Best Data Discovery Software for GitLab

Find and compare the best Data Discovery software for GitLab in 2026

Use the comparison tool below to compare the top Data Discovery software for GitLab on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

  • 1
    Bold BI Reviews

    Bold BI

    Syncfusion

    $495
    Bold BI by Syncfusion is an AI-powered embedded analytics platform for SaaS companies and software vendors. Product teams add white-labeled, multi-tenant dashboards to their own apps using native SDKs for React, Angular, Vue, .NET, and Blazor, with row-level security and single sign-on. Business users can also build their own dashboards with self-service, drag-and-drop tools. Bold BI has 140+ data connectors, an AI Copilot that turns plain-English requests into charts, and an AI Agent that answers follow-up questions. Pricing is per application, not per user or viewer, by custom quote. Run it in the cloud, in a managed private cloud, or on your own servers, including Docker, Kubernetes, and air-gapped setups. A 30-day free trial is available with no credit card required.
  • 2
    HoundDog.ai Reviews

    HoundDog.ai

    HoundDog.ai

    $200 per month
    An AI-driven code scanning tool aims to adopt a proactive, shift-left approach for safeguarding sensitive information and ensuring compliance with privacy regulations. The rapid evolution of product development often surpasses the capacity of privacy teams, necessitating frequent updates to outdated data maps, which can significantly burden their workload. With HoundDog.ai’s advanced code scanner, vulnerabilities that traditional SAST scanners might miss can be continuously identified, especially those exposing sensitive data in plaintext through various channels like logs, files, tokens, cookies, or external systems. It provides critical insights and remediation techniques, such as the removal of sensitive data, implementation of masking or obfuscation, or substitution of PII with UUIDs. Users receive timely alerts when new data elements are added, categorized by their sensitivity levels, helping to prevent unauthorized product changes from being released, thus mitigating potential privacy breaches. By automating these processes, the scanner effectively reduces the reliance on manual methods, which are often riddled with errors. This innovative solution not only enhances security but also streamlines workflow for privacy teams, allowing them to focus on more strategic initiatives.
  • 3
    IRI Voracity Reviews

    IRI Voracity

    IRI, The CoSort Company

    IRI Voracity is an end-to-end software platform for fast, affordable, and ergonomic data lifecycle management. Voracity speeds, consolidates, and often combines the key activities of data discovery, integration, migration, governance, and analytics in a single pane of glass, built on Eclipse™. Through its revolutionary convergence of capability and its wide range of job design and runtime options, Voracity bends the multi-tool cost, difficulty, and risk curves away from megavendor ETL packages, disjointed Apache projects, and specialized software. Voracity uniquely delivers the ability to perform data: * profiling and classification * searching and risk-scoring * integration and federation * migration and replication * cleansing and enrichment * validation and unification * masking and encryption * reporting and wrangling * subsetting and testing Voracity runs on-premise, or in the cloud, on physical or virtual machines, and its runtimes can also be containerized or called from real-time applications or batch jobs.
  • 4
    MIND Reviews
    MIND is an AI-native data loss prevention and data security platform that protects sensitive information across traditional enterprise environments, GenAI applications, and autonomous AI agents. It continuously discovers and classifies sensitive data across SaaS platforms, endpoints, email, on-premises file shares, GenAI services, and agentic AI environments. MIND is designed to simplify DLP deployment by reducing the need for manually created regular expressions, extensive policy tuning, and professional services. Its Data Detection and Response capabilities analyze billions of signals in real time and enrich security incidents with contextual information to help distinguish meaningful risks from false positives. Automated remediation enables the platform to respond to identified risks without requiring security teams to manually investigate and resolve every event. Real-time loss prevention capabilities can stop sensitive data from leaving the organization or interact with users to remediate risky activity and reinforce security policies. MIND also provides AI DLP agents that apply autonomous capabilities to data security operations and help organizations protect information as AI adoption expands. Supported use cases include data discovery and classification, GenAI security, agentic AI protection, insider risk management, SaaS data protection, endpoint DLP, email DLP, on-premises DLP, and integrated identity and data security. By combining discovery, detection, response, prevention, and user coaching in one platform, MIND helps security teams operate a centralized data protection program across human and AI activity.
  • 5
    Teleskope Reviews
    Teleskope is an innovative platform for data protection that aims to streamline the processes of data security, privacy, and compliance on a large scale within enterprises. It works by consistently discovering and cataloging data from a variety of sources, including cloud services, SaaS applications, structured datasets, and unstructured information, while accurately classifying more than 150 types of entities such as personally identifiable information (PII), protected health information (PHI), payment card industry data (PCI), and secrets with remarkable precision and efficiency. After identifying sensitive data, Teleskope facilitates automated remediation processes, which include redaction, masking, encryption, deletion, and access adjustments, all while seamlessly integrating into developer workflows through its API-first approach and offering deployment options as SaaS, managed services, or self-hosted solutions. Furthermore, the platform incorporates preventative measures, integrating within software development life cycle (SDLC) pipelines to prevent sensitive data from being introduced into production environments, ensure safe adoption of AI technologies without utilizing unverified sensitive information, manage data subject rights requests (DSARs), and align its findings with regulatory standards such as GDPR, CPRA, PCI-DSS, ISO, NIST, and CIS. This comprehensive approach to data protection not only enhances security but also fosters a culture of compliance and accountability within organizations.
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