Dt Sec Contextualization logo

Dt Sec Contextualization

Organization
Dynatrace
dt-sec-contextualization

Resolve security signals, IoC matches, or Smartscape nodes to runtime Dynatrace entities and connect findings on different entity levels through a shared runtime entity. Covers identity-to-Smartscape mapping (incl. container-image digest/ID to workload), cross-level topology (K8s pod detection vs. node CVE via pod-to-node), per-entity risk summarization, and coverage match recipes shared by dt-sec-insights. Trigger: "map these findings to workloads/hosts", "which workload does this container image run as", "do these findings relate through the same runtime entity", "enrich this IoC match with entity context", "which threat report mentions this IoC". Queries security.events ONLY for THREAT_REPORT IoC enrichment (matched IoC to attributing reports); Do NOT use for broad security.events posture/overview (use dt-sec-insights), general DQL (use dt-dql-essentials), IoC hunting in logs/spans (use dt-sec-ioc-hunting), or K8s observability outside the security cross-level context (use dt-obs-kubernetes).

Overview

PublisherDynatrace
Repositorydynatrace-for-ai
Skill namedt-sec-contextualization
Stars
156
Forks
30
Bundled files
4
LicenseApache-2.0
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.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Dt Sec Contextualization 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.
  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/Dynatrace/dynatrace-for-ai.git /tmp/dynatrace-for-ai
mkdir -p .claude/skills
cp -r /tmp/dynatrace-for-ai/skills/dt-sec-contextualization .claude/skills/dt-sec-contextualization
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dt Sec Contextualization 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 Dt Sec Contextualization 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 Dt Sec Contextualization 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.

Security Contextualization Skill

Resolve security signals and entity attribute sets to runtime Dynatrace Smartscape entities, summarize findings across entity levels, and connect signals that land on different levels (e.g. a detection on a K8S_POD vs. a CVE on a KUBERNETES_NODE) via a shared runtime entity.

What This Skill Covers

  • Identity → Smartscape mapping — given a row carrying any of dt.smartscape_source.id, container_image.digest, container_image.id, host.ip, dt.entity.*, or k8s.* fields, resolve it to a Smartscape entity at any requested level (CONTAINER / K8S_POD / workload / K8S_NODE / HOST / cloud / GENAI_SERVICE — AI/GenAI workloads).
  • Artifact → runtime bridgecontainer_image.digestsmartscapeNodes CONTAINERis_part_of.* → parent workload or runs_on.host → HOST. Works without pre-enriched dt.smartscape_source.id.
  • Cross-level correlation — tiered entity matching to determine whether two findings (e.g. a detection and a CVE from different legs) relate through a shared runtime entity. Tier 1: exact entity id match; Tier 2: same workload/pod/host by name; Tier 3: same namespace/cluster (context-only — does not contribute to scoring).
  • Pod → node topology — resolve K8S_POD to its K8S_NODE via k8s.node.name (co-projected field) or Smartscape edge traversal. Enables "detection hit pod X — does that pod run on a vulnerable node?"
  • Coverage match recipes — 2-way and 3-way container→workload match patterns shared across dt-sec-insights coverage counting queries.
  • Entity enrichment — given findings, IoC matches, or raw Smartscape nodes, produce per-entity risk-level breakdowns and entity-key bundles for downstream scoring.
  • IoC enrichment — attribute an already-matched IoC (IP / domain / URL / email / CVE / hash / MITRE TTP) with adversary context (actor, malware family, MITRE technique, targeting, provider) by reverse-looking-up the ingested THREAT_REPORT events whose observable arrays contain that IoC.

When to Use This Skill

Intent / triggerReference
Map findings / IoC matches to workloads, hosts, or cloud entitiesidentity-mapping.md -> entity-enrichment.md
Which Smartscape entity does this container image / digest run as?identity-mapping.md § Mapping Primitive (Path 2 - container digest)
Do this detection and this CVE relate via a shared entity?correlation-and-coverage.md § Correlation
Pod X fired - does it run on a vulnerable node?correlation-and-coverage.md § Correlation (Pod->Node Topology)
Per-entity risk summary (Critical/High/Medium/Low)entity-enrichment.md
Coverage match recipe - which workloads are covered by product Y?correlation-and-coverage.md § Coverage
Which entity-identity fields are relevant to a finding type?identity-mapping.md § Data Model
Enrich a matched IoC (IP/domain/hash/CVE/...) with threat-report adversary contextioc-enrichment.md
Scope findings to AI/GenAI workloads; which processes belong to an AI service; resolve a process to its AI serviceidentity-mapping.md § Mapping Primitive (Path 4 - GENAI_SERVICE -> SERVICE -> PROCESS)

