные метрики у самого `Trivy Operator` и затем поверх них построить recording rules/дашборд.

**Investigating metrics configuration**

I’m looking into the `metricsClusterComplianceInfo: true` setting. It’s supposed to provide detailed information, but I need to understand its relationship with other configurations. It seems like the next step is to search the web for documentation or any issues related to this flag. I'll query the exact flags to get a clearer picture of how everything connects, which will help in resolving whatever uncertainty I have.


**Inspecting repository issues**

I might need to check the issue number 2007. But then again, maybe I should read the current README regarding metrics or installation details. That could provide some insight. I wonder if searching within the repository for "metricsClusterComplianceInfo" could yield useful results. It might give me a clearer understanding of what’s happening with these metrics. Let’s explore these options and see what I can find!


Самый удобный способ для вас: собирать метрики прямо с `/metrics` самого `Trivy Operator` через существующий `Deckhouse/Prometheus` scrape, а не делать отдельный exporter.

Судя по вашему конфигу, база уже есть:
- pod уже помечен `prometheus.deckhouse.io/port: "8080"` и `prometheus.deckhouse.io/custom-target: trivy-operator`
- `serviceMonitor` выключен, то есть у вас уже выбран deckhouse-style scrape
- при этом сейчас включен `operator.metricsClusterComplianceInfo: true`, а `operator.metricsFindingsEnabled: false`

Я бы рекомендовал поменять модель так:

1. Основной контур
- Оставить сбор через текущие `prometheus.deckhouse.io/*` аннотации.
- Включить `operator.metricsFindingsEnabled: true`.
- Выключить `operator.metricsClusterComplianceInfo: false` для всех кластеров по умолчанию.

2. Что собирать
- Для CIS использовать summary-метрику `trivy_cluster_compliance`.
- По ней строить:
  - `pass_count`
  - `fail_count`
  - `compliance_percent = pass / (pass + fail) * 100`
- Это дает удобные дашборды и алерты без взрыва cardinality.

3. Что не включать массово
- `*_Info`-метрики (`metricsClusterComplianceInfo`, `metricsConfigAuditInfo`, `metricsVulnIdEnabled` и т.д.) лучше не держать включенными везде.
- Они нужны только для глубокого разбора, но для постоянного мониторинга слишком дорогие по cardinality.

Практически это даст вам 2 уровня:
- Операционный: Prometheus/VictoriaMetrics + Grafana + alerting по `trivy_cluster_compliance`
- Диагностический: смотреть сам `ClusterComplianceReport` CRD, когда нужно понять, какие именно контроли упали

Итог: для задачи тимлида я бы предложил именно такой стандарт:
- `Trivy Operator` как источник метрик
- `Deckhouse Prometheus` как сборщик
- `VMRule/PrometheusRule` для агрегатов
- `Grafana` для витрины
- CRD использовать как drill-down, а не как основной источник метрик

Если хочешь, я следующим сообщением могу сразу подготовить:
1. конкретный patch для `values-global.yaml`
2. пример `VMRule` с `fail_count` и `compliance_percent`
3. набросок дашборда/алертов под CIS.

**Evaluating metrics settings**

I'm pondering the current repository, which has `clusterComplianceInfo` set to true, indicating high cardinality. I might suggest the opposite configuration: disable `metricsClusterComplianceInfo`, enable `metricsFindingsEnabled`, and use the `trivy_cluster_compliance` summary instead. I should verify if the summary metric is gated by the findings being enabled, which seems likely. Current settings could be suboptimal, with detailed metrics on but summary off— that feels odd. And I should check the Trivy config docs for the exact flag names.
**Analyzing monitoring needs**

I’m considering whether we really need a ServiceMonitor unless we're standardizing outside Deckhouse. For the data model, I think the SLI/KPI in Prometheus could come from `trivy_cluster_compliance{title="cis",status="Pass|Fail"}`, with `trivy_resource_configaudits` and `trivy_resource_infraassessments` as optional for more detailed analysis. I want to avoid using `*_info` and `metricsVulnIdEnabled` by default due to cardinality issues. For aggregation, creating recording rules in VictoriaMetrics for total pass/fail and compliance percentages sounds good. Should I also track the age of the last successful report?
**Evaluating compliance report details**

I’m considering the details I need for per-control metrics in the CRD collection, which might have high cardinality but is also structured. I think I should check the cluster compliance report document for the fields that indicate if per-control and resources are included. It might be unnecessary, but the report type does cover detailed targets since `compliance.reportType: all`. This increases detail, and noting it could be useful for the user, especially regarding planning rather than reviewing. I can also reference the code, including file paths and line numbers for clarity.
**Planning code citations**

I need to ensure I include at least one line in the final output, and it seems a current config snippet would be helpful. Maybe I should use code blocks with specific lines like 136-169, 237-251, and 655-658, which could be sufficient. I’ll gather those exact snippets via ReadFile specific offsets. It looks like I can cite those code blocks directly without using a tool since I have the necessary line numbers. This will make the references more precise and clear!


<user_query>
вот еще спросил у него, не отвечает
Ruslan Piatrovich, [10.03.2026 15:24]
Привет!
я на всякий уточню

Ruslan Piatrovich, [10.03.2026 15:24]
Выработать удобный способ по сбору метрик с Trivy Operator.

Ruslan Piatrovich, [10.03.2026 15:24]
мы говорим про состояние самого оператора

Ruslan Piatrovich, [10.03.2026 15:24]
или для циса надо дащбордик намутить
</user_query>


По формулировке `Выработать удобный способ по сбору метрик с Trivy Operator` это скорее звучит как:

- как технически забирать метрики из `Trivy Operator` в вашу систему мониторинга
- какие именно метрики включать
- как не словить лишнюю cardinality

Если бы задача была именно про `CIS`-витрину, обычно это сформулировали бы как `сделать дашборд по ClusterCompliance/CIS` или `визуализировать результаты benchmark`.

Но у вас есть двусмысленность, потому что `Trivy Operator` у вас используется именно для `CIS`, так что тимлид мог иметь в виду и второе.

Я бы пока исходил из такой трактовки:
- базовая задача: организовать корректный сбор метрик из `Trivy Operator`
- результат второго этапа: на этих метриках сделать `CIS`-дашборд и алерты

Можно ему дописать коротко так:

```text
Понял, тогда я пока иду от базового варианта:
1. определить, какие метрики Trivy Operator нам реально нужны и как их удобно собирать в Prometheus/VM
2. отдельно предложить, какие из них пойдут на CIS dashboard/alerts

Если задача именно про визуализацию CIS benchmark, то я сразу смещу фокус на dashboard и recording rules.
```

Если хочешь, я могу помочь тебе прямо сейчас сформулировать:
- короткий ответ тимлиду
- мини-концепт задачи в 5-7 строк для чата/тикета
- или сразу техническое предложение, которое можно ему отправить.

**Interpreting user requests**

The user’s request seems ambiguous, so I think it's essential to provide a clear response that leads them in the right direction. I don't expect to use tools here, just need to keep it concise and in Russian. I'll mention both possible interpretations, ask for a better phrasing, and clarify that "сбор метрик с Trivy Operator" relates more to the technical observability of exported metrics rather than just the CIS dashboard. I should craft a message to guide them.


<user_query>
давай пока включим метрики, потому что дефолтный дашборд триви уже развернут, но метрик никаких вообще нет
</user_query>


Проверю, какие значения реально прокидываются в конфиг оператора, и затем включу минимально нужные метрики так, чтобы дефолтный дашборд начал получать серии без лишней кардинальности.

**Considering metric enabling**

I’m thinking about editing the values-global files, potentially including production. The user has asked to enable metrics, so I need to inspect the chart and configuration mapping for both staging and production environments. I wonder if I should focus solely on the current stage or if that applies to all environments. The user mentioned a default dashboard is already deployed, but it seems there are no metrics because the metricsFindingsEnabled option is set to false. I might need to set the service monitor to activate the metrics endpoint.
**Enabling metrics for dashboard**

I’m considering the current setup, where vulnerabilityScannerEnabled, rbac, and exposedsecret are all set to false, but cluster compliance is still true. The user wants to enable metrics because the dashboard doesn’t show any data. I might need to set operator.metricsFindingsEnabled to true globally and confirm the need for basic findings summary metrics. It seems cluster compliance summary might rely on metricsFindingsEnabled being true. I’ll need to inspect the relevant files, and it may require updates for both stage and production environments. Asking about the scope could clarify things.
**Considering metric updates**

The user wants to enable metrics, which makes me think it's okay to modify the current chart for the staging environment. However, there are also production values to consider. I wonder if I should confirm if they only want to change staging, but maybe I can do both if they mirror each other. Since the user mentioned the dashboard is already deployed, I’ll check the production values-global and compare them. If the issues are similar, I may update both, but it's safer to ask for clarification first.


Вижу корень проблемы: `OPERATOR_METRICS_BIND_ADDRESS` уже включен, scrape-аннотации есть, но сами report-метрики отключены через `operator.metricsFindingsEnabled: false`. Меняю это в глобальных values, чтобы дашборд начал видеть серии.

**Deciding on Editing Metrics**

I’m considering if I should edit the stage and production values to be identical since there are no metrics to guide us. It seems reasonable to keep both environments consistent if the chart shows both. I should inform the user before making changes. I’ll apply the patch to a single file for each. Since the dashboard defaults to no metrics, enabling findings as true seems minimal, while potentially keeping clusterComplianceInfo true might be wise, but I’ll think about it further.
**Considering Override Values**

I need to check if the per-cluster values override metricsFindingsEnabled, but I think it’s likely they don’t. Even so, it doesn't seem necessary to worry about that for now. I’ll go ahead and patch both global settings instead. This way, I’m ensuring consistency across the board, which seems like a smart move. I want to make sure we're on the same page with all metrics involved, so this approach feels reasonable!


Правка внесена минимальная: включил именно summary-метрики, не трогая остальные `*_Info` флаги. Сейчас быстро проверю, что YAML и IDE-диагностика чистые.


Включил метрики findings в обоих глобальных конфигах:
- `common/trivy-operator/stage/values/values-global.yaml`
- `common/trivy-operator/prod/values/values-global.yaml`

Изменение одно: `operator.metricsFindingsEnabled: true`. Это должно начать отдавать summary-серии вроде `trivy_cluster_compliance`, `trivy_resource_configaudits`, `trivy_resource_infraassessments` на уже существующем `/metrics` и сделать дефолтный дашборд непустым.

`yamllint`-ошибок по измененным файлам нет.

Дальше нужно только применить релиз и проверить в Prometheus/VM запросом вроде:
```promql
{__name__=~"trivy_.*"}
```

Если хочешь, следующим сообщением могу подготовить короткий smoke-check список: что посмотреть после rollout, чтобы быстро понять, что метрики реально поехали.


<user_query>
http://localhost:38113/metrics
</user_query>


Проверю, что реально отдает endpoint `/metrics`, и скажу, какие серии там уже появились.

**Checking server metrics**

I think the user wants to inspect some metrics from a URL, but I can't use WebFetch with localhost. Maybe I could use a shell command like curl instead. To check if anything’s running, I should run a simple command with curl on localhost. I wonder if I need to check the terminals folder for existing processes — maybe not for a quick curl command. I’ll go ahead and run curl to get the information.


