Wazuh Sca Hardening logo

Wazuh Sca Hardening

Organization
gensecaihq
wazuh-sca-hardening

Use Wazuh Security Configuration Assessment (SCA) results — CIS policy scores, failed checks and regressions — to prioritize hardening and map it to CIS Controls, PCI DSS and ISO 27001; use for hardening reviews, compliance evidence, or when a vulnerability or incident points to a misconfiguration.

Overview

Publishergensecaihq
RepositoryWazuh-Autopilot
Skill namewazuh-sca-hardening
Stars
61
Forks
15
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by gensecaihq on GitHub. Read the source before you install it.

Installation

Install the Wazuh Sca Hardening AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/gensecaihq/Wazuh-Autopilot/tree/main/backend/app/skills/wazuh-sca-hardening
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/gensecaihq/Wazuh-Autopilot.git /tmp/Wazuh-Autopilot
mkdir -p .claude/skills
cp -r /tmp/Wazuh-Autopilot/backend/app/skills/wazuh-sca-hardening .claude/skills/wazuh-sca-hardening
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Wazuh Sca Hardening in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Wazuh Sca Hardening on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Wazuh Sca Hardening is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Wazuh SCA Hardening

Wazuh SCA runs policy files (mostly CIS Benchmarks: cis_ubuntu22-04, cis_win2022, cis_rhel9, …) on each agent and reports each check as passed, failed or not applicable, plus an overall score.

SCA rules (stock ruleset, group sca)

RuleLevelMeaning
rule 190013SCA summary: policy score
rule 190024Score less than 90%
rule 190035Score less than 80%
rule 190047Score less than 50%
rule 190059Score less than 30%
rule 190077Check result failed (sca.check.result: failed)
rule 190083Check result passed
rule 190093Check result not applicable
rule 190103Status changed from failed to passed (remediated)
rule 190119Status changed from passed to failed (regression)
rule 190125Status changed from passed to not applicable

A regression (rule 19011) is the most important SCA signal: something that was compliant stopped being compliant. It can mean drift, a bad change — or an attacker weakening a host (disabling auditing, enabling root SSH login). Treat regressions on servers as potential incidents, not just hygiene.

Key fields

sca.policy, sca.policy_id, sca.check.id, sca.check.title, sca.check.result, sca.check.previous_result, sca.check.rationale, sca.check.remediation, sca.check.compliance.* (CIS, PCI DSS, NIST 800-53, HIPAA, ISO mappings shipped in the policy), sca.score, sca.passed, sca.failed.

Getting the data

  • Current state: get_sca_policy_checks agent_id="<id>" (optionally policy_id) — returns checks with result, rationale and remediation.
  • Changes over time: get_wazuh_alerts rule_groups=["sca"] agent_id="<id>" timestamp_start="now-7d"; regressions only: filter the rule_id parameter on rule 19011.
  • Fleet view: iterate agents from get_wazuh_agents status="active" — keep it to the in-scope set (servers, internet-facing, crown jewels) to stay within budget.

Prioritizing failed checks

Score each failed check, highest first:

  1. Exposure: internet-facing or crown-jewel host > internal server > workstation.
  2. Attack relevance: checks that directly enable known techniques — remote root login, password authentication on SSH, SMBv1, missing audit logging, world-writable system paths, no host firewall, unpatched auto-update disabled.
  3. CIS profile: Level 1 checks are baseline and cheap; Level 2 checks are defence-in-depth.
  4. Correlation: a failed check on a host that also has related alerts or critical vulnerabilities moves to the top (hand off vulnerability context to vuln-management if you are compliance).
  5. Effort: note likely breakage (e.g. disabling a service in use).

Mapping to frameworks

AreaCIS Controls v8.1PCI DSS v4.0.1ISO/IEC 27001:2022
Secure configurationCIS 4Req 2A.8.9
Audit loggingCIS 8Req 10A.8.15
Vulnerability / patchCIS 7Req 6A.8.8
Access control / accountsCIS 5, CIS 6Req 7, Req 8A.5.15, A.8.2
Network defence / firewallCIS 12, CIS 13Req 1A.8.20

Prefer the mapping shipped in sca.check.compliance.* when present; the table is a fallback.

Remediation hand-off

SCA findings are fixed by system owners, not by Wazuh active response. Record remediation as a recommendation: check ID, title, affected agents, remediation text from the policy, and the priority. Regressions that look deliberate on a sensitive host go to triage as a new case.

Output

  • add_finding per host or per theme: score, failed check count, top failed checks with remediation, regressions with timestamps, and refs such as CIS-v8.1:4, PCI-DSS-4:Req 2, ISO-27001:A.8.9.
  • save_report (kind compliance) for fleet reviews: score distribution, worst hosts, top recurring failures, regressions since last review.

Frequently asked questions

What does the Wazuh Sca Hardening AI skill do?

Use Wazuh Security Configuration Assessment (SCA) results — CIS policy scores, failed checks and regressions — to prioritize hardening and map it to CIS Controls, PCI DSS and ISO 27001; use for hardening reviews, compliance evidence, or when a vulnerability or incident points to a misconfiguration.

Why use Wazuh Sca Hardening on TypingMind?

Because you install it once and use it with any model. Wazuh Sca Hardening is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Wazuh Sca Hardening in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/gensecaihq/Wazuh-Autopilot/tree/main/backend/app/skills/wazuh-sca-hardening. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Wazuh Sca Hardening?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Wazuh Sca Hardening?

As many as you like. As long as a model supports skills, you can use Wazuh Sca Hardening with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Wazuh Sca Hardening AI skill free?

Yes. It is published on GitHub by gensecaihq under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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