“data-integrity”的搜索结果

Design and operate a data integrity monitoring programme based on ALCOA+ principles. Covers detective controls, audit trail review schedules, anomaly detection patterns (off-hours activity, sequential modifications, bulk changes), metrics dashboards, investigation triggers, and escalation matrix definition. Use when establishing a data integrity monitoring programme for GxP systems, preparing for inspections where data integrity is a focus area, after a data integrity incident requiring enhanced monitoring, or when implementing MHRA, WHO, or PIC/S guidance.

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Picklist governance for Salesforce admins — Restricted vs Unrestricted, Global Value Sets vs local picklists, value deactivation that breaks reports, dependent picklists with deactivated controllers, and the 'value not in picklist' phantom-data problem. Covers the per-record-type value selection model, the API-vs-label mismatch trap, and the mass-replace-values runbook. NOT for auditing an org where invalid values already exist or record-type mappings have drifted — use admin/picklist-field-integrity-issues. NOT for creating a picklist field or wiring up a dependent picklist — use admin/picklist-and-value-sets.

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Check industrial process data servers for unauthorized clients and suspicious data patterns using a Python detector. Provides alerts for missing or constant values and investigation checks for attackers moving between IT and operational technology networks.

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• Checking industrial historian servers for unauthorized clients
• Investigating missing or suspiciously constant process data
• Reviewing signs of movement between IT and OT networks

Coordinates an active production incident by establishing command, reducing blast radius, preserving evidence, communicating verified impact, restoring service, validating recovery, and creating owned follow-up actions. Use for outages, severe degradation, security or data-integrity events, failed releases, and urgent operational triage. Not for a normal local bug, routine alert tuning, or a postmortem after the incident is already closed.

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Implement audit trail functionality for R projects in regulated environments. Covers logging, provenance tracking, electronic signatures, data integrity checks, and 21 CFR Part 11 compliance. Use when an R analysis requires electronic records compliance (21 CFR Part 11), when you need to track who did what and when in an analysis, when implementing data provenance tracking, or when creating tamper-evident analysis logs for regulatory submissions.

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Use when picklist data quality has degraded: unrestricted picklists accepting garbage API values, dependent picklist relationships bypassed by integrations, record type picklist value mappings drifting out of sync, or orphaned values appearing in reports. NOT for the value-deactivation runbook or the API-name-vs-label trap — use admin/picklist-data-integrity. NOT for initial picklist creation or Global Value Set design — use admin/picklist-and-value-sets. NOT for record type configuration itself — use admin/record-types-and-page-layouts.

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Design or repair lookup filters that constrain a child lookup using fields from the parent record (or a sibling record on the same object). Triggers: 'limit lookup based on another field', 'cross-object lookup filter', 'lookup filter $Source vs $User', 'lookupFilter block in field-meta.xml', 'booleanFilter with three filterItems', 'picker still shows every record after deploy', 'lookup filter error message not translated'. NOT for choosing lookup vs master-detail - use admin/lookup-and-relationship-design. NOT for duplicate matching rule criteria - use admin/duplicate-management. NOT for the rest of the CustomField element set - use admin/custom-field-creation.

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Review code changes for correctness defects, regressions, security risks, data-integrity problems, concurrency hazards, and missing meaningful tests. Use for pull requests, branches, commits, patches, or working-tree reviews; do not use for implementation unless the user separately requests fixes.

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Detect cyber attacks targeting OT historian servers (OSIsoft PI, Ignition, Wonderware) that sit at the IT/OT boundary and serve as pivot points for lateral movement between enterprise and control networks, including data manipulation, unauthorized queries, and exploitation of historian-specific vulnerabilities.

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Detect cyber attacks targeting OT historian servers (OSIsoft PI, Ignition, Wonderware) that sit at the IT/OT boundary and serve as pivot points for lateral movement between enterprise and control networks, including data manipulation, unauthorized queries, and exploitation of historian-specific vulnerabilities.

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Detect cyber attacks targeting OT historian servers (OSIsoft PI, Ignition, Wonderware) that sit at the IT/OT boundary and serve as pivot points for lateral movement between enterprise and control networks, including data manipulation, unauthorized queries, and exploitation of historian-specific vulnerabilities.

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Detects data integrity issues including orphaned records, broken foreign key relationships, constraint violations, and provides automated fix migrations. Use for "data integrity", "orphaned records", "broken relationships", or "data quality".

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