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Security ML Integrations

ElasticOfficialCreated by elastic

  • Skill
  • Workflow
  • Cloud and DevOps
  • Apache-2.0
  • No issues found

From the author

Install and manage Elastic security ML integration packages (beaconing, ded, dga, lmd, pad, problemchild). Handles the full lifecycle: pre-flight checks, Fleet package install, component templates, ingest pipelines, transforms, data views, ML anomaly detection jobs, and detection rules. Use when a user asks to install, set up, check the status of, or troubleshoot a security ML integration package.
— the description by elastic

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured, multi-step workflow for installing, verifying, and troubleshooting Elastic security ML integration packages, including beaconing, data exfiltration, DGA, lateral movement, privileged access, and living-off-the-land detection. It manages the entire lifecycle, from pre-flight environment checks and Fleet package installation to configuring component templates, ingest pipelines, transforms, data views, ML anomaly detection jobs, and detection rules. The skill ensures safe operations by requiring user confirmation before applying any changes and by performing state validation to prevent configuration conflicts.

What it does

  • Performs pre-flight checks for license, permissions, and ML capacity.
  • Installs and manages Fleet security ML integration packages.
  • Configures shared component templates and ingest pipelines without overwriting existing data.
  • Manages transform lifecycle, including resetting and starting jobs.
  • Deploys ML anomaly detection jobs and enables associated detection rules.
  • Provides status reporting and documentation retrieval for installed packages.

When to use it

  • When you need to install a new security ML integration package.
  • When you need to verify the installation status or configuration of an existing package.
  • When troubleshooting issues with ML jobs, transforms, or detection rules related to these packages.
  • When you need to ensure all required assets like data views and pipelines are correctly set up.

Advantages

  • Reduces manual effort by automating complex, multi-step deployment procedures.
  • Prevents configuration errors through pre-flight state validation.
  • Protects existing infrastructure by merging rather than overwriting shared templates and pipelines.
  • Ensures operational safety by requiring explicit user confirmation before any write operations.

Usage examples

“Install PAD”
The agent gathers the current state of the PAD package, checks ML capacity, identifies required template merges, and presents a plan for installation, including transform and data view setup, before requesting confirmation to proceed.
“Is beaconing installed?”
The agent queries the Fleet package manager for the beaconing package status and reports the title, version, and current installation status without modifying any system settings.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/elastic/security-ml-integrations/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip security-ml-integrations.zip -d .agents/skills

Documentation by elastic

Security ML Integration Package Management

Install, check, or troubleshoot a security ML package. Supported packages: beaconing (Network Beaconing Identification), ded (Data Exfiltration Detection), dga (Domain Generation Algorithm Detection), lmd (Lateral Movement Detection), pad (Privileged Access Detection), problemchild (Living off the Land Attack Detection).

<!-- begin-partial: preamble -->

Environment Configuration

This skill executes Elasticsearch operations through the elastic CLI. If the elastic CLI is not installed, tell the user what it is needed for. Do not guess credentials, call the HTTP API directly, or attempt other workarounds.

This skill references operations in HTTP-shorthand form (e.g., GET /, GET /_cat/indices, GET /{index}/_mapping, GET /{index}/_settings/index.mode, POST /_query). The Operations table at the end of this document maps each shorthand to the equivalent elastic CLI command — always use the CLI rather than calling the HTTP API directly.

<!-- end-partial: preamble -->

Package source patterns, shared template fields, and ingest-pipeline wiring live in package capabilities.

Process

  1. Classify the request. Status, README, or install. Status and README stay read-only — do not install, start transforms, deploy jobs, or enable rules. After identifying the package, branch: status uses GET kbn:/api/fleet/epm/packages/{package} only (title, version, status); README adds GET kbn:/api/fleet/epm/packages/{package}/{version}/docs/README.md and returns that document; install continues.

  2. Identify the package. Map the user's words to one of the six package names above. If the name is ambiguous, ask before calling any API. Load that package's row from package capabilities — omit fields that are empty for that package.