Endpoint отвечает, сейчас быстро вытащу из вывода именно `trivy_*` серии, чтобы понять, появились ли report-метрики или там пока только служебные метрики процесса.

**Exploring file inspection**

I need to inspect an output file using ReadFile. I'm wondering if searching for Trivy metrics names with a tool like "rg" would work well for this. I think I can use ReadFile to get the file, then utilize "rg" to search through the file for specific patterns, maybe focusing on names that start with "trivy_". I should also check if there are any metrics to capture. Let's see how this goes!


**Evaluating metrics processing**

I think I need to focus on inferring only the process metrics, maybe looking at the top of the file to gather the necessary information. This seems like a straightforward approach to ensure I'm getting the most relevant data without getting bogged down in unnecessary details. I'll need to be detail-oriented here, so keeping it simple might work best for efficient analysis.


На `http://localhost:38113/metrics` сейчас `trivy_*` метрик нет вообще.

Там есть только служебные метрики самого процесса/operator runtime:
- `controller_runtime_*`
- `process_*`
- `go_*`
- `promhttp_*`

Это значит одно из двух:
- изменения с `metricsFindingsEnabled: true` еще не доехали в кластер
- или pod, на который ты смотришь, еще работает со старым `ConfigMap`

Что проверить первым:
1. выполнить rollout/синк приложения
2. перезапустить `trivy-operator`, если ConfigMap не подхватился автоматически
3. снова открыть `/metrics` и поискать:
   - `trivy_cluster_compliance`
   - `trivy_resource_configaudits`
   - `trivy_resource_infraassessments`

Если после rollout там все еще пусто, тогда уже надо смотреть, какой `OPERATOR_METRICS_FINDINGS_ENABLED` реально внутри текущего pod/configmap. Могу дальше помочь точечно с командами `kubectl` для этой проверки.

**Explaining deployment findings**

I need to clarify that there haven't been any trivy_* results yet, possibly because the deployment hasn't been rolled out or there are no findings due to configurations not being applied. The localhost endpoint currently shows only internal metrics, so I might suggest rolling out the deployment. If kubectl is available, I could check the configmap directly. The user likely just wants a concise response with my findings based on the endpoint they provided.


<user_query>
apiVersion: v1
kind: ConfigMap
metadata:
  name: trivy-operator-config
  namespace: devsecops-trivy-operator
  uid: 97e92f52-53ee-4776-afa0-67428065d23d
  resourceVersion: '143185287'
  creationTimestamp: '2026-01-28T10:01:07Z'
  labels:
    app.kubernetes.io/instance: trivy-operator.atlas-a-stage-cluster
    app.kubernetes.io/managed-by: Helm
    app.kubernetes.io/name: trivy-operator
    app.kubernetes.io/version: 0.25.0
    helm.sh/chart: trivy-operator-0.27.0
  annotations:
    argocd.argoproj.io/tracking-id: >-
      trivy-operator.atlas-a-stage-cluster:/ConfigMap:devsecops-trivy-operator/trivy-operator-config
  managedFields:
    - manager: argocd-application-controller
      operation: Update
      apiVersion: v1
      time: '2026-03-10T13:45:31Z'
      fieldsType: FieldsV1
      fieldsV1:
        f:data:
          .: {}
          f:CONTROLLER_CACHE_SYNC_TIMEOUT: {}
          f:OPERATOR_ACCESS_GLOBAL_SECRETS_SERVICE_ACCOUNTS: {}
          f:OPERATOR_BATCH_DELETE_DELAY: {}
          f:OPERATOR_BATCH_DELETE_LIMIT: {}
          f:OPERATOR_BUILT_IN_TRIVY_SERVER: {}
          f:OPERATOR_CACHE_REPORT_TTL: {}
          f:OPERATOR_CLUSTER_COMPLIANCE_ENABLED: {}
          f:OPERATOR_CLUSTER_SBOM_CACHE_ENABLED: {}
          f:OPERATOR_CONCURRENT_NODE_COLLECTOR_LIMIT: {}
          f:OPERATOR_CONCURRENT_SCAN_JOBS_LIMIT: {}
          f:OPERATOR_CONFIG_AUDIT_SCANNER_ENABLED: {}
          f:OPERATOR_CONFIG_AUDIT_SCANNER_SCAN_ONLY_CURRENT_REVISIONS: {}
          f:OPERATOR_EXPOSED_SECRET_SCANNER_ENABLED: {}
          f:OPERATOR_HEALTH_PROBE_BIND_ADDRESS: {}
          f:OPERATOR_INFRA_ASSESSMENT_SCANNER_ENABLED: {}
          f:OPERATOR_LOG_DEV_MODE: {}
          f:OPERATOR_MERGE_RBAC_FINDING_WITH_CONFIG_AUDIT: {}
          f:OPERATOR_METRICS_BIND_ADDRESS: {}
          f:OPERATOR_METRICS_CLUSTER_COMPLIANCE_INFO_ENABLED: {}
          f:OPERATOR_METRICS_CONFIG_AUDIT_INFO_ENABLED: {}
          f:OPERATOR_METRICS_EXPOSED_SECRET_INFO_ENABLED: {}
          f:OPERATOR_METRICS_FINDINGS_ENABLED: {}
          f:OPERATOR_METRICS_IMAGE_INFO_ENABLED: {}
          f:OPERATOR_METRICS_INFRA_ASSESSMENT_INFO_ENABLED: {}
          f:OPERATOR_METRICS_RBAC_ASSESSMENT_INFO_ENABLED: {}
          f:OPERATOR_METRICS_VULN_ID_ENABLED: {}
          f:OPERATOR_PRIVATE_REGISTRY_SCAN_SECRETS_NAMES: {}
          f:OPERATOR_RBAC_ASSESSMENT_SCANNER_ENABLED: {}
          f:OPERATOR_SBOM_GENERATION_ENABLED: {}
          f:OPERATOR_SCANNER_REPORT_TTL: {}
          f:OPERATOR_SCAN_JOB_RETRY_AFTER: {}
          f:OPERATOR_SCAN_JOB_TIMEOUT: {}
          f:OPERATOR_SCAN_JOB_TTL: {}
          f:OPERATOR_SEND_DELETED_REPORTS: {}
          f:OPERATOR_VULNERABILITY_SCANNER_ENABLED: {}
          f:OPERATOR_VULNERABILITY_SCANNER_SCAN_ONLY_CURRENT_REVISIONS: {}
          f:OPERATOR_WEBHOOK_BROADCAST_CUSTOM_HEADERS: {}
          f:OPERATOR_WEBHOOK_BROADCAST_TIMEOUT: {}
          f:OPERATOR_WEBHOOK_BROADCAST_URL: {}
          f:TRIVY_SERVER_HEALTH_CHECK_CACHE_EXPIRATION: {}
        f:metadata:
          f:annotations:
            .: {}
            f:argocd.argoproj.io/tracking-id: {}
          f:labels:
            .: {}
            f:app.kubernetes.io/instance: {}
            f:app.kubernetes.io/managed-by: {}
            f:app.kubernetes.io/name: {}
            f:app.kubernetes.io/version: {}
            f:helm.sh/chart: {}
  selfLink: /api/v1/namespaces/devsecops-trivy-operator/configmaps/trivy-operator-config
data:
  CONTROLLER_CACHE_SYNC_TIMEOUT: 5m
  OPERATOR_ACCESS_GLOBAL_SECRETS_SERVICE_ACCOUNTS: 'false'
  OPERATOR_BATCH_DELETE_DELAY: 10s
  OPERATOR_BATCH_DELETE_LIMIT: '10'
  OPERATOR_BUILT_IN_TRIVY_SERVER: 'false'
  OPERATOR_CACHE_REPORT_TTL: 48h
  OPERATOR_CLUSTER_COMPLIANCE_ENABLED: 'true'
  OPERATOR_CLUSTER_SBOM_CACHE_ENABLED: 'false'
  OPERATOR_CONCURRENT_NODE_COLLECTOR_LIMIT: '1'
  OPERATOR_CONCURRENT_SCAN_JOBS_LIMIT: '5'
  OPERATOR_CONFIG_AUDIT_SCANNER_ENABLED: 'true'
  OPERATOR_CONFIG_AUDIT_SCANNER_SCAN_ONLY_CURRENT_REVISIONS: 'true'
  OPERATOR_EXPOSED_SECRET_SCANNER_ENABLED: 'false'
  OPERATOR_HEALTH_PROBE_BIND_ADDRESS: ':9090'
  OPERATOR_INFRA_ASSESSMENT_SCANNER_ENABLED: 'true'
  OPERATOR_LOG_DEV_MODE: 'false'
  OPERATOR_MERGE_RBAC_FINDING_WITH_CONFIG_AUDIT: 'false'
  OPERATOR_METRICS_BIND_ADDRESS: ':8080'
  OPERATOR_METRICS_CLUSTER_COMPLIANCE_INFO_ENABLED: 'true'
  OPERATOR_METRICS_CONFIG_AUDIT_INFO_ENABLED: 'false'
  OPERATOR_METRICS_EXPOSED_SECRET_INFO_ENABLED: 'false'
  OPERATOR_METRICS_FINDINGS_ENABLED: 'true'
  OPERATOR_METRICS_IMAGE_INFO_ENABLED: 'false'
  OPERATOR_METRICS_INFRA_ASSESSMENT_INFO_ENABLED: 'false'
  OPERATOR_METRICS_RBAC_ASSESSMENT_INFO_ENABLED: 'false'
  OPERATOR_METRICS_VULN_ID_ENABLED: 'false'
  OPERATOR_PRIVATE_REGISTRY_SCAN_SECRETS_NAMES: '{}'
  OPERATOR_RBAC_ASSESSMENT_SCANNER_ENABLED: 'false'
  OPERATOR_SBOM_GENERATION_ENABLED: 'false'
  OPERATOR_SCANNER_REPORT_TTL: 20h
  OPERATOR_SCAN_JOB_RETRY_AFTER: 30s
  OPERATOR_SCAN_JOB_TIMEOUT: 5m
  OPERATOR_SCAN_JOB_TTL: ''
  OPERATOR_SEND_DELETED_REPORTS: 'false'
  OPERATOR_VULNERABILITY_SCANNER_ENABLED: 'false'
  OPERATOR_VULNERABILITY_SCANNER_SCAN_ONLY_CURRENT_REVISIONS: 'false'
  OPERATOR_WEBHOOK_BROADCAST_CUSTOM_HEADERS: ''
  OPERATOR_WEBHOOK_BROADCAST_TIMEOUT: 30s
  OPERATOR_WEBHOOK_BROADCAST_URL: ''
  TRIVY_SERVER_HEALTH_CHECK_CACHE_EXPIRATION: 10h

</user_query>


По этому `ConfigMap` видно, что проблема уже не в настройках.

