Results for “data-integrity”

Use to finalize Cell Press display items — column-width sizing, minimum fonts, RGB, show-the-data with defined error bars/n/replicate type, scale bars, colorblind-safe palettes, multi-panel discipline, stand-alone legends, and image integrity.

Validate backup integrity through cryptographic hash verification, automated restore testing, corruption detection, and recoverability checks to ensure backups are reliable for disaster recovery and ransomware response scenarios.

Implement the NIST Privacy Framework CONTROL function covering CT.DM data management, CT.DP data processing policies and procedures, and CT.PO disassociated processing. Provides technical control architectures, data management workflows, and de-identification implementation guidance.

Guides privacy impact assessment for emerging technologies including IoT, blockchain, AR/VR, quantum computing, and digital twins. Covers risk identification methodology, proportionality assessment, and technology-specific privacy challenges. Activate when evaluating new technology adoption, innovation projects, or emerging tech procurement. Keywords: PIA, emerging technology, IoT, blockchain, AR/VR, quantum computing, digital twins, innovation privacy.

Enforces a strict input boundary protocol (detect, classify, filter, verify) to ensure untrusted data never reaches business logic raw.

Persists app data on Android using Room, SQLite, SharedPreferences, DataStore, and encryption.

Validates data quality with completeness, consistency, accuracy checks and generates quality reports

Engineering privacy and data governance: PII detection, data flow mapping, consent patterns, GDPR/CCPA-compliant implementation, DPIA. Use when privacy-by-design is needed.

Build an append-only log integrity chain using SHA-256 hash chaining for tamper detection. Each log entry is hashed with the previous entry's hash to create a blockchain-like structure where modifying any entry invalidates all subsequent hashes.

Enforces complexity analysis, correct data structure selection, vectorization, and algorithm implementation discipline at principal-engineer level.

Use when processing, storing, or transmitting any data that might contain personally identifiable information (PII) or sensitive personal data. This skill provides procedures for identifying, protecting, and handling personal data in compliance with privacy regulations (POPIA, GDPR, CCPA).

Enforces a strict input boundary protocol (detect, classify, filter, verify) to ensure untrusted data never reaches business logic raw.

Use when implementing GDPR compliance, handling data subject requests, conducting DPIAs, managing consent, or responding to data breaches - covers all key GDPR requirementsUse when ", " mentioned.

Use to finalize PNAS display items — column-width sizing (~9 / 11.4 / 18 cm), minimum font sizes, show-the-data with n and defined error bars, scale bars, colorblind-safe palettes, legend structure, image integrity, and color-mode notes. Enforces figure rigor before submission.

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.

Use this skill when the user asks about Electronic Logging Device (ELD) compliance under 49 CFR 395.20-38 — ELD mandate, AOBRD vs ELD transition, ELD exemptions, ELD malfunctions, 8-day paper backup, ELD certification on FMCSA registered list, data file integrity, edits + annotations, driver portability, and how to handle ELD failures at roadside. Cite 49 CFR 395.20-395.38.

Analyse experiment results, run validity checks (SRM, exposure parity, data integrity, novelty/primacy), interpret causally, make a ship/iterate/kill decision against the pre-declared rule, and append to cumulative learnings. Forces honest readouts — strips significance claims from underpowered or peek-violating tests; never lets directional results masquerade as causal wins. Load when results exist, or when the user says "read out this experiment", "analyse the test", "did the test win", "interpret the results", "what did we learn", "ship or kill", or when the experimentation orchestrator routes here.