Results for “data-exposure”

Use this when reviewing or designing Firebase security rules for Firestore or related client-accessed Firebase data. Trigger on requests about Firebase rules safety, access constraints, auth-claim checks, or client data exposure.

Guides secure design and review of Lightning Web Components, covering DOM safety, Apex data exposure, and Lightning Web Security.

Assess AI/ML systems for prompt injection, jailbreak vulnerabilities, model inversion risk, data poisoning exposure, and agent tool abuse, with MITRE ATLAS mapping and guardrail recommendations.

Scans codebases for security vulnerabilities by reasoning about code context, data flow, and component interactions, covering injection flaws, secrets exposure, authentication issues, and weak cryptography across multiple languages.

Use to find personal data support collects and stores that nothing actually uses, and to stop collecting it. Trigger for "do we collect too much data", "which fields do we actually use", data minimisation review, "why do we ask customers for this", reducing breach exposure, or a form that grew a field every quarter.

Scan for Bluetooth Low Energy devices, enumerate GATT services and characteristics, and detect security vulnerabilities such as unencrypted data exposure and known vulnerable device fingerprints.

Simulate an insider threat or post-breach attacker to identify lateral movement paths, privilege escalation vectors, and sensitive data exposure within a corporate network.

Transform raw data into compelling narratives with structured story frameworks, visualization techniques, and presentation templates for executive audiences.

Guides data analysis, EDA, and ML tasks by teaching, diagnosing MCPs, and deciding with the user, offering multiple options and documenting decisions.

Configures sensitivity labels, DLP policies, and endpoint data protection rules in Microsoft Purview to safeguard sensitive information across Exchange, SharePoint, OneDrive, Teams, and endpoints.

Design clear, accessible data visualizations with appropriate chart selection and styling.

Converts CSV, Excel, or JSON data into a polished, interactive visual report page with KPI cards, charts, data tables, and insights.

Profile an unfamiliar dataset — shape, grain, quality, nulls, distributions, and duplicates — before any analysis is trusted.

Crea gráficos profesionales con Matplotlib, Seaborn y Plotly, desde exploración rápida hasta visualizaciones publicables, eligiendo el tipo de gráfico adecuado para cada historia de datos.

Analyze datasets to answer defined questions through statistical methods, trend identification, hypothesis testing, and correlation analysis. Use when the user needs evidence-backed findings or decisions from data; use exploratory-data-analysis instead for open-ended first-pass profiling before questions are defined.

Transform audit data, performance reports, and structured analyses into polished visual deliverables. Generates presentation decks (PPTX), interactive HTML dashboards, or styled markdown reports with charts. Includes a customizable design system with forest green accent and warm cream backgrounds. Use when the user has analysis data and needs a visual deliverable.

Use this skill for any data or analytics task — querying databases, analyzing metrics, exploring data warehouses, processing datasets, or creating visualizations.

Data analysis workflow from import through modeling and communication. Use when analyzing a dataset, exploring data, building a statistical model, selecting features, or communicating findings to stakeholders.

Big Data Based Analysis Skill

Answer any data question, scaling effort from a one-number lookup to a full stakeholder-ready analysis with a recommendation.

Design leakage-safe feature engineering strategies for tabular/time-series datasets. Use when: (1) preparing model-ready features, (2) selecting transformations and encodings, (3) documenting feature lineage. NOT for: model serving or infra provisioning.

Collects publicly available information about a target organization to identify attack surfaces, social engineering targets, technology stacks, and credential exposures for authorized security testing.

Execute and analyze AI Data Poisoning attacks. By subtly injecting malicious or targeted misinformation into an LLM's training or fine-tuning dataset, an attacker can covertly manipulate the model's future outputs, implant backdoors, or enforce biases without altering the model architecture.

Use this skill for any data or analytics task — querying databases, analyzing metrics, exploring data warehouses, processing datasets, or creating visualizations.

Review protected asset policy and risky local data surfaces.

Detect poisoned training data and backdoored models across the ML pipeline using statistical analysis, activation clustering, and spectral signatures.

Detect insider threat behavioral indicators including unusual data access, off-hours activity, mass file downloads, privilege abuse, and resignation-correlated data theft.

Provides a structured risk scoring methodology for Data Protection Impact Assessments aligned with ENISA threat taxonomy and ISO 29134. Covers likelihood and severity assessment, risk matrix construction, inherent vs residual risk calculation, and risk appetite thresholds per EDPB WP248rev.01 guidance. Keywords: risk scoring, DPIA risk matrix, likelihood, severity, ENISA, ISO 29134, residual risk, risk appetite.

Investigates insider threat incidents involving employees, contractors, or trusted partners who misuse authorized access to steal data, sabotage systems, or violate security policies. Combines digital forensics, user behavior analytics, and HR/legal coordination to build an evidence-based case.

Time-Series Data