privacy-research-engineer-safeguards

Original：🇺🇸 English

Translated

Guides privacy research engineering for safeguards—PII and sensitive-data detection research, redaction and de-identification evals, memorization and extraction risk studies, privacy benchmarks and labeled corpora, logging/retention minimization for safety pipelines, and research memos on privacy–utility trade-offs for guardrail systems. Use when measuring PII detector quality, designing privacy eval suites for moderation stacks, studying training-data leakage or prompt logging risk, or recommending privacy mitigations for safeguard models—not for SOC 2/GDPR evidence automation (compliance-engineer), legal DPIA or AI policy (ai-risk-governance), harm/toxicity classifier R&D (ml-research-engineer-safeguards), production inference gateways (ml-infrastructure-engineer-safeguards), or general non-privacy research (ai-researcher).

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Added on2026-05-20

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SKILL.md Content

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Privacy Research Engineer, Safeguards

When to Use

Frame privacy research questions for safeguard and moderation stacks
Design PII detection/redaction benchmarks — precision/recall, re-identification risk
Evaluate de-identification techniques (mask, tokenize, synthetic replace) on realistic prompts
Study memorization and extraction — can models or logs leak user content?
Curate privacy-sensitive datasets — synthetic data, consent boundaries, labeling rules
Run ablations on detector architecture, threshold, or post-processing
Define logging minimization — what safety systems may store vs must discard
Write research memos with privacy–utility trade-offs and production recommendations
Specify promotion criteria for privacy mitigations before prod rollout

When NOT to Use

Audit evidence pipelines for GDPR/SOC 2 attestations →
```
compliance-engineer
```
Legal DPIA, acceptable-use policy, regulatory mapping →
```
ai-risk-governance
```
Harm categories, jailbreak benchmarks, toxic classifiers →
```
ml-research-engineer-safeguards
```
Deploy gateways, canaries, safety-path SLOs →
```
ml-infrastructure-engineer-safeguards
```
Red-team attack campaigns →
```
ai-redteam
```
Enterprise data governance architecture →
```
data-architect
```
Human-data platform product ethics (contributor labor) →
```
product-management-human-data-platform
```
General literature review unrelated to privacy in ML →
```
ai-researcher
```

Related skills

Need	Skill
Safety classifier research	`ml-research-engineer-safeguards`
Safeguard production infra	`ml-infrastructure-engineer-safeguards`
AI governance and DPIA framing	`ai-risk-governance`
Compliance controls and evidence	`compliance-engineer`
Data classification and lineage	`data-architect`
Adversarial extraction testing	`ai-redteam`
General research methods	`ai-researcher`
Human-data platform privacy	`product-management-human-data-platform`
Release and incident ops	`ai-lead-ops`

Core Workflows

1. Privacy research framing

Threat model, metrics, baselines.

See
references/privacy_research_framing.md
.

2. PII detection and redaction research

Detectors, redaction quality, evals.

See
references/pii_detection_redaction_research.md
.

3. Memorization and extraction

Leakage studies, attack surfaces.

See
references/memorization_and_extraction.md
.

4. Privacy benchmarks and datasets

Corpora, labeling, versioning.

See
references/privacy_benchmarks_datasets.md
.

5. Logging and retention minimization

Safety observability without over-collection.

See
references/logging_retention_minimization.md
.

6. Handoff to production

Promotion bar, monitoring hooks.

See
references/privacy_to_production_handoff.md
.

Outputs

Threat model — assets, adversaries, failure modes for privacy in safeguards
Benchmark spec — PII types, locales, adversarial variants
Results table — detection/redaction metrics by slice (language, format)
Leakage study report — methodology, findings, confidence
Logging policy draft — fields allowed, TTL, access controls (engineering input to legal)
Promotion recommendation — go/no-go with privacy–utility summary

Principles

Minimize data — collect and retain only what eval and ops truly need
Separate privacy from safety metrics — low PII leak rate is not interchangeable with low toxicity FN
Locale and format matter — email in one language ≠ global PII detector
Synthetic ≠ risk-free — synthetic PII can still encode patterns; document limits
Legal review for human data — research plans involving real user content need governance sign-off