Факт:
- `OPERATOR_METRICS_BIND_ADDRESS: ':8080'` есть
- `OPERATOR_METRICS_FINDINGS_ENABLED: 'true'` уже включен
- `OPERATOR_CLUSTER_COMPLIANCE_ENABLED: 'true'` тоже включен

То есть конфиг применился корректно. Если на `/metrics` все равно нет `trivy_*`, значит дальше гипотезы такие:

1. В кластере сейчас нет самих report-ресурсов, из которых строятся метрики.
2. Есть report-ресурсы, но оператор их не экспортирует из-за бага/состояния reconcile.
3. Смотришь не на тот pod/инстанс, хотя по `controller_runtime_*` это похоже именно он.

Что проверить следующим шагом:

```bash
kubectl get clustercompliancereports.aquasecurity.github.io
kubectl get configauditreports.aquasecurity.github.io -A
kubectl get infraassessmentreports.aquasecurity.github.io -A
```

Если `ClusterComplianceReport` есть, то для CIS я бы ожидал хотя бы `trivy_cluster_compliance`.

Если отчеты есть, а `trivy_*` все равно нет, тогда смотреть логи оператора:

```bash
kubectl -n devsecops-trivy-operator logs deploy/trivy-operator
```

И отдельно я бы проверил прямо наличие серий в endpoint так:

```bash
curl -s http://localhost:38113/metrics | grep '^trivy_'
```

Вывод на сейчас такой: `values` и `ConfigMap` уже правильные, надо дебажить не Helm, а наличие `*Report` ресурсов или поведение самого оператора. Если хочешь, я помогу тебе составить короткий чек-лист `kubectl` команд именно для этого кейса.

It seems like the user didn't specifically request to run commands; they might be looking for more of an interpretation instead. I could definitely provide the exact commands if necessary. I’m also thinking about mentioning whether there are any CRs, but without applicable metrics, which makes me wonder if there might be a bug or compatibility issue with appVersion 0.25.0 or chart version 0.27.0. It's all quite intriguing!


<user_query>
я переключился в нужный контекст, проверь
</user_query>


Проверю прямо из текущего kubectl-контекста: есть ли отчеты `Trivy` и появились ли `trivy_*` серии на endpoint.


Контекст правильный, и отчеты точно есть. Добью до причины: посмотрю, какой образ реально запущен и нет ли в логах намека, почему report-метрики не публикуются.

**Inspecting deployment details**

I’m thinking about running more checks on the pods, as the user asked for verification. I'll definitely use `kubectl logs` to inspect pod arguments and environment variables. It seems like a concise update would be useful. I should check deployment images and environment settings in `kubectl`, since just looking at config maps might not provide enough info. It might be worth noting the pod restart times too. I’ll run the current pod image and grep for metrics in the logs.


Похоже, это уже не вопрос контекста: крутится `trivy-operator:0.25.0`, отчеты есть, но endpoint все равно не отдает report-метрики. Проверю еще, нет ли у них в этом билде других имен или только runtime-метрики.

**Evaluating metrics and versioning**

I need to verify the app version, which is noted as 0.25.0 in the documentation. I’m wondering if metrics reporting is tied to this version, especially since previous versions had them. I recall that there might only be info metrics when OPERATOR_METRICS_FINDINGS_ENABLED is true. I wonder if the naming convention for metrics has changed, so I’ll look for “compliance” in the curl output and inspect for relevant terms like “audit” or “vulner.”


Проверил: контекст правильный, отчеты есть, но причина найдена.

Сейчас в живом pod `trivy-operator` переменная все еще старая:

- `ConfigMap`: `OPERATOR_METRICS_FINDINGS_ENABLED='true'`
- `pod env`: `OPERATOR_METRICS_FINDINGS_ENABLED=false`

Из-за этого на `/metrics` нет `trivy_*` серий, хотя `ClusterComplianceReport`, `ConfigAuditReport` и `InfraAssessmentReport` в кластере уже есть.

Значит нужен рестарт pod/deployment, чтобы он перечитал `ConfigMap`. Достаточно:

```bash
kubectl -n devsecops-trivy-operator rollout restart deploy/trivy-operator
kubectl -n devsecops-trivy-operator rollout status deploy/trivy-operator
```

После этого проверить:

```bash
kubectl -n devsecops-trivy-operator exec deploy/trivy-operator -- printenv | rg OPERATOR_METRICS_FINDINGS_ENABLED
curl -s http://localhost:38113/metrics | rg '^trivy_'
```

Ожидаем:
- `OPERATOR_METRICS_FINDINGS_ENABLED=true`
- появятся `trivy_cluster_compliance`, `trivy_resource_configaudits`, `trivy_resource_infraassessments`

Если хочешь, я дальше помогу интерпретировать вывод после рестарта.


<user_query>
http://localhost:46733/metrics
</user_query>


Проверю новый endpoint после рестарта и посмотрю, появились ли `trivy_*` серии.


Endpoint снова отвечает, но надо уже точно понять, что там с `trivy_*`: вывод ушел в файл, сейчас достану оттуда сами серии и при необходимости сверю env у текущего pod.


Теперь все ок: метрики появились.

На `http://localhost:46733/metrics` уже есть:
- `trivy_cluster_compliance`
- `trivy_resource_configaudits`

И live env тоже правильный:
- `OPERATOR_METRICS_FINDINGS_ENABLED=true`

По CIS прямо сейчас видно:
- `CIS Kubernetes Benchmarks v1.23`: `Fail=6`, `Pass=110`
- `NSA`: `Fail=4`, `Pass=23`
- `PSS Baseline`: `Fail=1`, `Pass=10`
- `PSS Restricted`: `Fail=1`, `Pass=16`

То есть задача “включить метрики, чтобы дефолтный дашборд ожил” выполнена.

Я бы дальше сделал только одно практическое замечание: `metricsClusterComplianceInfo` у вас включен, но для постоянной эксплуатации он может давать лишнюю cardinality. Если дефолтному дашборду хватает summary-метрик, потом можно отдельно решить, оставлять его или выключить.

Если хочешь, я следующим сообщением дам готовые PromQL-запросы для быстрого sanity-check в Grafana/VM UI.


<user_query>
получил ответ от тимлида
Anton Fedorov, [10.03.2026 16:54]
Скорее сбор результатов, с CRD, которые триви операторы генерят

Anton Fedorov, [10.03.2026 16:55]
Тоесть они сканируют ноды и выдают результаты

Anton Fedorov, [10.03.2026 16:55]
И хочется их результаты каким-то образом собирать

Ruslan Piatrovich, [10.03.2026 16:56]
аааа

Ruslan Piatrovich, [10.03.2026 16:56]
это совсем меняет дело

Ruslan Piatrovich, [10.03.2026 16:56]
теперь понял задачу :)
</user_query>


Да, это реально меняет задачу.

Тогда `/metrics` самого `trivy-operator` это только побочный контур для health/summary, а основная задача теперь такая: забирать результаты из CRD, которые оператор создает после сканов.

Я бы предложил такую формулировку решения:

- Источник истины: CRD `Trivy Operator`
  - `ClusterComplianceReport` для CIS/NSA/PSS
  - `ConfigAuditReport`
  - `InfraAssessmentReport`
  - при необходимости `VulnerabilityReport`
- Способ сбора: отдельный collector, который читает эти CRD через Kubernetes API и нормализует результаты
- Что отдавать наружу:
  - в Prometheus/VM агрегированные метрики для дашбордов и алертов
  - при необходимости в отдельное хранилище raw-результаты для истории и drill-down

Я бы не делал ставку только на `kube-state-metrics`, потому что у `Trivy` CRD довольно вложенная структура, и для полных результатов это быстро станет неудобно. Для MVP он еще терпим, но для нормального сбора результатов лучше свой collector.

Практически я бы предложил 2 контура:
- `/metrics` `Trivy Operator` оставить для технического мониторинга самого решения
- основной сбор результатов делать из CRD

Для CIS MVP можно начать вообще только с `ClusterComplianceReport`:
- читать `report.summary`
- строить `pass/fail` по benchmark
- потом, если нужно, углубляться до `controls/checks`

Если хочешь, я следующим сообщением могу сразу собрать тебе:
1. короткое техпредложение для тимлида,
2. целевую схему collector-а,
3. список CRD-полей, которые имеет смысл забирать в первую очередь.

**Exploring CRD data collection**

I'm considering a convenient way to collect results from CRDs, treating them as the source of truth. I think building a custom collector or using kube-state-metrics could work well. Wait, kube-state-metrics can expose custom resource metrics, which is a neat approach to gather data without needing to write an exporter. The user mentioned collecting results from CRDs generated by the Trivy Operator. I should suggest using Prometheus scrape for operator metrics or kube-state-metrics to expose CRD status fields effectively.
**Considering custom collectors for CRDs**

I'm thinking about setting up a custom collector or ETL to monitor CRDs and send normalized events or results to a database like ClickHouse or Victoria logs. This approach seems better for detailed storage of checks across nodes. If the team lead is interested in "сбор результатов," they probably want to analyze historical data from CRDs. I should emphasize how this setup could aid in effective storage and analysis for long-term insights.


<user_query>
техпредложение не нужно
задача полностью на мне, нужно реализовать продакшен решение с деплоем вместе с триви через арго и правильный дашборд графаны
</user_query>


Сначала быстро изучу, как у вас в репозитории принято деплоить `Grafana` dashboards и небольшие exporters/collectors через `Argo`, чтобы встроить решение в существующий паттерн, а не городить отдельную схему.


Теперь нужно выбрать правильную реализацию, а не просто “что-то работающее”. Я быстро проверю, можно ли обойтись production-ready компонентом без написания собственного образа: поднять рядом с `Trivy` отдельный `kube-state-metrics` для его CRD и строить Grafana на этих метриках.

**Evaluating collector implementation**

I need to keep moving forward with the implementation. I need to inspect relevant files and decide on the collector implementation. There’s the option to implement a sidecar or container within the trivy chart using kube-state-metrics custom-resource-state. However, we require a production solution. Writing a custom exporter could be more work but is doable. I should explore runtime patterns like shell or Python for building a simple exporter container. Also, I need to check if kube-state-metrics exists in the cluster or repo.
**Exploring dashboard options**

I'm thinking about using Grafana dashboards directly from the kube API, but it seems better to utilize kube-state-metrics custom resource state instead. I should inspect the docs, especially since customresourcestateconfig supports features like labelsFromPath for complex nested arrays. Kube-state-metrics v2+ can extract path and labels, and using it could expose relevant compliance fields. Deploying kube-state-metrics scoped to Trivy CRDs might be easier than building a custom exporter, especially since the team lead specifically asked for metrics derived from CRDs. I need to check the repo for existing kube-state-metrics usage and dependency options.
**Considering kube-state-metrics options**

I'm contemplating whether to vendor kube-state-metrics as a subchart. It might be simpler to deploy kube-state-metrics independently, using either Bitnami or a registry.k8s image. However, the repo probably expects static manifests from the Helm chart. I could create our own Deployment, Service, RBAC, and configmap while referencing the kube-state-metrics image and using the `--custom-resource-state-config-file=/etc/ksm/customresourcestate.yaml`. This approach would keep it all organized while ensuring we're aligned with repo expectations.
**Evaluating dashboard options**

I’m considering if a compliance summary from the ClusterComplianceReport might be sufficient for CIS. However, the raw results across CRDs could be useful too. A production solution might need to collect multiple CRDs, and I need to focus on building a Grafana dashboard that emphasizes both CIS and generic severity counts from config audit and infra assessment. The current default dashboard uses Trivy metrics, but I need to check the JSON to understand the panels better. I’ll also explore the existing dashboard templates.


