63건 · 관련도순
Prepare training datasets for large language models with GPU-assisted cleaning. Covers duplicate removal, quality filtering, and personal information redaction, with workflows for text, images, video, and audio using NeMo Curator.
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Audit Python SDK logs, exceptions, and telemetry for sensitive data exposure. Trace values to their sources, fix confirmed leaks, and test that redaction hides sensitive information while preserving the caller's expected behavior.
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Prepare training datasets for large language models with GPU-assisted cleaning. Covers duplicate removal, quality filtering, and personal information redaction, with workflows for text, images, video, and audio using NeMo Curator.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Manages Data Subject Access Request procedures for employee requests under Art. 15 GDPR. Covers scope of disclosable HR records, emails, CCTV footage, performance reviews, monitoring data, and training records. Implements third-party data redaction, legal professional privilege, exemptions for ongoing proceedings, and the one-month response timeline. Keywords: DSAR, subject access request, Art. 15, employee records, redaction, privilege, HR data, SAR.
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Redact reverse-engineering findings for public disclosure while preserving methodology, generalizable patterns, and teaching value. Covers the private-vs-public repo split, deny-list pattern maintenance, orphan-commit publish pattern that prevents `git log` leaks, category-based redaction calibration (methodology/pattern/version-finding/internal), and the `check-redaction.sh`-style CI gate that blocks merges when a deny-listed pattern appears. Use when publishing findings about a CLI harness you don't own, when preparing upstream proposals to an unrelated project, or when archiving a private research repo for public reference.
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Statically enforce a privacy/redaction boundary on an artifact tree before publication, the way an AST checker enforces a code contract. Builds a two-tier gate — a shape-based deny-list (regex over identifier shapes) plus a structure-aware tier that parses JSON/HTML/SVG/Markdown/code and asserts sensitive content cannot appear in meaningful positions — with a strict exit-code contract, label-only output that never echoes the patterns themselves, and false-positive narrowing instead of suppressions. Use when gating a public mirror, a disclosure draft, a wire-capture export, or any artifact that must pass a leak check before it ships, and when a plain line-grep is too coarse to trust.
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Prevents logging sensitive user data with redaction rules, logging policies, and safe log formatting. Use for "PII redaction", "log privacy", "GDPR logging", or "data privacy".
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Evidence-capture and PoC-redaction discipline for bug-bounty submissions: cookie redaction protocol (which fields to mask, Preview annotation / Burp panel hiding / DevTools workflow), PII black-bar discipline (what to mask in other-user data — names, emails, phones, faces — vs what is safe to leave — usernames, trace IDs, request bodies), HAR file sanitization (jq filters for Cookie/Set-Cookie/Authorization headers), Burp Repeater/Intruder screenshot hygiene (hide request body, show only Results table for rate-limit attacks), Chrome DevTools Console PoC patterns (credentials include so cookies are not echoed, labeled console.log), screenshot capture order, filename conventions, post-submission rotation hygiene. Use BEFORE any PoC screenshot, BEFORE attaching a HAR, or whenever preparing evidence with session cookies or other-user PII. Pairs with bugcrowd-reporting and report-writing.
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PII detection and redaction utilities for privacy-compliant conversational AI
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Build automated PII detection and redaction pipelines using spaCy NER, Microsoft Presidio, and AWS Macie integration. Includes confidence scoring, custom entity type definitions, batch processing workflows, and multi-format document scanning for structured and unstructured data sources.
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Redact a network/MITM capture directory — JSONL, JSON, logs, HAR-style dumps — in place before it can be referenced publicly, scrubbing credential tokens, API keys, service ids, UUIDs, emails, home paths, usernames, and device or session hashes while preserving token-class prefixes for analysis and leaving public identifiers untouched. Covers class-preserving substitution, an idempotent re-runnable pass, distinguishing a secret from a public id, and verifying the scrubbed capture through the redaction gate. Use when a wire capture from a sanctioned probe must enter notes, a guide, or a public repo without leaking the session that produced it.
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Redact rendered visual artifacts — Mermaid diagrams, SVGs, and HTML dashboards — before public disclosure, using an ordered longest-first mapping table (specific label, then namespace label, then catch-all placeholder) that swaps sensitive identifiers for descriptive stand-ins while preserving structure, node ids, and teaching value. Covers whole-word identifier replacement, node-label-vs-node-id discrimination, re-rendering the derived image from the redacted source, and verifying the result through the redaction gate. Use when a diagram or dashboard built from internal findings must ship to a public mirror without leaking the names behind it.
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Redact PII in your own code before it reaches Agentforce prompts, models, and logs. Trigger keywords: agentforce pii, pii redaction, data masking llm, prompt pii filter, audit pii leakage. NOT for turning on the platform's own masking, zero-retention and audit-trail controls — use agentforce/einstein-trust-layer. NOT for masking PII in a refreshed sandbox — use security/sandbox-data-masking.
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Review secret handling, brokered execution, redaction, vault boundaries, host approval, and public hygiene.
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Proposes redactions of personal identifiers in pasted text or an attached document (names, contact details, identification and account numbers, dates that identify, quasi-identifiers) and returns a DRAFT redacted copy with consistent placeholder tokens plus a redaction table stating what was replaced, where and why, with a confidence level and a blank reviewer decision per row. Use when the user asks to "redact the names in this document", "de-identify this transcript before we share it", "strip personal data from these notes", "anonymise this report for publication" or "what would need redacting here". Do not use for assessing a planned processing activity, use dpia-draft-pack instead; do not use for working out what data an incident exposed, use data-incident-impact-brief instead. Never declares a document anonymised, safe to release or compliant. Drafts for human review; never approves, authorises or signs off.
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Dockerfile、Compose、Terraform、GitHub Actionsの危険設定を決定的な検出器で検査し、共通Finding schemaとredaction基準で返す。secretや一般の脆弱性はreviewerのsecurity観点を使う。明示的な `/sentinel-review` の指定でだけ使う。
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Dockerfile・Compose・Terraform・GitHub Actionsの差分または指定パスを決定的な検出器で検査し、共通Finding schemaとredaction基準で結果を返す。IaCの危険設定検査専用で、secretや一般の脆弱性はreviewerのsecurity観点を使う。明示的な `/sentinel-review` の指定でだけ使う。
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Add redaction annotations to sensitive fields in schemas to block or hash PII. Use when configuring GDPR/HIPAA compliance, protecting credentials, or blocking internal fields from materializations. Use when user says "protect PII", "hide sensitive data", "hash field", "redact field", "GDPR compliance", "don't capture field", "remove field from Estuary", or "prevent Estuary from capturing field".
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GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Prepare LLM training datasets with NeMo Curator by removing duplicates, filtering low-quality content, and redacting personal information. The skill also covers image, video, and audio curation, including GPU processing and distributed workflows.
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Check federation health, the connectivity between agents in separate installations. Summarize connected peers, trust levels and scores, active sessions, message exchanges, personal information redactions, and threat detections, with a search of stored federation history.
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Check text, code, and configuration files for personal information and secrets before committing, logging, storing, or sending them. Identify categorized findings, flag their locations, and suggest redaction to hide sensitive details.
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Turn plain-language requirements and optional API documentation into OpenShell sandbox network policies in YAML. Define allowed destinations, programs, and API operations, with optional middleware settings for inspecting or transforming traffic and controlling what happens when processing fails.
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