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OneTrust

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Manages data effectively for compliance and trust.

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Overview

Docket is a vision-first end-to-end testing platform that covers critical flows, eliminates flaky tests, and catches regressions before users encounter them. It automates complex user flows with pixel-perfect precision by capturing on-screen coordinates, enabling testing of elements like canvases, iframes, and popups that traditional selector-based tools cannot.

Key Features:

  • Records on-screen coordinates for pixel-perfect precision
  • Self-healing tests when UI elements move
  • AI-driven handling for dynamic flows
  • Runs tests at scale
  • CI/CD integration
  • Scheduling runs and notifications

Use Cases:

  • Providing reliable end-to-end test coverage for complex UIs
  • Testing dynamic web applications
  • Detecting regressions before release
  • Automating complex user flows that break selector-based scripts

Benefits:

  • Eliminates flaky tests
  • Catches regressions proactively
  • Tests non-standard elements like canvases and iframes
  • Reduces manual scripting effort with AI
  • Ensures flexible, human-like test coverage

Capabilities

  • Embeds compliance and control across the AI lifecycle.
  • Enables data use with real-time policy enforcement for AI-ready data.
  • Streamlines consent and preference management for consumer transparency.
  • Automates third-party management from intake and risk assessment to mitigation and reporting.
  • Enables responsible use throughout the data lifecycle.
  • Scales resources and optimizes the risk and compliance lifecycle.
  • Shortens approval cycles for data enablement with policies directly connected to native data controls in data & AI systems.
  • Automates policy enforcement at machine speed, ensuring compliance without slowing down AI, analytics, and data initiatives.
  • Applies governance policies at project initiation and throughout project lifecycles, ensuring compliance without unnecessary enterprise-wide restrictions.
  • Uses AI-driven classification with both structured and unstructured data to capture four key areas of data context: business, regulatory, consent, and data, and stores this context as machine-readable data labels.
  • Transforms documented privacy, consent, and regulatory policies into enforceable control code to manage data across its entire lifecyclefrom data ingestion and usage to data sharing.
  • Connects data policies directly to real-time, native data controls in modern data and AI systems to shift from policy attestation to policy enforcement.
  • Audits the real-time application of the data controls and gain visibility into the application of column masking and row-filtering controls in order to achieve continuous governance.
  • Automates data policy enforcement and keeps up with the velocity at which AI-driven systems process and utilize data.
  • Predefines conditions, rules, and policies that regulate AI-ready datasets.
  • Creates and monitors data contracts that explicitly define how data can be used based on purpose and regulatory and privacy frameworks.
  • Applies governance dynamically within data pipelines, ensuring continuous compliance, reducing administrative overhead while mitigating risk.

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