SECOPS

Privileged-Grant Collusion and Self-Grant Pattern Investigator

An agent investigates suspicious grant patterns — self-grants, reciprocal grant rings, and after-hours elevations — by correlating Axiom logs and GitHub activity.

CategorySecOps
Enginepaperclip
Difficultyadvanced
Triggerschedule
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly scheduled investigation run
  • ActionPull and correlate grant events from AxiomAxiom
  • ActionCross-reference GitHub permission changesGitHubGitHub
  • LogicIdentify self-grants, rings, and off-hours clusters
  • ActionFile evidence-backed case in NotionNotionNotion
  • OutputNotify security lead of top findings in SlackSlack

What it does

It hunts for relationship-level abuse of the grant process that single-event rules miss: people granting themselves privilege, pairs who repeatedly elevate each other, and clusters of off-hours grants. It assembles the evidence into a reviewable case rather than a raw alert.

When to use it

Use this for periodic insider-risk review of how privileged access is being handed out, not just whether a single grant is risky. Best when you need narrative, evidence-linked findings an investigator can act on.

How it works

  1. 1A weekly schedule starts the investigation run.
  2. 2The agent pulls the period's grant events from Axiom and correlates grantor-grantee pairs, timing, and self-grants.
  3. 3It cross-references GitHub org and repo permission changes to catch elevation tied to code-access changes.
  4. 4It reasons over the combined timeline to identify collusion rings, self-elevation, and anomalous clusters.
  5. 5It files a structured case in Notion with linked evidence, and pings the security lead in Slack with the top findings.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect AxiomLog streams, queries, dashboards.
  2. 2
    Connect GitHubRepos, issues, pull requests, actions.
  3. 3
    Connect NotionPages, databases, comments.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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