Discover, govern, and control your data across every system, so you can deploy AI safely, meet compliance requirements, and eliminate risk at the source.
Most organizations are rushing to adopt AI without understanding the state of their data. Sensitive data is scattered. Ownership is unclear. Policies aren’t enforced consistently. And once data enters AI systems, control is lost.
Data Sentinel offers a unified platform to discover, understand, govern, protect, and operationalize your data across cloud, SaaS, and on-prem environments.
From discovery to remediation, Data Sentinel enables real action, not just insight.
TRUST MODEL
Data Sentinel doesn’t just analyze your data. It operates where your data lives.
Policy-driven control of which data is eligible for AI training and inference. Separation of data layers, continuous oversight, full auditability.
Enforce GDPR, CCPA, HIPAA, PDPL, NDMO, and EU AI Act obligations by design. Automated visibility, privacy-by-design workflows, not reactive checklists.
Govern structured and unstructured data across all cloud, SaaS, and on-prem environments. One unified view - no data left dark.
Continuous assessment of accuracy, currency, and reliability. Eliminate hallucinations and bias at the source - before data reaches AI systems.
Your data never leaves your environment. Maintain full control, ownership, and security at all times.
Combine powerful technology with expert support to drive real outcomes, not just dashboards.
Handle billions of records across cloud, SaaS, and on-prem systems without disrupting operations.
Move beyond visibility to remediation, enforcement, and continuous governance.
Get clear answers to the most common questions about data governance, compliance, data quality and how to safely prepare your data for AI.
A data trust platform helps organizations ensure their data is accurate, governed, compliant, and safe to use—especially for AI, analytics, and regulatory reporting. It combines data discovery, governance, privacy, and quality into a unified system that enables organizations to trust and act on their data with confidence.
Preparing data for AI requires identifying sensitive data, validating data quality, and enforcing policies that control what data can be used for training and inference. Without these controls, organizations risk exposing sensitive data or introducing bias into AI models. Platforms like Data Sentinel ensure only trusted, compliant data enters AI systems.
AI data governance defines how data is selected, controlled, and monitored for AI use. It ensures that only approved, compliant, and high-quality data is used in AI models. This is critical for reducing bias, preventing data leakage, and meeting emerging AI regulations.
Traditional data governance tools focus on visibility and reporting. Data Sentinel goes further by enabling real-time control, policy enforcement, and automated remediation. It also operates directly in your environment (behind your firewall), ensuring sensitive data never needs to be moved or exposed.
No. Data Sentinel operates within your environment, meaning your data never leaves your systems. Unlike SaaS-based systems this approach improves security, supports regulatory requirements, and ensures you maintain full ownership and control over your data.
Data Sentinel automates the discovery, classification, and monitoring of sensitive data across your environment. It identifies compliance gaps, enforces policies, and enables continuous audit readiness to reduce the manual effort required to meet regulatory requirements.
Data discovery and classification is the process of identifying where data exists across your systems and labeling it based on sensitivity, type, and risk. This is foundational for governance, compliance, and AI readiness, as you cannot protect or control data you cannot see.
Reducing data risk requires continuous visibility into where data is overexposed, misused, or non-compliant combined with the ability to take action. Data Sentinel enables organizations to identify risks in real time and enforce policies that prevent misuse or breaches before they occur.
AI data readiness refers to whether your data is accurate, governed, compliant, and suitable for use in AI systems. Without proper data readiness, organizations risk poor model performance, regulatory issues, and data security exposure.
Yes. Data Sentinel is designed for complex enterprise environments and provides unified visibility and control across cloud platforms, SaaS applications, and on-prem infrastructure to ensure that no data is left unmanaged.