  3. Gather environment state before any mutation. Install only. GET /_license first — continue only for platinum, enterprise, trial, or Serverless. If GET /_ml/info returns 403, report missing manage_ml and stop. Then collect:

    • Package: GET kbn:/api/fleet/epm/packages/{package} for name, title, version, and status.

    • Source data: POST /{source_patterns}/_search with size: 0 and track_total_hits: true. If every pattern has zero hits, warn that jobs will not produce useful anomalies.

    • Shared template (DGA, PAD, ProblemChild only): GET /_component_template/{name}. Exists → plan a merge of the fields in the reference. 404 → plan a create with only those fields.

    • Shared pipeline (DGA, PAD, ProblemChild only): GET /_ingest/pipeline/{name}. Exists → plan to append a processor. 404 → plan a single-processor pipeline.

    • Transforms: GET /_transform/{pattern} (skip if the package has no transform pattern). Record each id, dest.index, and the .all alias (dest.aliases where move_on_creation is false).

    • Data views: GET kbn:/api/data_views and check whether the package's data-view pattern already exists.

    • ML capacity: GET /_ml/info (total memory) and GET /_ml/anomaly_detectors/_all/_stats (sum memory of opened jobs). GET kbn:/internal/ml/modules/get_module/{module_id} for job count and per-job memory. If jobs do not fit, say so and suggest prioritizing job groups — do not deploy anyway without the user accepting the risk.

    • Rules: GET kbn:/api/detection_engine/rules/prepackaged/_status and POST kbn:/internal/detection_engine/prebuilt_rules/installation/_review filtered by the package detection tag.

  4. Present a plan and wait for confirmation. Show package identity and status, source-data hits (warn if all zero), template/pipeline merge-or-create, transform count and states, data-view presence, ML memory fit, and rule count. Do not call any write API until the user confirms. If they decline, stop.

  5. Install the Fleet package, then apply shared mappings. After confirmation:

    • POST kbn:/api/fleet/epm/packages/{package} to install assets (transforms may be deferred until started).

    • If the package uses a component template: PUT /_component_template/{name} with the merged or new body. Then POST /{data_stream}/_rollover on the rollover target so new indices pick up mappings.

    • If the package uses an ingest pipeline: PUT /_ingest/pipeline/{name} with appended or new processors. Use the installed package version in the versioned pipeline name.

    Skip template, pipeline, and rollover when the package row has no values for them (beaconing, ded, lmd).

  6. Start transforms and create destination aliases. Skip this block (except .ml-anomalies-shared) when the package has no transform pattern. Fleet's first checkpoint often skips .all aliases. After install, GET /_transform/{pattern} again if IDs were missing. Reset only if the transform is new, never started, failed, or the user asked to rebuild: POST /_transform/{id}/_stop, POST /_transform/{id}/_reset, POST /_transform/{id}/_update with the existing description, POST /_transform/{id}/_start, POST /_transform/{id}/_schedule_now. If already started with checkpointing.last.checkpoint >= 1, skip reset; restamp via the existing description and ensure .all aliases exist. Then POST /_aliases to add the .all alias (and .latest if the dest defines it) on dest.index. If a data-view pattern is missing, POST kbn:/api/data_views/data_view. Also ensure a hidden .ml-anomalies-shared data view exists (POST kbn:/api/data_views/data_view) so package dashboards resolve.

  7. Confirm the first transform checkpoint before ML. If the package row has no transform pattern, skip this step and continue to jobs. Otherwise call GET /_transform/{pattern}/_stats. Proceed only when each transform is started and checkpointing.last.checkpoint >= 1; do not use documents_processed as a substitute. Zero documents with a completed checkpoint is acceptable when source patterns are empty — destination aliases still let datafeeds start. Otherwise tell the user transforms must finish processing and wait for confirmation before the next step. Never set up the ML module against missing dest indices.