В репозитории уже виден сильный паттерн с `custom-*-kube-state-metrics`, так что это выглядит как правильная база для production-решения без собственного образа. Сейчас найду конкретный референс по `custom resource state` и по нему соберу реализацию для `Trivy` CRD.

**Inspecting custom KSM**

I need to look into existing custom Kube State Metrics (KSM), possibly focusing on Strimzi's custom version. First, I’ll search for details regarding how Strimzi implements KSM to understand its structure and functionality. I'll gather insights on its metrics and customizations, which will help in evaluating its effectiveness in managing Kubernetes state. Let’s make sure I collect useful information to assist in whatever task follows!


I need to inspect some relevant examples. I’ll search within the fetched document for things like arrays and labels from paths. It’s essential to gauge each of these elements to understand their usage properly. I’m thinking this is a good approach to clarify what I’m looking for and ensure I get accurate information. Let’s take this step to make sure everything makes sense!


Сейчас доберу фактическую структуру живых `Trivy` CRD из кластера, чтобы не строить конфиг `kube-state-metrics` по одной лишь схеме CRD и сразу попасть в реальные поля для дашборда.

**Investigating report details**

I need to understand numeric coercion from booleans, which sounds good. I should inspect the actual ClusterComplianceReport objects to find out the values in the summaryReport field so I can map controls. Using `kubectl get` to retrieve one as YAML could work. It seems the config audit report's metadata labels may include owner references. I’ll also check metric labels from metadata.labels, possibly including trivy labels. Let’s query some small examples to see what I can find!


**Summarizing report parts**

I need to read the cluster comp report, but it seems like it might be too large. It’s a good idea to break it into smaller portions for a more manageable summary and ensure I can perform a control check effectively. I want to make sure I capture the key details without feeling overwhelmed by the size of the entire document. That will help me get a clearer understanding!


**Locating report lines**

I need to find the summary report lines around line 893. It seems like looking at line 890 and onwards should give me the necessary context. I wonder if I can gather enough information from that range to ensure I cover everything important. It might require revisiting a few sections, so I'll keep an eye out for any details that could be crucial as I go through them. Let’s see what I can find!


**Searching for totalFail**

I'm considering summarizing reports, especially focusing on the lines related to "totalFail." It seems like there's something specific I need to gather there. I'll make sure that I’m thorough in my search and identify the right information to pull together. Since it looks like this might be important, I want to ensure I'm capturing all critical points to create an informative summary later on. Let's dig into those details!


**Considering summary report needs**

I'm thinking that perhaps there's an absence of a summary report, which could be a little frustrating. It seems like I need to make sure I provide a clear bottom line or conclusion to help the user better understand. Maybe I'll focus on summarizing key points and providing something actionable. Getting the main takeaways into a concise report could help clarify things for everyone involved. I'll work on that!


Реальная структура CRD уже понятна: для `ClusterComplianceReport` есть готовые `pass/fail` summary и детальный список контролей, для `ConfigAuditReport`/`InfraAssessmentReport` есть summary по severity. Сейчас соберу из этого минимальный, но production-пригодный набор метрик и шаблон деплоя рядом с `Trivy`.

**Considering dashboard updates**

I'm thinking about possibly replacing the existing default dashboard with a new one. But maybe it’s better to add a second dashboard called trivy-crd-results-dashboard. This way, we keep the existing one, which is based on trivy metrics, just in case it’s still useful. The user wants a dashboard specifically for CRD results, so adding another template and JSON sounds right. I'll also need to explore KSM config and deployment, looking for patterns in custom resource deployment.
**Inspecting Trivy operator details**

I need to check out the Trivy operator namespace helpers and labels. To make sure I have the right style, I might read the helper file and look at the service template as a reference. It could also be useful to review service accounts and RBAC settings, just to ensure everything aligns with the intended configuration. I want to make sure I’m thorough in this process!


**Evaluating deployment strategies**

I need to figure out if using the same chart app version is necessary, but it's not crucial for now. Understanding the differences between production and staging environments could help; I need to edit both. When deploying with Trivy via Argo, I have to consider the necessary image config values. There’s a decision to make regarding names and possibly adding a new section for collector metrics. I might want to read the kube-state-metrics CLI args for enabling custom resources and ensuring proper access through RBAC. This involves getting the right permissions across all namespaces. It seems getting a comprehensive solution might be key here.
**Deciding on metrics for the dashboard**

The dashboard may focus on three primary metrics, but the team lead wants to ensure "they scan nodes and output results." It seems I can start with the enabled ones: config audit, infra assessment, and cluster compliance, while keeping vulnerabilities and RBAC disabled for now. I should consider configuring the metrics to potentially include all generated ones since operator values can differ by cluster. For future-proofing, it might be wise to include everything, but the dashboard should emphasize what's relevant. Inspecting all CRD names under the Trivy chart sounds essential, perhaps using a glob method.


**Examining result status**

I'm considering that each result can contain multiple failed checks for various targets. Maybe the control status is derived from the result status? It seems there is a `status:` field in the result level of the CRD schema, but I didn’t check if it appears in live YAML entries. I might want to search for "status:" in the results, as it could likely be missing. Using success booleans might help count failures by target or check, but it's not straightforward at the control level.
**Implementing KSM Config**

I need to figure out the best way to implement the KSM config. I’m looking into using either bitnami/kube-state-metrics or registry.k8s.io/kube-state-metrics/kube-state-metrics — I wonder if the image is accessible. The current repository relies on docker-security.art.lmru.tech as a global registry for mirroring, so I need to align with that policy.

Maybe I should check if kube-state-metrics is mirrored anywhere. It seems safer to use the registry.k8s.io image but there’s no guarantee.


Чтобы не выдумывать версию образа для нового collector-а, возьму из кластера уже используемый `kube-state-metrics` image/tag и переиспользую его же для `Trivy`-сборщика.

**Searching for pods**

I’m thinking about using kubectl, and maybe I’ll need to check the namespace d8-monitoring. I wonder if I should search for a specific pod too. I could use the command `kubectl get deploy -A` and perhaps pipe it through something like `rg kube-state-metrics` to filter. There seems to be a lot of options here, so I'm considering how to structure my search effectively. Let's go with that approach!


**Investigating deckhouse images**

I'm finding the deckhouse and frontend images to be a bit strange. They might be custom images that aren't very useful. I should probably check the container names and arguments to figure out which version I'm dealing with. Maybe I can use the bitnami image, though I'm unsure if it needs to be pinned. It seems like it could also involve kube-state-metrics and kube-rbac-proxy, so I'll need to inspect the deployment containers' names and arguments.


**Evaluating image reuse**

I'm considering whether I can reuse the deckhouse kube-state-metrics image, but it references a specific digest from a wrapper, which isn't ideal. It might be better to go for the upstream version from `registry.k8s.io`, ensuring it matches deckhouse's argument style. I’ll need to query the version, maybe using `kubectl exec` to check the metric. It could also be a good idea to read the startup logs for the version, but I really want to focus on selecting a pinned version for consistency.
**Assessing deployment methods**

I'm considering whether to deploy using an external public chart, but that might not be the best option. Alternatively, I could use the native trivy metrics, which are already proven to work. The requirement mentions something about using CRDs, but summary metrics can come from there too. The team lead wants results from CRDs, so kube-state-metrics could fit. I'll need to see if I can extend the current deckhouse metrics. Since the user wants to deploy with trivy through Argo, I might consider adding regex annotations for remote writing, but that may not be necessary if the custom-target includes it. Let's see if the regex includes the right patterns for remote writing and if Grafana can access local Prometheus.
**Inspecting Grafana configuration**

I'm considering that if the global Grafana uses VM remote-write, I might need to check the job regex, but I'm not really sure how that will play out. The existing trivy dashboard is likely on the local cluster, which is fine for now. However, I'm curious if the dashboards are set up on the deckhouse Grafana and reading from the local Prometheus. I should inspect the dashboard definition kind path in common/trivy to confirm since there is already a local dashboard in place.
**Considering dashboard updates**

I'm contemplating adding a second dashboard template. It might make sense to update the default dashboard to use the new custom kube-state-metrics job metrics instead of the direct Trivy metrics. However, this will require redoing all existing panels based on `trivy_*`. Adding a new JSON for the Trivy dashboard seems simpler. The user might prefer a correct dashboard, so perhaps replacing the old one with a clearer name would help reduce confusion. I also need to think about the KSM configuration.
**Planning KSM deployment**

I'm thinking about deploying the collector alongside the chart, and I might need to set `collector.enabled` and `collector.dashboard.enabled`. I’ll require values for the image repository and tag, resources, RBAC, scrape annotations, and probably use the `devsecops-trivy-operator` namespace. Since the reports are cluster-wide, I need a cluster role. It seems sensible to mount the config file for the KSM. I'll also consider options like `--custom-resource-state-only=true` to avoid default metrics. Ensuring the configuration is secure will also be essential.
**Choosing metric naming**

I'm considering using a metric naming prefix, and "trivy" seems like a good choice. It’s concise and directly relates to the Trivy tool, which helps maintain clarity in what the metrics represent. This could help organize the metrics better and make them easier to identify in the dashboard. I should think about how the prefix fits with existing naming conventions and if there are any potential conflicts. Keeping consistency in naming is always important!
**Exploring metrics configuration**

I’m looking at the ksm config where we can set `metricNamePrefix: trivy` for metrics like `trivy_clustercompliance_pass_total`. It's better to use intuitive names for clarity.

For a cluster compliance summary, I see things like `report_name`, `compliance_id`, etc. under resources, along with metrics. I’m curious about the syntax for paths targeting objects.

I'll need to check the documentation for examples to clarify how the `valueFrom` works, especially regarding the object target syntax.


I'm thinking about needing an object example, possibly focusing on `valueFrom[]`. I could consider using `labelFromKey` in conjunction with an object key. It seems like there are multiple ways I can approach this; I'm just not sure which will be the most efficient or clear for the user. It’s all about finding the right balance for clarity and effectiveness, so I’ll explore these options further to solidify my understanding!


**Exploring metrics options**

I'm considering whether to use one metric per result item, possibly including status. A path array might be useful here, but I'm unsure if using `StateSet` is safe. For the dashboard on the CIS benchmark, I think a pass/fail summary, with added metrics for failed percentages and freshness, could be right. Then again, the team lead wants results from CRDs, so I wonder if I should include `metadata.creationTimestamp`. Is using `Gauge` for freshness a good idea? I have some doubts about that approach.
**Creating a Grafana dashboard**

I’m thinking about how to set up dashboard queries based on a custom job name. Since the metrics have a custom prefix, maybe I don’t need job filtering at first. I could use a variable like `job="$job"`, but GrafanaDashboardDefinition might be simpler without variables right away. I’m considering crafting a minimal JSON for the dashboard manually. I’ll include some panels like CIS pass count, fail count, compliance percentage, and maybe decide between a bar gauge or time series for one of them.
**Designing compliance frameworks in Grafana**

I’m considering how to set up a table for compliance frameworks. I might use a bar gauge to sum metrics by severity, a top 10 table for failed configaudit resources, and another for top infrastructure assessment resources. For freshness, I could calculate with `time() - max(...)`.

