Borrowing it
Nothing to install: this file belongs to ThiagoGuislotti/copilot-instructions. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ThiagoGuislotti/copilot-instructions/main/.github/instructions/data-privacy-compliance.instructions.mdgit clone --depth 1 https://github.com/ThiagoGuislotti/copilot-instructionsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/thiagoguislotti/copilot-instructions/data-privacy-compliance)<a href="https://agentmods.dev/instructions/thiagoguislotti/copilot-instructions/data-privacy-compliance"><img src="https://agentmods.dev/badge/instructions/thiagoguislotti/copilot-instructions/data-privacy-compliance.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00566 | $0.00566 |
| Opus 5 | $0.00283 | $0.00283 |
| Sonnet 5 | $0.00113 | $0.00113 |
| Haiku 4.5 | $0.00057 | $0.00057 |
Grade A, and why
copilot-instructions data-privacy-compliance.instructions.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Privacy and Compliance Baseline
- Use this instruction for privacy-by-design decisions across API, frontend, backend, and database layers.
- Treat personal data handling as an explicit architecture concern, not only a legal review step.
Scope and Governance
- Maintain a current data inventory with owner, purpose, sensitivity class, and retention policy.
- Classify data at minimum as public, internal, confidential, and restricted.
- Define accountability for each processing activity and audit ownership.
Privacy by Design
- Apply data minimization; collect and retain only what is necessary for the declared purpose.
- Enforce purpose limitation; prevent uncontrolled reuse of collected data.
- Prefer pseudonymization or tokenization for identifiers in non-core processing flows.
- Separate operational identifiers from personal identifiers whenever feasible.
Consent and Legal Basis
- Record legal basis and consent state for processing operations that require it.
- Keep auditable records of consent capture, updates, and withdrawal events.
- Propagate consent constraints to downstream systems and derived datasets.
Access Control and Segregation
- Enforce least privilege access to personal data by role and business purpose.
- Segment privileged access paths and require elevated controls for sensitive datasets.
- Protect multi-tenant boundaries with strict tenant-scoped access checks.
Data Protection Controls
- Encrypt personal and sensitive data in transit and at rest.
- Use managed key lifecycle controls including rotation and access auditing.
- Mask or redact personal data in logs, telemetry, and non-production datasets.
- Prohibit secrets and personal data leakage in error messages and debug outputs.
Retention, Deletion, and Archival
- Define retention schedules per data class and enforce automated expiration where feasible.
- Implement deletion workflows that include primary stores, replicas, caches, and derived datasets.
- Validate backup and archival deletion semantics against retention and legal constraints.
- Maintain verifiable evidence of deletion and retention policy execution.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 57 lines · 566 tokens per session scan A 39c72b139024
copilot-instructions data-privacy-compliance.instructions.md is an instructions file published in the GitHub repository ThiagoGuislotti/copilot-instructions (2 stars, last pushed 2mo ago), licensed MIT. It adds 566 tokens to every session, about $0.0028 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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