  8. Deploy ML jobs, then detection rules. After transforms are confirmed (or skipped):

    • POST kbn:/internal/ml/modules/setup/{module_id} with startDatafeed: true and indexPatternName set to the package data-view pattern, or source patterns when that cell is empty. Do not put spaces after commas in indexPatternName — the setup API splits on commas without trimming. Correct: logs-*,ml_okta_multiple_user_sessions_pad.all,ml_windows_privilege_type_pad.all.

    • Initialize the detection engine if needed (POST kbn:/api/detection_engine/index), install the prebuilt rules package (POST kbn:/api/fleet/epm/packages/security_detection_engine), review the package-tagged rules, then call POST kbn:/internal/detection_engine/prebuilt_rules/installation/_perform with {"mode":"SPECIFIC_RULES","rules":[{"rule_id":"<rule_id>","version":<version>}]} (one entry per reviewed rule). Enable the package's ML rules with POST kbn:/api/detection_engine/rules/_bulk_action (disable then enable on immutable ML rules tagged with the package detection tag so API keys are stamped with the calling user).

  9. Report created versus skipped. Package version and asset count; template/pipeline/rollover created, updated, or skipped; transforms started; data views created or already present; ML jobs and datafeeds started (and any that failed, with the mapping or index reason); rules installed and enabled.

Examples

Install PAD

User: "Install PAD"

  1. Gather state for pad. Package not installed, zero source hits, logs-endpoint.events.process@custom already has ProblemChild fields, no @custom pipeline, two stopped transforms, 15 jobs fit in available memory, tagged rules available.

  2. Present the plan: warn about empty source data; merge process.command_line_entropy; create the PAD pipeline processor; start both transforms; create the PAD data view. Wait for confirm.

  3. After confirm: POST kbn:/api/fleet/epm/packages/pad, merge-and-PUT the shared template, PUT the pipeline, restamp via existing description (reset only if transforms are new or stopped), POST /_aliases for each .all alias.

  4. GET /_transform/*pad*/_stats. If checkpoint 1 is done (even with 0 docs), continue; otherwise wait for the user.

  5. POST kbn:/internal/ml/modules/setup/pad-ml with a no-space indexPatternName, then install and enable rules tagged Use Case: Privileged Access Detection.

  6. Report each section as created, updated, or skipped.

Install beaconing (no template or pipeline)

User: "Set up beaconing"

  1. Gather state. Omit template, pipeline, and rollover — the beaconing row has none.

  2. Present a shorter plan (package, one transform, data view, jobs, rules). Wait for confirm.

  3. Install the package, start the transform, create ml_beaconing* if missing, confirm checkpoint, set up beaconing-ml, enable rules tagged Use Case: Network Beaconing Identification.

Status only

User: "Is beaconing installed?"

  1. GET kbn:/api/fleet/epm/packages/beaconing only.

  2. Report title, version, and status. A README request also fetches and returns the docs README. Do not install or start anything.

Guidelines

  • Gather environment state before every install. Do not skip the pre-flight reads.

  • Stay read-only on status and README questions.

  • Wait for explicit confirmation before any write. Check license before the Fleet POST.

  • Pause after transforms until checkpointing.last.checkpoint >= 1 (0 docs is acceptable when source patterns are empty). Skip transform and package data-view steps when those capability cells are empty.

  • Never overwrite shared @custom templates or pipelines. PAD and ProblemChild share logs-endpoint.events.process@custom — merge fields and append processors.

  • Put no spaces after commas in ML indexPatternName.

  • Cite Fleet package APIs as kbn:/api/fleet/.... Do not use a fleet: prefix.

  • Restamp a Fleet transform by setting --description to the existing text, then start. Do not use --body '{}'.

  • On serverless, GET /_ml/info may return 410. Continue with opened-job stats from GET /_ml/anomaly_detectors/_all/_stats; do not abort the install.

