Protect data analytics
Data analytics drives innovation and decision-making, with data scientists using analytical techniques to extract insights and solve complex problems. However, managing sensitive or proprietary data poses significant privacy and security challenges.
Organizations using data analytics face challenges like increasing data volume, siloed databases, and outdated records. Failing to protect sensitive data in those analytics can lead to regulatory issues, operational disruption, and lost competitive advantage.
Skyflow Data Privacy Vault addresses these challenges by isolating, protecting, and governing sensitive data and enabling secure analytics workflows.
Risks in data analytics
Organizations across industries rely on analytics for informed decision-making but face challenges safeguarding sensitive data. Key examples include
- Healthcare: Analyzing patient records to improve outcomes and advance research.
- Finance: Using customer data for fraud detection, risk management, and personalized services.
- Retail: Studying customer behavior to optimize inventory and tailor recommendations.
Over the past decade, analytics platforms have proven to be frequent targets of data breaches, exposing critical vulnerabilities:
- Target 2013: Attackers breached Target’s analytics systems, exposing payment data for over 40 million customers and highlighting analytics platforms as potential attack entry points.
- Strava 2018: Strava’s global heatmap, based on user activity data, unintentionally revealed sensitive military locations and personnel movements, posing national security risks.
- Microsoft 2021: A Microsoft Power Apps misconfiguration exposed over 38 million records, including COVID-19 contact tracing and other sensitive data.
Secure your analytics
Businesses must adhere to strict privacy protocols to thrive in a data-driven world. The following solutions focus on ways to isolate, protect, and govern data.
Isolate analytics
Partition your data to make only anonymized or tokenized data available for analytics, isolating sensitive information from frontend applications.
Use case: A financial institution uses tokenized data to analyze customer spending, separating sensitive details like credit card numbers and account information.
Protect analytics
Use your own tokens to migrate large datasets and associated tokens into your vault, and then pass that data to a datastore (like Snowflake) to seamlessly continue your analytics lifecycle.
Use case: A global e-commerce company tokenizes sensitive data before transferring sales and customer information into Snowflake for analysis.
Govern analytics
Establish fine-grained access controls to restrict the visibility of sensitive datasets. Allow access only to authorized users or teams for querying or viewing specific data.
Use case: A marketing team analyzing sales trends accesses anonymized data while the finance team views detailed account information with specific permissions.
Protect analytics in a data privacy vault
Skyflow’s Data Privacy Vault securely isolates, protects, and governs sensitive analytics data. It minimizes risks by tokenizing information while allowing key operations via polymorphic encryption, enabling safe analysis without exposing raw data.
With seamless integration via SDKs and APIs, Skyflow simplifies leveraging data analytics in web or mobile applications while maintaining privacy and compliance.
Unlock the power of protected data analytics. Learn how to get data into Skyflow or review security best practices.
