Know Where Sensitive Data Lives.
Control Who Can Access It.
Prove It’s Protected.
Sensitive data is moving faster than most security teams can track, i.e., across cloud platforms, SaaS apps, databases, files, analytics tools, legacy systems, and AI workflows.
DataStealth gives you one platform to find sensitive data everywhere, protect it at the field level, and enforce access policies in real time.Reduce exposure, respond to threats faster, simplify audits, and give your stakeholders confidence that your sensitive data is fully resilient to any threat, including a breach.
Find data sources that contain sensitive information across on-premise, cloud, SaaS apps, legacy environments, and AI.
Learn MoreAutomatically classify sensitive data in every application, database, file share, or anywhere else it exists.
Learn MoreProtect sensitive data using tokenization, encryption, and masking to safeguard your data without disrupting applications or workflows.
Learn MoreMove to fully controlling your data by combining discovery, classification, data-layer protection, and real-time enforcement.

Locate sensitive information across cloud accounts, SaaS, and legacy/on-prem data stores, then reduce exposure with policy-driven remediation. Improve security posture across distributed cloud environments without slowing delivery.
Identify over-permissioned access and enforce least-privilege policies to reduce insider and accidental exposure. Centralize access control decisions around what data is accessed, by whom, and why.


When an event happens, move from “we got an alert” to “we know what’s exposed and confirm it’s protected and unusable to the attacker.”
See where regulated, confidential, and high-risk data lives across cloud, SaaS, on-prem, and hybrid environments.

Apply tokenization, encryption, and dynamic data masking to limit what users, apps, and systems can see.
Enforce least privilege around the data itself, not just the application or network layer.
Avoid the “security vs productivity” tradeoff by protecting data while keeping systems usable – so protection sticks instead of being bypassed.
Seamless integration with existing tools (SIEM/SOAR/ITSM/IdP) so alerts become actions, actions become evidence, and security teams stay in one workflow.
Generate evidence that sensitive data is protected, policies are enforced, and risk is being reduced.

Discover sensitive, regulated, and business-critical data across cloud, SaaS, on-prem, and hybrid environments.

Classify data with context so your team can prioritize the highest-risk stores, users, systems, and workflows.

Apply tokenization, encryption, and masking in ways that protect data while keeping business systems usable.

Enforce policies based on user, role, context, and data type, reducing reliance on broad application-level access.

4.8/5 rating on G2 and other review platforms for data-centric security and ease of deployment.

Named a top data security platform for giving organizations visibility into shadow IT and high-risk data.

DataStealth is recognized in Forrester’s Data Security Platform Landscape Report and trusted by highly regulated organizations that cannot afford data exposure or downtime.
A data security platform is a unified system that combines data discovery, classification, protection, and governance capabilities into a single solution.
It replaces the traditional approach of managing separate tools for DLP, encryption, database security, and access control.
The platform provides centralized visibility into where sensitive data exists across an organization's infrastructure and applies consistent protection policies regardless of where that data resides – on-premise systems, cloud environments, or SaaS applications.
Enterprise buyers evaluating data security platforms for encryption and compliance typically consider vendors based on their specific environment and requirements.
For organizations needing to protect data without code changes across hybrid environments – particularly those with PCI or HIPAA requirements – DataStealth's proxy-based tokenization and encryption eliminates the need for application rewrites while removing systems from compliance scope entirely.
Traditional DLP solutions focus primarily on preventing data exfiltration by monitoring and blocking data transfers at network egress points and endpoints.
Data security platforms take a broader approach: they discover where sensitive data exists (including unknown or shadow data stores), classify it based on content and context, protect it through encryption or tokenization at rest and in transit, and govern access based on policies.
While DLP is reactive – alerting when someone tries to move data they shouldn't – modern data security platforms are proactive, ensuring sensitive data is protected before it can be exposed.
Start by mapping your data landscape: where does sensitive data exist today, and where is it flowing? Identify your compliance requirements (PCI, HIPAA, GDPR) and determine which systems need to remain in or out of scope.
Evaluate platforms against three criteria: coverage (can it discover and protect data across your entire environment, including legacy systems?); architecture (does deployment require agents, code changes, or application modifications?); and integration (does it connect with your SIEM, identity provider, and ticketing systems?).
Run a proof-of-concept focused on your highest-risk data stores and measure time-to-value, false positive rates, and performance impact.
Yes, and this capability is increasingly critical. Modern data security platforms can identify when sensitive data is being fed into AI systems – whether enterprise tools like Microsoft Copilot or external services like ChatGPT.
They can classify prompts and training data for PII, PHI, or proprietary information and either block the request, redact sensitive elements, or alert security teams.
This "GenAI governance" capability has become a primary differentiator in 2025 as organizations balance AI adoption with data protection requirements.
Data Security Posture Management (DSPM) is a subset of the broader data security platform category. DSPM tools focus specifically on discovering data across cloud environments, classifying it, and identifying posture risks (misconfigurations, overly permissive access, compliance gaps).
However, most DSPM tools are strong on discovery but weak on enforcement – they tell you where problems exist but don't block data exfiltration in real-time.
A full data security platform combines DSPM capabilities with active protection mechanisms like encryption, tokenization, and real-time access controls. The market is converging as DSPM vendors add protection features and traditional security vendors add discovery capabilities.