I’m also pondering whether to include variables for namespace and job, given the stable custom collector job. If Grafana is aggregating multiple clusters, it might make sense to add a `kubernetes` template variable for accuracy. I'll check existing dashboards for this setup.
**Exploring dashboard templating**

I'm thinking about using rg in another dashboard, but it might not be necessary. It seems like inspecting a repo dashboard for standard templating could give me valuable insights. I should look into the Trivy dashboard JSON to see how they handle templating. That might help me decide whether I need to implement it in my own dashboard or if I can keep it simpler without rg. Happy to explore these options!


Остался важный технический нюанс: pin образа. Сначала достану версию `kube-state-metrics` из уже работающего инстанса в кластере, чтобы не тащить случайный тег в production.

**Considering dashboard requirements**

I'm looking into creating CustomPrometheusRules, but the user specifically asked for a dashboard, not alerts. However, production solutions often involve alerting. I think it might be out-of-scope for now, but I’m considering a minimal alert rule for stale reports, even though that might be too much. I need to inspect existing dashboard templates and consider using the `exported_namespace` variable, as it aligns with naming conventions in Prometheus and custom resources. For namespaced CRDs, I can set labels correctly.
**Adopting exported namespace**

I'm thinking it's a good idea to use `exported_namespace`, and I might not need to include the `kube_` prefix. With the metricNamePrefix as `trivy`, metrics can be named something like `trivy_clustercompliance_pass_count`. I also need to consider whether to add labels like `scanner` and `scanner_version`, which I can pull from the report. I should create a `collector.name` job using service labels, matching existing patterns, and ensure dashboard queries filter correctly. I’ll start the name with `custom-` for consistency and avoid needing remote-write updates. I’ll check for cluster-level metrics without namespaces, too.
**Considering failed check exposure**

I’m wondering if we can expose the failed check count for each control. It seems possible by using the path results[] and creating a metric for each one. I’d label it with `control_id`, `control_name`, `severity`, and set the value to 1 if there's any failure. However, it does seem a bit tricky. I’ll need to think through the implementation details to make sure it works as intended.


Не смог вытащить версию из контейнера через `exec` из-за distroless-образа, поэтому коротко сниму ее через `port-forward` и сразу закрою туннель.


Нашел лучший способ: порт-форвард напрямую на внутренний `kube-state-metrics` порт без `kube-rbac-proxy`. Так можно достать build info и взять реальный upstream version без догадок.

**Exploring image options**

I’m considering whether I could use the current `trivy-operator` container along with a custom script, but that seems impossible. Maybe I could use busybox to query the existing image label with `kubectl get deploy`. I can also get the image digest from the mirror using `kubectl get deploy -o yaml`. This digest might need manual updates for future releases. I'll focus on using it for ksm, since there are different digests for ksm and kube-rbac-proxy. Let's move forward with adding optional templates.
**Considering file updates**

I’m thinking about adding under templates/collector and how to apply patches for multiple files. It seems using applyPatch for one file per call is tricky, though. Could I use a shell or Python script to handle multiple files? An editing tool would be preferable. Since applying patches to multiple files is okay, I can repeat the apply process. I should also consider updating the chart version since changes typically require an increment. Let’s keep that in mind!
**Considering chart maintenance**

I'm wondering if the repo is still maintained and if I really need to adjust anything. A comment suggests bumping the chart version with changes, which could be important for maintaining best practices, but since the charts are likely path-based due to Argo, it might be less relevant. I have to decide if I should update the README as well. I might run `helm template` to validate the chart and check for syntax errors, ensuring the values file is set correctly. Let's focus on designing those value keys!
**Designing trivyReportExporter configuration**

I'm thinking about the configuration for the `trivyReportExporter`. I’ve got settings like enabling it, specifying the image repository, and setting the service annotations for Prometheus.

For the service name, I could go for `custom-trivy-kube-state-metrics`, and using `fullnameOverride` might help keep things organized. I wonder if I should create a dedicated service account or just reuse the existing Trivy service account, which already has cluster-wide RBAC. While reusing could simplify deployment, a dedicated service account might be better for production to ensure the principle of least privilege, although it adds more templates.
**Considering helper definitions**

I think I need to create a service account and collector name helper. Adding helper definitions might not be necessary, but for consistency, it could be better to include these in _helpers.tpl.

For example, I could define "trivy-operator.reportMetricsName" using a printf format. However, since the fullnameOverride is trivy-operator, the result would be `trivy-operator-kube-state-metrics`, while I want `custom-trivy-kube-state-metrics`.

It might help to separate the resource name from the job label. I'll also consider dashboards and whether to use a common chart.
**Evaluating configurations**

I’m looking at whether the remote-write regex doesn't match custom job names in some clusters. Our custom target label defining `job` seems likely to equal the label value in deckhouse scrape. I think setting it to `custom-trivy-kube-state-metrics` is a good plan. Also, default Trivy dashboard queries likely don’t need filtering on job names since metrics should be unique. We might want to use `prometheus.deckhouse.io/sample-limit: "5000"` for moderate metrics cardinality, possibly increasing it to 15000, but service annotations should suffice.
**Considering health checks and configurations**

I’m wondering if we should add readiness and liveness checks to the `/healthz` port 8081, especially since the official ksm health check is likely on port 8080. Using `/healthz` sounds good! Additionally, I need to specify the args for `--telemetry-port=8081`, which shouldn’t be scraped. I could also look into volume mounting the config file while ensuring the content has proper YAML indentation. Sharing a config map across stages could work since there aren't environment differences. I’ll check where to logically place service monitoring settings and consider including dashboards only if the exporter is enabled.
**Creating dashboards**

I’m considering whether to include both the old and new dashboards. It seems reasonable! I could create the dashboard JSON manually, aiming for something simpler with just a few panels. Exporting might be the easiest option, but handcrafting is fine too! I should also think about using the Grafana 10 fields. Maybe it would be simplest to copy the structure from an existing dashboard and then just replace the panels? That could save time!
**Considering compliance metrics**

I'm exploring the use of an alternative object for cluster compliance metrics. While it's tempting, I think it's not worth the risk. I might also add a `trivy_clustercompliance_info` metric for the title, type, and version, but I'd need clearer labels for compliance, like `name`, `compliance_title`, `compliance_type`, and `compliance_version`. I’m leaning towards using concise label names like `benchmark`, `benchmark_id`, `benchmark_type`, and `benchmark_version`, with metric names like `trivy_cluster_compliance_pass_count` for consistency.
**Considering deployment options**

I might need to use `bitnami/kube-state-metrics` allowed by runtime-audit-engine, but I have to consider if my cluster has egress restrictions. Another possibility is using `docker-security.art.lmru.tech/bitnami/kube-state-metrics`, provided a global mirror exists. I need to identify the naming pattern for that. Searching the repository for `docker-security.art.lmru.tech` images might not be enough. I could set the repository to `bitnami/kube-state-metrics` and the registry to `docker-security.art.lmru.tech`.

Deriving the actual upstream version from the existing deckhouse image seems challenging.
**Exploring service metrics access**

I’m considering whether the build info metric is accessible through the existing service’s main metrics via cluster curl without authentication. It might be possible to port-forward and use plain HTTP, but I’m realizing kube-rbac-proxy uses HTTPS, which could complicate things. If I try to use curl with the `-k` option, I might run into errors. I need to think about how to navigate those potential hurdles to access the metrics I need.
I'm considering how to connect using `openssl s_client`, but maybe that's not the right route. I’m thinking about port-forwarding 8080 to a proxy with plain HTTP, since liveness suggests HTTPS. I got a strange SSL35 response from curl -k, possibly because the proxy expects a client cert. I might try port-forwarding directly to pod port 8081 instead and use curl with plain HTTP. That could simplify things by skipping TLS. Sounds like a great plan!


**Searching for version lines**

I think I need to find information on version lines since there might not be any build info metric available. It's curious how versioning can sometimes be a bit tricky, and I wonder what specific details I’ll uncover by searching for those lines. I’ll proceed to look for that information to clarify things and hopefully get everything sorted out!


**Exploring deployment options**

I'm considering the best approach to query the version of the kube-state-metrics, but I might not need the exact upstream version if I'm using the same image digest. I think a straightforward solution would be to deploy using the current image digest in the deckhouse repo. This would allow me to revisit if there's an update, and writing the digest from the live cluster into the repo could work well too. I might move forward with the current cluster's digest approach.
**Checking image access**

I’m wondering if the namespace devsecops-trivy-operator can pull images from the specified Docker registry. It’s likely that it won't be able to without the correct imagePullSecrets. I might need to add an optional `imagePullSecrets` value, especially if the registry requires the `deckhouse-registry` secret, which might not be present. I think I should verify if that secret exists in the trivy namespace, so I'll check using the command `kubectl get secret -n devsecops-trivy-operator deckhouse-registry`.


Проверю бинарь напрямую без шелла: если повезет, контейнер отдаст свой version string, и тогда можно будет зафиксировать нормальный upstream tag.


План реализации такой:

1. Встроить в chart `Trivy Operator` отдельный `kube-state-metrics`-collector, который читает `Trivy` CRD через Kubernetes API.
2. Снимать из CRD production-safe метрики:
   - `ClusterComplianceReport`: `pass/fail` и freshness
   - `ConfigAuditReport`: counts по severity и freshness
   - `InfraAssessmentReport`: counts по severity и freshness
3. Экспортировать их через отдельный scrape target в стиле репозитория: `custom-trivy-kube-state-metrics`.
4. Добавить отдельный Grafana dashboard, который работает именно от этих CRD-метрик, а не от встроенного `/metrics` оператора.
5. Прогнать `helm template` и lint-проверки на `stage`/`prod`.

Сейчас начну вносить шаблоны и values в `stage`, затем зеркально в `prod`, после этого проверю рендер.

**Evaluating Docker image options**

I need to use version v2.17.0 and consider getting the image from Docker Hub, specifically from Bitnami. However, it might be better to go with the official repo `registry.k8s.io/kube-state-metrics/kube-state-metrics:v2.17.0`, or a mirrored version like `docker-security.art.lmru.tech/kube-state-metrics/kube-state-metrics:v2.17.0`. Since global.image.registry is set to that mirror, I can leverage the chart helper for consistency. If the global registry is configured, it should simplify things and reduce errors. I think this should work!
**Refining template logic**

I've got the example where `{{ $registry := include "global.imageRegistry" . | default .Values.trivyReportMetrics.image.registry }}` but it returns a blank string. Maybe I need a one-liner like `{{- $registry := include "global.imageRegistry" . | default .Values.reportMetrics.image.registry -}}` since the default treats an empty value as default. If the registry isn’t empty, I should use `printf "%s/%s:%s" $registry .Values...`.

For pulling images, I might need to reuse `.Values.image.pullSecrets`, especially since if the global registry is private, the collector image will need the same secrets. It's fine for stages and prod clusters, but I should check if `reportMetrics.dashboard.enabled` should default to true.