How This Skill Is Organized

  1. SKILL.md (this file) — entry point and routing.
  2. references/
    • identity-mapping.md — generalized identity->Smartscape resolver (mapping primitive Paths 1/2/3/4), pre-flight identifier checks, level selection, and entity-identity field guidance.
    • entity-enrichment.md — consumers of the mapping primitive: cloud (Path 1), K8s workload (3-way), host-by-IP, host-by-entity, natural-language fallback, problem->entities->findings chain. Per-entity risk-level breakdowns (Critical/High/Medium/Low).
    • correlation-and-coverage.md — cross-level entity convergence, pod->node topology resolution, scoring contract, and 2-way/3-way coverage match recipes shared with dt-sec-insights.
    • ioc-enrichment.md — reverse-lookup IoC enrichment: attribute a matched IoC to ingested THREAT_REPORT events and surface adversary context (actor / malware / MITRE / targeting). Single and batch (per-IoC) templates; supported-IoC taxonomy.

Universal Best Practices

  1. Always load dt-dql-essentials first — DQL syntax and function names differ from SQL. Confirm all functions before generating queries.
  2. Ground every query in a named template — do not improvise Smartscape joins. The 3-way match, digest→CONTAINER→workload, and pod→node traversal patterns are precise; deviating produces silent zero-row results.
  3. Run the pre-flight check before the full 3-way enrichment — external providers vary widely. Confirm at least one identifier path is populated before running the expensive append chain.
  4. Check dt.smartscape_source.type before trusting Path 1 — a non-null dt.smartscape_source.id is not proof of workload-level resolution; the field may point to a namespace, cluster, or cloud resource. Only K8s workload types (K8S_DEPLOYMENT, K8S_DAEMONSET, K8S_STATEFULSET, K8S_CRONJOB, K8S_JOB, K8S_REPLICASET) are eligible for workload enrichment via Path 1.
  5. Dedup early and after append — dedup before joins to collapse re-ingested duplicates; dedup again after append because the same finding can match multiple paths.
  6. Tier 3 correlation is context only — same namespace/cluster shared by two findings does not raise the exposure score. Never treat a cluster-level shared attribute as proof of entity-level relatedness.
  7. Route topology queries to dt-obs-kubernetes — pod→node placement and Smartscape edge traversal patterns live in dt-obs-kubernetes/references/pod-node-placement.md. Do not re-author them here; reference them and apply the output in correlation-and-coverage.md.
  8. No dt.system.bucket filters — security event data may live in any bucket; filtering by bucket risks hiding findings.
  9. THREAT_REPORT is the one security.events query allowed here — reverse lookup only. ioc-enrichment.md attributes a matched IoC to reports (IoC → report). Broad THREAT_REPORT overviews, IOC rollups, and forward report → environment correlation stay in dt-sec-insights (threat-intelligence.md). Never author finding/posture queries here.

Related Skills

SkillRole
dt-dql-essentialsLoad first. Core DQL syntax, functions, Smartscape patterns.
dt-sec-insightsConsumer of mapping primitive; owns finding-schema queries and coverage counting logic. Owns forward threat-intel (report → environment correlation, overviews, IOC rollups) in threat-intelligence.md; this skill owns only the reverse IoC → report enrichment (ioc-enrichment.md).
dt-sec-ioc-huntingRoutes cross-evidence correlation and entity enrichment to this skill.
dt-obs-kubernetesPod→node topology; K8s entity placement patterns.
dt-obs-hostsHost inventory; process-level context for HOST/PROCESS_GROUP findings.
dt-obs-aws / dt-obs-azure / dt-obs-gcpCloud Smartscape topology for cloud-entity enrichment.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Dt Sec Contextualization AI skill do?

Resolve security signals, IoC matches, or Smartscape nodes to runtime Dynatrace entities and connect findings on different entity levels through a shared runtime entity. Covers identity-to-Smartscape mapping (incl. container-image digest/ID to workload), cross-level topology (K8s pod detection vs. node CVE via pod-to-node), per-entity risk summarization, and coverage match recipes shared by dt-sec-insights. Trigger: "map these findings to workloads/hosts", "which workload does this container image run as", "do these findings relate through the same runtime entity", "enrich this IoC match wi...

Why use Dt Sec Contextualization on TypingMind?

Because you install it once and use it with any model. Dt Sec Contextualization 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 Dt Sec Contextualization in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Dynatrace/dynatrace-for-ai/tree/main/skills/dt-sec-contextualization. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Dt Sec Contextualization?

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 Dt Sec Contextualization?

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

Is the Dt Sec Contextualization AI skill free?

Yes. It is published on GitHub by Dynatrace under the Apache-2.0 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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