Operations

HTTP API (shorthand)elastic CLI command
GET kbn:/api/fleet/epm/packages/{package}elastic kb elastic-package-manager-epm get-fleet-epm-packages-pkgname --pkg-name '{package}'
GET kbn:/api/fleet/epm/packages/{package}/{version}/docs/README.mdCLI gap — package-file fetch 404s on registry paths; keep this shorthand as the contract
POST kbn:/api/fleet/epm/packages/{package}elastic kb elastic-package-manager-epm post-fleet-epm-packages-pkgname --pkg-name '{package}'
POST /{source_patterns}/_searchelastic es count --index '{source_patterns}' --allow-no-indices true --ignore-unavailable true
GET /_component_template/{name}elastic es cluster get-component-template --name '{name}'
PUT /_component_template/{name}elastic es cluster put-component-template --name '{name}' --template '{json}'
GET /_ingest/pipeline/{name}elastic es ingest get-pipeline --id '{name}'
PUT /_ingest/pipeline/{name}elastic es ingest put-pipeline --id '{name}' --input-file '{path}'
POST /{data_stream}/_rolloverelastic es indices rollover --alias '{data_stream}'
GET /_transform/{pattern}elastic es transform get-transform --transform-id '{pattern}'
GET /_transform/{pattern}/_statselastic es transform get-transform-stats --transform-id '{pattern}'
POST /_transform/{id}/_stopelastic es transform stop-transform --transform-id '{id}' --force true --wait-for-completion true
POST /_transform/{id}/_resetelastic es transform reset-transform --transform-id '{id}' --force true
POST /_transform/{id}/_updateelastic es transform update-transform --transform-id '{id}' --description '{existing}'
POST /_transform/{id}/_startelastic es transform start-transform --transform-id '{id}'
POST /_transform/{id}/_schedule_nowelastic es transform schedule-now-transform --transform-id '{id}'
POST /_aliaseselastic es indices update-aliases --actions '{json}'
GET kbn:/api/data_viewselastic kb data-views get-all-data-views-default
POST kbn:/api/data_views/data_viewelastic kb data-views create-data-view-default --data-view '{json}'
GET /_licenseelastic es license get
GET /_ml/infoelastic es ml info
GET /_ml/anomaly_detectors/_all/_statselastic es ml get-job-stats --job-id '_all'
GET kbn:/internal/ml/modules/get_module/{module_id}CLI gap — ML module get is an internal Kibana API; keep this shorthand as the contract
POST kbn:/internal/ml/modules/setup/{module_id}CLI gap — ML module setup is an internal Kibana API; keep this shorthand as the contract
GET kbn:/api/detection_engine/rules/prepackaged/_statusCLI gap — no generated prepackaged-status command; keep this shorthand as the contract
POST kbn:/api/detection_engine/indexCLI gap — no generated detection-engine index command; keep this shorthand as the contract
POST kbn:/api/fleet/epm/packages/security_detection_engineelastic kb elastic-package-manager-epm post-fleet-epm-packages-pkgname --pkg-name 'security_detection_engine'
POST kbn:/internal/detection_engine/prebuilt_rules/installation/_reviewCLI gap — prebuilt-rule review is an internal Kibana API; keep this shorthand as the contract
POST kbn:/internal/detection_engine/prebuilt_rules/installation/_performCLI gap — prebuilt-rule install is an internal Kibana API; keep this shorthand as the contract
POST kbn:/api/detection_engine/rules/_bulk_actionCLI gap — generated perform-rules-bulk-action accepts only edit; keep this shorthand for disable/enable

Internal ML-module APIs, prebuilt-rules _review/_perform, detection-engine index, and rules disable/enable have no usable generated CLI commands yet. Keep the HTTP shorthand as the contract; do not add skill-owned scripts as a workaround. Generated Fleet EPM get/install commands exist — use those instead of the shorthand.

Files

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an API key

The author says: Requires Kibana 9.4+ with Fleet, ML, and Security; a Platinum, Enterprise, or trial license (or Serverless); manage_ml; and the elastic CLI ≥ 0.5.

Security

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Attribution and licence

Created by
elastic
Publisher
Elastic (Official)
Source
elastic/agent-skills · skills/security/ml-integrations · commit 09d879d
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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