I'll also need to consider compliance labels and ensure the update timestamp exists in the config audit report per RFC3339.


**Planning compliance panels**

I’m working on creating compliance panels for the cluster. First, I’ll gather fail and pass counts, using `sum` queries for each. For the compliance percentage, I realize I can simplify the query using `clamp_min` to ensure I don’t divide by zero. I’m considering using either a bar chart or table for displaying results. While I could go for more complex visuals, going with a simple table may make it easier to interpret the data.
**Evaluating dashboard design**

I'm trying to figure out how to structure the dashboard, specifically for the "Config Audit" row. For critical/high data, I think using a bar chart would be good, and maybe I can create various stat panels for different severity levels. Although, I wonder if having four stat panels might be too many. A simpler design with three sections and three to four panels could work better. I want to ensure the JSON for the layout is easy to manage and keep everything unique, possibly using the `timeseries` panel type for a clean visual.
**Designing the dashboard**

I'm considering how to produce a dashboard with specific stats. I plan to include three key stats: CIS Fail, CIS Pass, and CIS Compliance percentage, alongside tables for compliance frameworks and config audit resources. For severity levels, I'll add rows for Critical, High, Medium, and Low. I also think using only instant queries might be better. Handwriting the JSON seems manageable, and I could use existing dashboard structures as a reference. However, I'm unsure about the exact query object format, so checking templates will help!


Начинаю с шаблонов и хелперов для нового collector-а, чтобы дальше на них опереть `Deployment`, `RBAC` и `Service`.

**Designing dashboard panels**

I see I can maintain a similar variable structure, which is great! I want to name the dashboard something like "Trivy Operator - CRD Results." My aim is to create around 8 panels with specific IDs. I’m wondering if I can simplify by using just the datasource variable? The existing dashboard fixed UID for the main datasource seems useful, so I might stick with that. For the panel config, maybe I should consider `timeseries` or a minimal `table`. The structure for stat panels could be copied from what’s already there, while the table might benefit from using transforms. A raw instant query should display the Metric and Value columns, which seems fine. I like the idea of using `legendFormat`, but for simplicity, it might be easier to just go with the table.
**Building dashboard panels**

I’m creating panels that include a row for Cluster Compliance with stats for Fail, Pass, and Compliance %. I want to include a table showing failing frameworks using the query for trivy compliance fail counts. Then there's a row for Config Audit, with stats on critical, high, medium, and low counts, plus a table for top config audited resources. For the Infra Assessment row, I’ll again include critical, high, medium, low stats, and a table for top infra resources. I think using the `exported_namespace` variable from trivy_config_audit_high_count is a good move.

I’ll compute the compliance percentage with a specific formula and maybe run a table query to get the top config resources based on certain counts. If there's no data, I think it’ll be okay.
**Adding freshness metrics**

I’m thinking about including a row for freshness in the dashboard. This could feature a stat showing the time since the last update for trivy cluster compliance, calculated with `time() - max(trivy_cluster_compliance_updated{benchmark="$benchmark"})`, measured in seconds. I could also add freshness stats for config audits and infra assessments, which might be helpful. So, I’ll plan to add these three stats at the bottom, and I'll represent their unit as seconds (s).
**Considering configuration settings**

I'm thinking about setting thresholds for percentage values. Maybe green for above 95? For freshness, I could establish thresholds like 0 for green, 21600 for yellow, and 43200 for red. It seems like I have a lot of JSON, but it feels manageable if I keep it minimal. I should create a configuration map and templates using ApplyPatch. It’ll take time, but I’m ready to start working on the helper stage and proceed to production.


Конфиг collector-а делаю на базе `kube-state-metrics custom-resource-state`: это дает reproducible сбор из CRD без собственного кода и хорошо ложится в GitOps.

**Considering compliance updates**

I'm thinking about possibly using `metadata.creationTimestamp` for cluster compliance updates since there isn't an `updateTimestamp`. Including `scanner_version` might be optional. I’m also considering whether to set `commonLabels`, but maybe that’s not needed. I need to note the metric names: `trivy_cluster_compliance_pass_count`, `trivy_config_audit_critical_count`, and `trivy_infra_assessment_critical_count`. That's all looking good, so I’ll add the same setup for both staging and production environments.


**Considering service accounts**

I'm looking at service account configurations, specifically for the cluster role and bindings in both staging and production environments. I wonder if I can combine multiple configurations into one while keeping things clear.

It seems I can create one and then duplicate it as needed. I also think I should keep the commentary concise to avoid verbosity, so I'll focus on adding the stage service account efficiently.


Собираю сам `Deployment` для collector-а: он будет lightweight, с теми же scheduling/security паттернами, что и остальной chart.


**Addressing security context issues**

I’m considering potential issues with the securityContext and its top-level includes, like privileged and readOnly options. Also, I’m thinking about whether the pod level can be empty. I wonder about priorityClassName — is a blank quote acceptable? I think it might be fine as an empty string, but I'm not completely sure. And there’s also the service template to keep in mind. This seems like a complex situation to navigate!


Добавляю отдельный dashboard, а не переписываю существующий: так не ломаем уже развернутую витрину по встроенным `trivy_*` метрикам и получаем новую, правильную витрину именно по CRD-результатам.

**Crafting JSON dashboard**

I'm in the process of creating a dashboard template with JSON files for staging and production. I want to compose this carefully and consider including a datasource variable like before. It might be easier to generate the JSON using a Python script since I don't want to deal with complex shell operations. I'm thinking of creating an identical setup for both stages, but I need to ensure the JSON handles backslashes and quotes properly while keeping it manageable at around 400 lines. I'll focus on unique IDs and a neat layout.
**Configuring dashboard options**

I'm looking to ensure everything is safe for the dashboard options. I think I’ll include settings like `showHeader`, cell height, and a footer that won't show. It might be okay to omit some `fieldConfig.defaults.custom` since it's not necessary. For stat panels, I can copy configurations from existing ones using `reduceOptions.lastNotNull`, and I need to set up thresholds. I'll arrange panel IDs and titles sequentially. For queries, I’ll cover fail counts, pass counts, compliance percentages, freshness, and specific queries for auditing critical issues too.


**Managing JSON Stages**

I’m looking to add a stage and production setup for JSON. It’s a big task, and I need to ensure the JSON is valid. Maybe I can create the stage first and then copy the production. I think keeping it to a manageable size of about 300 lines would help. I need to be careful with the commas! Prioritizing the stage first seems like the right approach before duplicating the production.


Шаблоны уже на месте. Сейчас прогоню `helm template` на `stage` и `prod`, чтобы выловить синтаксис Helm/YAML до коммита и до Argo.

**Investigating string issues**

I’m thinking there might be a potential issue with the `global.imageRegistry` helper returning values that have leading spaces. I should check lines 77-80 in the helper to see if there's any two-space indentation, which might lead to extra whitespace. If that’s the case, an example might return something like `  docker-security...`. I guess I’ll need to define the block clearly without trimming. I wonder if existing templates utilize an include helper. Let’s search for that!


Рендер прошел в `stage` и `prod` без падений. Сейчас быстро проверю, что в итоговом манифесте действительно присутствуют collector, `RBAC` и новый dashboard, а потом добьюсь отсутствия IDE-диагностик.

**Inspecting rendered resources**

I need to inspect rendered resources and possibly check names or see if there's JSON included. It feels like searching in those rendered files for report metrics could be essential. I'm also considering whether I should lint the files as part of this process. Using a tool like `rg` seems like a good idea for these searches. I'll just have to make sure I'm being thorough while keeping things organized!


Делаю еще две быстрые проверки качества: валидность JSON дашборда и наличие ожидаемого `custom-resource-state` конфига в уже отрендеренном манифесте.

I see I might have an issue with the `trivy_cluster_compliance_updated` metric name that's generated from the metric prefix and name. It seems like the metric variable datasource is hardcoded to "main," rather than being a variable. The existing dashboard appears to be the same. Should I add old metrics findings changes? No, that's not necessary. I could inspect the rendered service account name in production too, but it looks the same. I might consider running `helm template` and grepping the `custom-resource-state.yaml` to check the quotes around the label key.
**Evaluating chart updates**

I'm questioning whether a chart version bump is necessary. While changes in charts can be relevant, I'm not sure it's essential right now. However, I need to consider if automation references the chart version. For completeness, it might be good to make sure the existing README values table is updated, even if it’s currently not required. I should also think about running tests or lints, and check the JSON parse against the rendered configmap for any custom resource config.


Имплементация собрана и прошла локальные проверки. Осталось быстро посмотреть итоговый diff, чтобы убедиться, что изменения ограничены только нужным контуром `Trivy + CRD collector + dashboard`.

**Inspecting git status**

I need to check the git status and diff to summarize the changes. It seems like using shell commands for `git status` and `git diff` might be a good approach. Since I’m not committing anything yet, I could consider using parallel commands. I'm feeling pretty optimistic about this! Let's make sure I get a clear view of what has changed without the confusion of committing yet.


Осталось только прибрать временные `port-forward` и можно подводить итог. После этого дам тебе короткий список, что именно проверить уже после Argo sync.


Сделал production-вариант именно под сбор результатов из `Trivy` CRD, а не из `/metrics` самого оператора.

## Что добавил
В `common/trivy-operator/stage` и `common/trivy-operator/prod` появился встроенный рядом с `Trivy` collector на `kube-state-metrics custom-resource-state`:
- `templates/collector/configmap.yaml`
- `templates/collector/deployment.yaml`
- `templates/collector/service.yaml`
- `templates/collector/serviceaccount.yaml`
- `templates/collector/clusterrole.yaml`
- `templates/collector/clusterrolebinding.yaml`

Он публикует отдельный scrape target `custom-trivy-kube-state-metrics` и снимает метрики из:
- `ClusterComplianceReport`
- `ConfigAuditReport`
- `InfraAssessmentReport`

Добавил новый Grafana dashboard:
- `common/trivy-operator/stage/dashboards/trivy-crd-dashboard.json`
- `common/trivy-operator/stage/templates/grafana-crd-dashboard.yaml`
- и то же самое для `prod`

Dashboard строится уже от CRD-метрик:
- `trivy_cluster_compliance_*`
- `trivy_config_audit_*`
- `trivy_infra_assessment_*`

Также добавил настройки collector-а в оба `values-global.yaml` и ранее включенные `metricsFindingsEnabled: true` оставил как есть.

## Что получилось
После деплоя вместе с chart’ом у тебя будет:
- отдельный collector рядом с `Trivy Operator`
- сбор через привычный deckhouse scrape
- отдельный dashboard `Trivy Operator - CRD Results`
- данные по compliance, config audit и infra assessment из CRD

## Проверка
Проверил локально:
- `helm template` для `stage` с `atlas-a-stage-cluster.yaml` проходит
- `helm template` для `prod` с `sextans-a-prod-cluster.yaml` проходит
- JSON dashboard валиден
- IDE/lint ошибок нет

Что не делал:
- не коммитил
- не синкал Argo
- не добавлял alert rules/recording rules

Следующий шаг после sync:
1. Убедиться, что появился target `custom-trivy-kube-state-metrics`.
2. Проверить запросы:
   - `trivy_cluster_compliance_fail_count`
   - `trivy_config_audit_high_count`
   - `trivy_infra_assessment_high_count`
3. Открыть новый dashboard в папке `Security`.

