Service Account Noise logo

Service Account Noise

Organization
CDESpace
Service Account Noise

Tune a detection that a non-human identity keeps firing — an infrastructure-as-code role creating IAM resources, a cloud service launching instances on schedule, an integration or backup account reading at machine scale. Judges whether the actor is genuinely a service doing the job it was built for, proves the pattern is durable and explains a recurring share of the noise, and builds an exclusion pinned to the identity and its durable source so a human or an attacker performing the same action still alerts.

Overview

PublisherCDESpace
RepositoryDetection-Skills
Skill nameService Account Noise
Stars
57
Forks
5
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

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

Installation

Install the Service Account Noise 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/CDESpace/Detection-Skills/tree/main/skills/tuning/service-account-noise
  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/CDESpace/Detection-Skills.git /tmp/Detection-Skills
mkdir -p .claude/skills
cp -r /tmp/Detection-Skills/skills/tuning/service-account-noise .claude/skills/cdespace-service-account-noise
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Service Account Noise 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 Service Account Noise 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 Service Account Noise 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.

Tuning Methodology

This use case fires on detections built to catch a human doing something — creating IAM roles, launching instances, reading records in bulk — that an automation does all day as its job: a Terraform or Scalr provisioning role creating the roles it manages, autoscaling.amazonaws.com launching instances on demand, an access-review service running the same lookups whenever a review opens, a backup agent reading at volumes no person would. The activity is benign for a structural reason that holds every time: a service did it, not a person, so there are no human credentials behind it to steal or misuse, and it performs exactly the operation it was built for.

A tuning is a permanent change to a live detection, so the question is never "was this run benign?" It is whether this identity is a durable, explainable source of benign noise. Confirm the actor is genuinely non-human — a service principal, an instance or pipeline role, an account that never logs in interactively — not a person's account that automation happens to borrow. Then establish durability the way you would a baseline: query for it. How much of this detection's recent firing does the identity explain, and does it recur across the window, or did it appear once? A service that accounts for most of the noise and keeps firing for the same reason is a real tuning; a single benign sighting is not.

Read the detection's intent before excluding. If the detection exists to watch automation itself — anomalous service-account usage, credential abuse of machine identities — then the service account is the very signal the detection was built to surface, and excluding it deletes the detection's reason to exist. Decline, however benign this case looked. Scope is a hard rule: tune only on identities that appeared in the triggering incident, never invent values, and check the detection's current logic first — a value already excluded by a prior clause is already tuned.

The clause is appended to the detection as a filter and is never negated for you: it must exclude the benign by NOT matching it. A bare match (==, contains, in) keeps only the benign and silently disables the detection — the one unforgivable mistake. A single identity takes a !-prefixed operator (actor.user.name != "scalr-terraformer"); a compound takes the benign match wrapped as a unit (not(actor.user.name == "scalr-terraformer" and src_endpoint.ip == "10.20.0.5")) — never hand-expanded into separate != terms, which over-excludes. Read field paths off the actual triggering events, not from memory. Then adversary-test it: an attacker can assume a role or steal a service credential, so if this identity turned hostile, would the filter blind the detection? Pin the exclusion to the identity together with the durable source it always operates from — the invoking service, the pipeline host, the role path — so the same name appearing anywhere else still alerts.

Output

Action

exclude — one filter clause appended to the detection removing the service identity's routine operation; when the actor turns out to be a person, the pattern appeared once, or the detection watches machine identities by design, propose nothing — a precise decline beats an exclusion that widens a blind spot.

Target

The detection whose false positive was confirmed — one appended filter clause on a field present in its current logic.

Value

The clause, one line: the service identity pinned to its durable source — not(actor == <identity> and <source field> == <its origin>), or a single != when the identity alone is unambiguous. Never a bare match operator.

Evidence

The triggering events the identity and field paths were read from; the identity's type and authentication pattern proving it is not a person; aggregation counts showing the share of the detection's recent firing it explains and its recurrence; the detection's current logic and existing tuning suggestions.

Reasoning

The mechanism, not the measurements: this is a service doing the job it was built for, with no human credentials behind it to abuse — and what still alerts: the same action by any person, or this identity operating from anywhere but its own source.

Frequently asked questions

What does the Service Account Noise AI skill do?

Tune a detection that a non-human identity keeps firing — an infrastructure-as-code role creating IAM resources, a cloud service launching instances on schedule, an integration or backup account reading at machine scale. Judges whether the actor is genuinely a service doing the job it was built for, proves the pattern is durable and explains a recurring share of the noise, and builds an exclusion pinned to the identity and its durable source so a human or an attacker performing the same action still alerts.

Why use Service Account Noise on TypingMind?

Because you install it once and use it with any model. Service Account Noise 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 Service Account Noise in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/CDESpace/Detection-Skills/tree/main/skills/tuning/service-account-noise. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Service Account Noise?

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 Service Account Noise?

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

Is the Service Account Noise AI skill free?

Yes. It is published on GitHub by CDESpace 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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