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Manage Data Security Policies

Organization
oracle
Manage Data Security Policies

Prepare individual data-security-policy creation, updates, deletion and redesign requests using verified FSM templates, and route supported changes through validated Data Loader imports.

Overview

Publisheroracle
RepositoryFusion-Setup-Configuration-Registry
Skill nameManage Data Security Policies
Stars
112
Forks
26
Bundled files
5
LicenseUPL-1.0
Links
Skill sourceRepository
  • 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.

  • 5 bundled files

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

  • Open source

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

Installation

Install the Manage Data Security Policies 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/oracle/Fusion-Setup-Configuration-Registry/tree/main/pillars/common/security-console/manage-data-security-policies
  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/oracle/Fusion-Setup-Configuration-Registry.git /tmp/Fusion-Setup-Configuration-Registry
mkdir -p .claude/skills
cp -r /tmp/Fusion-Setup-Configuration-Registry/pillars/common/security-console/manage-data-security-policies .claude/skills/oracle-manage-data-security-policies
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Manage Data Security Policies 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 Manage Data Security Policies 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 Manage Data Security Policies 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.

Manage Data Security Policies

Purpose

Provide a controlled, cross-pillar method to export, validate, and approval-gate the import of the Manage Data Security Policies FSM setup task. Delegate transport and live import execution to the declared shared and core dependencies. Capture the user's policy design and prepare supported FSM changes.

Export and move the FSM setup task Manage Data Security Policies only through the declared dependency workflows. Its catalog identity is ASXS_MANAGE_DATA_SECURITY_POLICIES; require a current catalog or live-task lookup to confirm that export/import remains supported before execution.

Scope and boundaries

  • Support task-level FSM CSV export and subsequent import of either the exact exported archive, a verified-template candidate or a user-supplied modified copy.
  • Accept policy creation, updates, deletion and redesign, including supported policy grants and conditions. Preserve user-specified scope and use only this task's verified contract. Route role-definition changes to Manage Job Roles or Manage Duties rather than substituting their archives here.
  • Do not call Fusion, OAuth, UCM, REST, FSM, or loader commands directly. shared.setup-export.task-level-export owns export; core.data-loader owns import preflight, upload, initiation, and monitoring.
  • A modified archive is a fresh source: calculate a new SHA-256 and repeat validation, preview, and immediate-approval gates. An approval for the original export does not approve a modified archive.

Required preflight

  1. Resolve the project workspace, requested operation, source and target environments, and exact policy scope. For migration, source and target must differ; for a same-environment restore, require explicit confirmation.
  2. Confirm task name and code with shared.setup-export.task-level-export and verify CSVExportImportSupportedFlag is Y. On an ambiguous, unavailable, or unsupported task, record a catalog-mismatch diagnostic and stop.
  3. Confirm Functional Setup Manager export access in source and Data Loader import privileges in target. Never request or expose credentials or tokens.
  4. Persist redacted source/target, task-code, scope, and execution-plan details before side effects.

Export

  1. Route export to shared.setup-export.task-level-export with task name Manage Data Security Policies, task code ASXS_MANAGE_DATA_SECURITY_POLICIES, source environment, and project ID.
  2. Let that skill complete discovery, support validation, process creation, polling, download, and project export persistence. Never substitute a template export for a data export.
  3. Preserve the returned ZIP unchanged and register its path, SHA-256, process ID, task identity, source environment, and timestamp. Preserve timeout or failure diagnostics; do not automatically start another export.

Create, update, delete and redesign

Accept these operation requests for this task's own setup objects, including user-specified individual changes. Prepare supported changes using the target's FSM template and execute them through core.data-loader; do not reject a request merely because the user does not already have an input archive.

Read FSM input preparation when preparing a change or helping a user understand the input. Obtain and safely extract the template, explain its files and required columns, map supplied business values, and prepare a separate candidate. Automatically authorize only the necessary template/spec export; retain Data Loader's validation and actual import approval workflow.

For deletion, verify the explicit supported action for the exact object or relationship. An omitted CSV row, blank value or unrelated REMOVE action is not whole-object deletion. If the task has no supported deletion contract, record that specific operation gap; do not impose a blanket prohibition on all individual changes or invent a browser/API execution route.

For update or redesign, establish existing keys, preserve unrelated values and show the intended changes. Intentional changes in the selected target are not same-environment restores. Apply restore confirmation only to an actual restore.

Template-first import preparation

  1. Unless the user explicitly overrides this sequence, export the task first and provide that export as the import template before asking for import content. Explain that the template establishes the supported archive and file structure; it is not approval to import.
  2. When the user provides an input file, compare it with the export template for file names, headers, column order, encoding, required values, and task-specific references. Help correct structural defects in a separate candidate file; preserve the supplied original and do not silently alter business values.
  3. Give the user a concise change summary and identify any unresolved values or references. Use the corrected file only after it conforms to the export structure and has been assembled into a new candidate archive using the supported archive shape. That candidate is a new artifact and must pass the validation and approval gates below.
  4. The user may override the export-first sequence, template choice, or proposed file corrections at any time. Honor that direction, while retaining the required task-identity, validation, and immediate import-approval gates.

Validate and import

  1. Require a non-empty ZIP and bind its SHA-256 to the task code, source, and target. Route archive validation to shared.validation.setup-data-validator. Fail closed on archive-shape, task-identity, scope, catalog, environment, or privilege mismatch.
  2. The ZIP must remain unchanged after validation. Do not unpack, repack, merge, or transform it. A user-modified copy begins a new validation cycle.
  3. Route only the immutable validated archive to core.data-loader as an FSM, task-routed import, with target environment, task context, SHA-256, and validation evidence.
  4. Let Data Loader complete source/API validation and create an immutable approval preview. Do not replace that route with direct FSM, UCM, REST, CSV, or browser operations.
  5. Present the exact target, task code, archive name and SHA-256, validation result, and expected policy impact. Stop before the first POST. Proceed only after a separate, immediate approval for that unchanged preview.
  6. Data Loader owns upload, initiation, monitoring, and diagnostics. Do not retry uploads, imports, overwrite/replacement operations, or failed jobs without fresh approval.

Failure handling

Classify failures as input validation, catalog mismatch, authorization, transport/environment, Fusion API, process timeout, process failed, artifact persistence, or approval blocker. Redact credentials, tokens, customer data, and sensitive policy details. Persist a redacted blocker and next action, then stop on ambiguity or unsafe paths.

Golden scenarios

ScenarioExpected result
Export policy setupConfirm identity and route only to task-level export.
Import the unmodified exportRequire ZIP hash, shared/core evidence, and separate immediate approval before Data Loader handoff.
Import a modified exported fileTreat it as a new archive and repeat validation and approval gates.
Request direct FSM/UCM/REST/browser importStop; catalog flags do not authorize a replacement route.

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 Manage Data Security Policies AI skill do?

Prepare individual data-security-policy creation, updates, deletion and redesign requests using verified FSM templates, and route supported changes through validated Data Loader imports.

Why use Manage Data Security Policies on TypingMind?

Because you install it once and use it with any model. Manage Data Security Policies 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 Manage Data Security Policies in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/oracle/Fusion-Setup-Configuration-Registry/tree/main/pillars/common/security-console/manage-data-security-policies. 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 Manage Data Security Policies?

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 Manage Data Security Policies?

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

Is the Manage Data Security Policies AI skill free?

Yes. It is published on GitHub by oracle under the UPL-1.0 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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