Если хочешь, следующим сообщением могу дать короткий post-sync checklist для проверки уже в кластере/Grafana.


<user_query>
надо убрать старый дашборд на метриках самого триви и добавить ряд с нужными панелями в новый
</user_query>


Сначала быстро посмотрю, что именно сейчас лежит в старом дашборде `Trivy`, и решу, какие панели логично перенести в новый CRD-dashboard, чтобы убрать дубль и не потерять полезную витрину.

**Updating dashboard metrics**

I need to modify the old dashboard by removing it and adding a row with the required panels in the new one. Should I ask the user which panels are needed? It’s also possible to infer that the new dashboard should include what was in the old one. I think I might need to check both dashboards and possibly remove the old YAML and JSON templates. Clarification is essential, though—do I include metrics for operator functions? Maybe I can suggest adding some relevant health source panels for report freshness counts or similar.
**Clarifying dashboard panel needs**

It seems the user may feel that the panels we built are insufficient and want to incorporate certain metrics from the old dashboard into the new one. The old dashboard with Trivy metrics should be removed, but maybe some panels are still useful. I should inspect the old dashboard structure to see if there are compliance panels to include in the new dashboard. Before proceeding, I’ll need to provide some commentary and possibly create a plan to ensure everything aligns correctly.


Уточнение по старому дашборду уже видно: он в основном про встроенные `trivy_*` summary-метрики, и из полезного там для новой витрины не хватает именно breakdown-панелей по namespace/severity. Уберу старый dashboard полностью и добавлю в новый отдельный ряд с этими разрезами для `ConfigAudit` и `InfraAssessment`.

**Evaluating dashboard updates**

I’m considering adding a row for "Resource Breakdown" that includes config audits and infra assessments by both namespace and severity. This seems to align with older panels. The user requested to remove the old dashboard, so I’ll delete the templates and possibly unused files. It might be better to remove both the template and old JSON from stage and prod completely. I may want to update the helm template too. A Python script could help for modifying the JSON, but I should also explore creating a new dashboard instead.
**Planning dashboard updates**

I’m realizing that the same file is used for both stage and prod, so I can update the stage first and then copy it to prod. Since the file is generated in a simple JSON format, I need to read the current dashboard to determine where to insert new rows and panels. I think a new row titled "Findings Distribution" with four table/bar panels would be effective. This row could feature "Config Audit by Namespace," "Config Audit by Severity," "Infra Assessment by Namespace," and "Infra Assessment by Severity." I'll ensure the JSON structure is valid.
**Evaluating file operations**

I need to perform edits and am considering a Python script via shell, but I recall that I shouldn't use the shell for file operations. The developer advised against that and suggested using the ApplyPatch tool for multiple files. I’ll also need the Delete tool for removing old templates. It might be better to update sections with ApplyPatch instead of replacing entire files. However, I wonder if scripting could be more efficient for handling similar JSON files.
**Considering file operation methods**

The developer suggests using the appropriate tools instead of shell for file operations. Applying a patch to a large JSON file could be cumbersome. I’m thinking about replacing the entire file with new JSON for both staging and production, but that sounds massive. Alternatively, I could combine ReadFile and ApplyPatch for specific sections where panels need inserting. Using a Python script via shell seems tempting for efficient modification, but I’m wary since it’s generally not considered appropriate for file operations. Maybe the best route is to delete old files and apply the new JSON with ApplyPatch.
**Exploring file update options**

I’m considering using the Delete tool on the existing file, then adding the entire content as a new file. While this requires two calls per file, it seems acceptable — even if it duplicates a large JSON content across staging and production. Alternatively, I could update the staging file and copy it to production, but I can't use shell commands. Deleting and adding files feels cumbersome since I'll be repeating about 2400 lines in total. It might be better to just delete the production file and read the staging file to add the same content.
**Considering dashboard updates**

I'm thinking about how to avoid duplicating the entire file. It seems I need to insert rows and panels before the Infra row. Maybe I can patch both stage and prod with the same insertion. Should I update the title? It might not be essential. Deleting the old dashboard template and files should be straightforward. I need to inspect the segment in the new JSON where the insertion is intended, probably around lines 730-790.


**Evaluating row positioning**

I need to figure out where to insert a new row before the infra row. The existing infra row is at y 22, with subsequent panels at positions y 23 to 27, while the final table is at y 27. The new row could start at y 22, shifting the infra down by 9 units.

I’m also considering whether to add the row after infra instead. Adding it at the bottom might be easier, avoiding any shifts. I'll inspect the end around position 1120 for clarity.


Сейчас уберу legacy-dashboard совсем из chart’а и добавлю в новый CRD-dashboard отдельный ряд с breakdown-панелями по namespace и severity, чтобы покрыть то, что было полезного в старой витрине.

**Planning panel layout**

Alright, I need to patch an insert row and four panels before closing the panels array. I think unique IDs starting from 21 to 25 could work, with row 21 for panels 22 to 25. The new row can begin at y=35, with each panel at y=36 and a height of about 8. I'll keep a table for a breakdown by namespace and severity, maybe considering a pie chart, but it’s not in the requirements. It seems sticking to tables is best for now.
**Updating dashboard metrics**

It looks like I'm summing by severity, using label_replace for critical and high counts, which should work. For namespace tables, I'll keep it simple with sums by exported_namespace. I might add one row with four tables and need to remove the old dashboard template using the Delete tool. I also want to update stage and production Helm render references since the old template was linked to outdated JSON. Keeping old metrics could be beneficial, but I’ll check if removal is necessary. I’ll consider updating titles or tags as well.
**Editing JSON files**

I need to edit both JSON files to have the same content. I'll use ApplyPatch for inserting before closing the panels array since the content is identical in both. I’ll carefully craft a large insertion with proper commas. It’s important to note that I need a preceding comma after the last object, following panel 20. Currently, it ends with "}" and then "]", so I plan to change it to "}," and then add the row. I’ll make these edits for stage first, then for production.


Старый dashboard уже убран из chart’а. Сейчас прогоню рендер и проверю, что новый dashboard валиден, а ссылок на legacy-файлы не осталось.

**Validating dashboard files**

I need to validate the JSON and the Helm template. It seems like old dashboard files might have been deleted, so I should check for lints and broken references due to missing files or templates. I might need to ensure the dashboard JSON has a title row and validate it properly. There’s also a thought about removing the old metricsFindingsEnabled, even though the user didn’t specifically ask for that. I’ll run the JSON tool and ensure that the Helm template is set for the production stage, without any old dashboard references.


Убрал старый dashboard полностью:
- удалены `templates/grafana-dashboard.yaml` в `stage` и `prod`
- удалены старые `dashboards/trivy-dashboard.json` в `stage` и `prod`

В новый `Trivy Operator - CRD Results` добавил ряд `Breakdown` с панелями:
- `Config Audit by Namespace`
- `Config Audit by Severity`
- `Infra Assessment by Namespace`
- `Infra Assessment by Severity`

Все изменения сделаны и для `stage`, и для `prod`.

Проверил:
- JSON обоих новых dashboard валиден
- `helm template` для `stage` и `prod` проходит
- linter-ошибок нет
- ссылок на старый dashboard в chart’е больше нет

Дальше можно просто синкать `Argo` и смотреть новый dashboard в `Security`.

**Finalizing version control details**

I think I should mention the git status, though maybe it’s not necessary. I could check for a diff summary to ensure the new row titles are present. Additionally, I might want to cite file references for clarity. I should mention that I've removed old files and added a breakdown row. It's also worth noting that I validated the helm template with no lints. A next step could be an Argo sync. Let's make this concise!


<user_query>
проверь еще раз, что все на месте перед пушем
</user_query>


Сейчас сделаю финальный pre-push прогон: проверю diff, отсутствие хвостов от старого dashboard, валидность JSON и рендер `Helm` для `stage`/`prod`.

**Considering command execution**

I'm thinking about using parallel processing for commands like git status, git diff, helm templates, and JSON validation. The user asked about using yamllint, so I should probably check for YAML issues. However, many templates might cause problems with yamllint because of Helm templating. Instead, it could be better to use ReadLints and helm template commands. I might also want to run a search for reportMetrics sections and inspect the rendered output. So, let's go ahead with the parallel execution!


Базовые проверки зеленые. Добьюсь последнего: посмотрю уже отрендеренные манифесты, чтобы убедиться, что collector и новый dashboard реально попадают в итоговый output и в `stage`, и в `prod`.

**Inspecting rendered output**

I need to check if the rendered output contains the expected resources. I'll consider comparing counts, even though the production dashboard shows something odd—one line seems weird, but the JSON is valid. It might be helpful to render the temporary names of the resources. Let's go ahead and do that! I want to make sure everything aligns as it should.


Все на месте, перед пушем выглядит чисто.

Проверил:
- `git status`: только ожидаемые изменения по `common/trivy-operator/stage` и `prod`
- старый dashboard полностью убран:
  - нет `trivy-dashboard.json`
  - нет `trivy-operator-dashboard`
- новый dashboard валиден в `stage` и `prod`
- `helm template` проходит для:
  - `stage` с `atlas-a-stage-cluster.yaml`
  - `prod` с `sextans-a-prod-cluster.yaml`
- в render-output реально присутствуют:
  - `trivy-operator-kube-state-metrics`
  - `trivy-operator-kube-state-metrics-config`
  - `custom-trivy-kube-state-metrics`
  - `trivy-operator-crd-results-dashboard`
- в render-output есть нужные `metricNamePrefix`:
  - `trivy_cluster_compliance`
  - `trivy_config_audit`
  - `trivy_infra_assessment`
- linter-ошибок нет

Итог: перед пушем проблем не вижу.
Пуш не делал.

**Summarizing the report**

I need to create a final concise report, and I should note that no push was performed. There's a subtle aspect to mention: the production dashboard file is minified into one line, but it's still JSON valid, which is normal for this context. I think it’s important to clarify that everything is clear overall. Keeping it straightforward will probably help the user understand the current status easily!


<user_query>
на dhi.io актуальный 2.18.0
сейчас так

rpc error: code = NotFound desc = failed to pull and unpack image "docker-security.art.lmru.tech/kube-state-metrics/kube-state-metrics:v2.17.0": failed to resolve reference "docker-security.art.lmru.tech/kube-state-metrics/kube-state-metrics:v2.17.0": docker-security.art.lmru.tech/kube-state-metrics/kube-state-metrics:v2.17.0: not found
вот так в хабе docker pull dhi.io/kube-state-metrics:2.18.0
в виртуальную репу артифактори docker-security ремоут docker-remote-dhi-io я добавил

</user_query>


Понял, тогда поправлю image reference под ваш Artifactory-virtual: переведу collector на `2.18.0` и зашью repository так, чтобы с учетом `global.image.registry` рендерился путь через `docker-security`.

**Analyzing Docker image pull**

I need to pull the kube-state-metrics image version 2.18.0 from Artifactory. The current error indicates that the image version 2.17.0 isn't found, so I may need to check the upstream hostname. Since the user provided a specific pull command, it seems I should include the remote host in the repository setup. It may also be wise to update reportMetrics.image.repository to "dhi.io/kube-state-metrics" with the tag 2.18.0 for proper referencing.
**Refining Docker setup**

I'm working through finalizing the Docker setup. So, it looks like the final image path is going to be docker-security.art.lmru.tech/dhi.io/kube-state-metrics:2.18.0. I might need to remove reportMetrics.image.registry since I'm using the global registry, which is currently set to registry.k8s.io. I want to ensure the repository is "dhi.io/kube-state-metrics" with the tag "2.18.0". I'll also update both stage and prod values and provide commentary on any edits. Then, I’ll run helm template and validate with the tools I have.


Правку внес. Сейчас быстро проверю итоговый render image path для `stage` и `prod`, чтобы убедиться, что получится именно путь через ваш `docker-security` virtual.


Поправил image для collector-а в `stage` и `prod`:

- `repository: dhi.io/kube-state-metrics`
- `tag: 2.18.0`

Проверил рендер: теперь chart собирает именно такой image:

```text
docker-security.art.lmru.tech/dhi.io/kube-state-metrics:2.18.0
```

Это подтверждено и для `stage`, и для `prod`.
`helm template` проходит, linter-ошибок нет.

То есть текущая проблема с `v2.17.0 not found` закрыта. Можно пушить/синкать.


<user_query>
верни как было с исправлением, вот правильный путь
docker-security.art.lmru.tech/kube-state-metrics:2.18.0
</user_query>


Сделаю точечную правку image path: уберу лишний `dhi.io/` из repository и быстро перепроверю итоговый render.


Правка внесена. Сейчас быстро подтвержу через `helm template`, что итоговый image ровно `docker-security.art.lmru.tech/kube-state-metrics:2.18.0`.


Вернул как нужно.

Теперь в `stage` и `prod` рендерится ровно:
`docker-security.art.lmru.tech/kube-state-metrics:2.18.0`

Проверил:
- `helm template` проходит
- `yamllint`/IDE lint чистые

Можно идти дальше с этим вариантом.


<user_query>
вот так сейча выглядит дашборд в графане
datasource

benchmark

exported_namespace

Cluster Compliance

Failed Checks
12
Passed Checks
159
Compliance Score
92.98%
Last Compliance Update Age
3 hours
Failed Checks by Framework

Time

__name__

benchmark

benchmark_id

benchmark_type

benchmark_version

customresource_group

customresource_kind

customresource_version

instance

job

name

namespace

pod

service

Value
2026-03-10 18:00:58.690trivy_cluster_compliance_fail_countCIS Kubernetes Benchmarks v1.23k8s-cis-1.23cis1.23aquasecurity.github.ioClusterComplianceReportv1alpha110.111.7.150:8080custom-custom-trivy-kube-state-metricsk8s-cis-1.23devsecops-trivy-operatortrivy-operator-kube-state-metrics-774d4b5fcc-wjgpctrivy-operator-kube-state-metrics62026-03-10 18:00:58.690trivy_cluster_compliance_fail_countNational Security Agency - Kubernetes Hardening Guidance v1.0k8s-nsa-1.0nsa1.0aquasecurity.github.ioClusterComplianceReportv1alpha110.111.7.150:8080custom-custom-trivy-kube-state-metricsk8s-nsa-1.0devsecops-trivy-operatortrivy-operator-kube-state-metrics-774d4b5fcc-wjgpctrivy-operator-kube-state-metrics42026-03-10 18:00:58.690trivy_cluster_compliance_fail_countKubernetes Pod Security Standards - Restrictedk8s-pss-restricted-0.1pss-restricted0.1aquasecurity.github.ioClusterComplianceReportv1alpha110.111.7.150:8080custom-custom-trivy-kube-state-metricsk8s-pss-restricted-0.1devsecops-trivy-operatortrivy-operator-kube-state-metrics-774d4b5fcc-wjgpctrivy-operator-kube-state-metrics12026-03-10 18:00:58.690trivy_cluster_compliance_fail_countKubernetes Pod Security Standards - Baselinek8s-pss-baseline-0.1pss-baseline0.1aquasecurity.github.ioClusterComplianceReportv1alpha110.111.7.150:8080custom-custom-trivy-kube-state-metricsk8s-pss-baseline-0.1devsecops-trivy-operatortrivy-operator-kube-state-metrics-774d4b5fcc-wjgpctrivy-operator-kube-state-metrics1
Config Audit Reports

Critical
0
High
112
Medium
0
Low
0
Last Config Audit Update Age
28 s
Top Config Audit Resources (Critical + High)

Time

exported_namespace

resource_kind

resource_name

Value
2026-03-10 18:00:20.857devsecops-tetragonDaemonSettetragon52026-03-10 18:00:20.857kube-systemPod42026-03-10 18:00:20.857d8-cni-ciliumDaemonSetsafe-agent-updater32026-03-10 18:00:20.857d8-ingress-nginxDaemonSetfailover-cleaner32026-03-10 18:00:20.857d8-monitoringDaemonSetnode-exporter32026-03-10 18:00:20.857d8-systemReplicaSetdeckhouse-74c9f458b832026-03-10 18:00:20.857d8-cloud-provider-yandexDaemonSetcsi-node32026-03-10 18:00:20.857d8-systemReplicaSetdeckhouse-5595bb9bb43
Infra Assessment Reports

Critical
0
High
0
Medium
0
Low
0
Newest Infra Report Age
5 weeks
Top Infra Resources (Critical + High)

Time

exported_namespace

resource_kind

resource_name

Value
2026-03-10 18:00:24.320kube-systemPodkube-scheduler-s-atlas-a-ks-master-002026-03-10 18:00:24.320kube-systemPodkube-controller-manager-s-atlas-a-ks-master-002026-03-10 18:00:24.320kube-systemPodkube-apiserver-s-atlas-a-ks-master-002026-03-10 18:00:24.320kube-systemPodetcd-s-atlas-a-ks-master-002026-03-10 18:00:24.320kube-systemCronJobd8-etcd-backup-c4d1c49fd2b0288fa6786d74629d585960
Breakdown

Config Audit by Namespace

Time

exported_namespace

Value
2026-03-10 18:00:39.221d8-cni-cilium182026-03-10 18:00:39.221kube-system172026-03-10 18:00:39.221d8-monitoring112026-03-10 18:00:39.221d8-system92026-03-10 18:00:39.221lmru-devops-k8s-backup92026-03-10 18:00:39.221charon-system62026-03-10 18:00:39.221devsecops-tetragon62026-03-10 18:00:39.221d8-cloud-instance-manager5
Config Audit by Severity

Time

severity

Value
2026-03-10 18:00:39.221High1122026-03-10 18:00:39.221Low02026-03-10 18:00:39.221Medium02026-03-10 18:00:39.221Critical0
Infra Assessment by Namespace

Time

exported_namespace

Value
2026-03-10 18:00:40kube-system0
Infra Assessment by Severity

Time

severity

Value
2026-03-10 18:00:40.354Low02026-03-10 18:00:40.354Medium02026-03-10 18:00:40.354High02026-03-10 18:00:40.354Critical0

</user_query>


Есть 3 явных недочета перед пушем.

1. Таблицы слишком сырые.
Сейчас Grafana показывает служебные поля `__name__`, `customresource_*`, `instance`, `job`, `pod`, `service`, `namespace`, `Time`. Для пользователя это шум. В таблицах надо оставить только бизнес-поля:
- для compliance: `benchmark`, `fail_count`
- для config audit: `exported_namespace`, `resource_kind`, `resource_name`, `value`
- для infra: `exported_namespace`, `resource_kind`, `resource_name`, `value`

2. `Top Infra Resources (Critical + High)` бесполезен в текущем виде.
Там показываются ресурсы с `0`, потому что `topk()` берет и нулевые серии. Нужно фильтровать:
- либо `(expr) > 0`
- либо `topk(10, expr and expr > 0)` через корректную PromQL-конструкцию
Иначе панель визуально есть, но смысла не несет.

3. Название `Newest Infra Report Age` вводит в заблуждение.
Сейчас метрика строится от `metadata.creationTimestamp`, а не от обновления отчета. То есть это не “последнее обновление”, а скорее “возраст самого свежего отчета”. Панель лучше переименовать в:
- `Newest Infra Report Creation Age`
или
- `Latest Infra Report Created`

В остальном по данным все хорошо:
- compliance считается
- config audit считается
- breakdown работает
- collector живой

Я бы перед пушем еще подкрутил UX:
- скрыть лишние колонки в table panels
- отфильтровать нули в infra top
- переименовать infra age panel
- по желанию заменить `Breakdown` tables на bar chart

Если хочешь, я сейчас сразу внесу эти правки в dashboard.

**Evaluating dashboard findings**

I see that the user shared how the dashboard looks, and I need to assess it, likely identifying issues and suggesting improvements. It seems there are tables that are too raw, which could benefit from organization and transformation. Variables might be empty or the datasource not user-friendly. I should mention the excess infrastructure labels and consider filtering options. Also, using the most recent update timestamp might create more clarity. I'll definitely offer to adjust the dashboard accordingly!
**Assessing dashboard updates**

I noticed that we used "created" in the config since there's no update timestamp in the live sample. However, referring to "Newest Infra Report Age" might be misleading; "Creation Age" would be clearer. This seems like a potential bug.

Also, the zero-value top-k table displays empty rows, which could be cleaned up by filtering out zeros. It’s worth asking if the user wants me to enhance the dashboard. I should summarize findings and suggest reorganizing fields for clarity while ensuring meaningful data is presented. Let's streamline the user experience!
**Refining dashboard elements**

I need to rename "Newest Infra Report Age" for clarity and consider suggesting bar charts instead of tables for better breakdowns. If the user asks for feedback, I might remark that the dashboard is functional but could benefit from some presentation polish. It's also important to note that not all metrics may have clear names. The `exported_namespace` variable only applies to config and infra sections, while benchmark relates specifically to compliance. Let's keep it concise and focused on these findings!


<user_query>
внеси правки
может еще что-то добавить?
</user_query>


Сейчас точечно поправлю UX нового dashboard: почищу таблицы, уберу нулевые строки из infra top и переименую неоднозначную панель. Заодно посмотрю, где есть быстрые улучшения без раздувания витрины.

**Considering JSON edits**

I need to edit the dashboard JSON for both stage and production. Applying a patch might work best, possibly using a Python script to avoid shell operations, since shell isn't recommended for file operations. I need to inspect the relevant panel configs, especially around tables and titles. The production file might be reduced to a single line due to minification. Instead, I could copy the properly formatted stage file to replace the production one. I'll focus on modifying and formatting the stage JSON for this.
**Evaluating JSON patching**

I might be able to use ApplyPatch on both stage and production files. The stage patch should be easier to implement, but with the production file being one line, recreating from stage using ApplyPatch could work. I wonder if I should ask for more input from the user. They mentioned possibly adding something extra. I could consider improvements like a freshness table or an infra filter for failures. Let's review the stage JSON, especially around specific panel configurations and transformations.