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October 3, 2023

Navigating the Landscape of Secure Data Sharing

Organizational data is critical to developing new value for customers and moving in new business directions. Secure data sharing ensures these valuable assets are protected while still allowing for collaboration, innovation, and strategic decision-making. It builds trust with stakeholders, partners, and customers and demonstrates a company’s commitment to data privacy and security.

At a time when data breaches and cyber threats are at an all-time high, data sharing security isn’t merely a strategy; it’s a competitive advantage.

Guarding the Digital Galaxy: Why Organizations Need Secure Data Sharing

Remember the massive cybersecurity attack known as SolarWinds? It was as bold as it was unfathomable. How could a nearly $2 billion US company specializing in business software expose itself to an attack by a group of Russian state-sponsored hackers called Cozy Bear? And why are other major organizations, including Microsoft, still struggling to stay ahead of the group’s ongoing assaults?

The SolarWinds attack exposed critical gaps in software supply chain security that have data sharing implications. And the ramifications were enormous, including national security risks, financial losses, and reputational damage.

Unfortunately, organizations continue to share data under the assumption their existing security protocols are adequate. Sadly, as the SolarWinds incident shows, it only takes one weak point to create a domino effect that leads to unauthorized access and significant breaches. It also reaffirms why secure data sharing isn’t just advisable, it’s also a vital necessity.

With that cautionary tale in mind, let’s explore why secure data sharing isn’t just advisable — it’s an absolute necessity.

How is the Data Sharing Landscape Changing?

It’s no surprise that, as cyber threats and data breaches become more prevalent, consumer attitudes toward how organizations collect, share, and use their data are changing. Organizations, too, must find solutions to inefficient data practices, particularly with respect to more stringent privacy regulations.

Significant changes we’ve seen and expect to see include:

  • The rise of decentralized systems. Technologies like blockchain are making it easier to share data securely without a central authority, enhancing security and increasing transparency and trust.
  • Data privacy laws like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) reflect increasing regulatory scrutiny. They’ve set new standards for data sharing, requiring organizations to take extra measures, including adopting robust data sharing security protocols.
  • Secure data sharing in clouds is revolutionizing how data is stored and shared, opening new avenues for collaboration. But it’s also introduced challenges in maintaining security and compliance across multiple jurisdictions and platforms.
  • Machine learning and AI-driven data access governance platforms are more sophisticated than ever, allowing for real-time monitoring, dynamic access controls, and advanced analytics to refine data sharing practices.
  • Cross-industry data sharing initiatives are becoming more common, necessitating versatile platforms able to handle various data types and comply with assorted regulatory frameworks.
  • Data portability features now enable consumers to safely transfer their data between service providers.
  • Advanced real-time secure data sharing solutions for the healthcare, finance, and logistics sectors are able to handle the scale and speed of such operations.

Lastly, ethical data sharing considerations remain top of mind, particularly in areas like healthcare. Organizations are formulating guidelines that ensure their data is shared in a way that respects individual privacy and responsibility.

One thing is sure — the data sharing landscape is in a constant state of flux, and organizations that want to stay ahead must adapt to these changes by adopting best practices and investing in state-of-the-art and secure data sharing platforms and tools.

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The Roll of AI in Data Sharing

Data sharing and collaboration methods have their fair share of challenges, including poor communication channels, a lack of standardization in data formats and structures, and the issue of data governance and compliance.

AI-powered data sharing tools use machine learning algorithms to analyze data and provide actionable insights. They help businesses overcome the obstacles common with traditional data management methods, facilitating efficient data sharing.

There are various types of AI tools for data sharing, including predictive analytics, natural language processing (NLP), and machine learning, which can facilitate efficient data sharing and enable team members to collaborate in real time. For instance, you can use NLP to extract insights from unstructured data sources such as texts, emails, and social media messages.

Key features of AI-powered data sharing platforms include:

  • Centralized data storage
  • Automated data use monitoring
  • Real-time analysis
  • Security and privacy controls

By implementing these AI-based platforms, organizations can enhance data sharing in various ways.

  1. Improved data quality and accuracy, reducing the likelihood of errors.
  2. Streamlined data sharing and communication, providing real-time data updates that can lead to better informed decisions.
  3. Enhanced data security and privacy, including access controls, encryption, and auditing.
  4. Real-time data analysis that delivers a clearer understanding of patterns, trends, and correlations, leading to faster identification of problems and opportunities.

Sectors that have successfully implemented AI-based data sharing technologies include:

  • Healthcare. From improved patient outcomes to optimized healthcare resources, AI tools are dramatically transforming how people receive health services. AI tools are used to analyze patient data and identify potential health risks so more personalized treatment plans can be designed.
  • Financial services. AI data sharing platforms are impacting how financial institutions store, analyze, and share data, enabling more accurate decision-making, improving risk management, and enhancing customer service experiences through personalized offerings.
  • Retail and eCommerce. AI-driven sharing tools are helping retailers deliver tailored customer experiences and optimize operations. They’re being used for everything from analyzing customer data to identifying product trends.

Data Sharing Best Practices

Whether you’re a large enterprise or an emerging startup, data security and privacy should be top priorities. Following these best practices can help you overcome common data sharing challenges. And investing in an automated data security platform can streamline the process and help you put them into practice faster.

  1. Conduct a data audit. Your first step is identifying what you’re working with. Take stock of your organization’s types of data and classify them according to organizational importance and sensitivity.
  2. Define data access levels. Few people need access to all of an organization’s data. Assign roles and responsibilities based on functions and needs. And ensure team members have just enough access to data to perform their tasks efficiently.
  3. Use encryption. Like installing high-quality locks on your home’s doors, encryption is a vital security measure that protects data from unauthorized access. It scrambles data into an unreadable format, ensuring that it remains unintelligible to unauthorized users, even if it is intercepted or leaked during sharing.
  4. Implement multi-factor authorization. It enhances security, mitigates the risk of stolen or compromised sensitive information, ensures regulatory compliance, and boosts customer confidence.
  5. Monitor and log activity. Set up digital “surveillance” by continuously tracking who accesses your data and use your secure data sharing platform to log this information for auditing purposes.
  6. Regularly update security protocols. Protecting your data from every potential risk is a nearly impossible task. But by building security measures into your technology, you can proactively protect data no matter where it lives.

Ready to Take the Next Step in Securing Data Sharing?

Secure data sharing, of course, is not just a best practice; it’s a necessity. The risks of not securing your organization’s data properly before sharing it can be disastrous, leading to financial losses, regulatory penalties, and irreversible damage to your brand’s reputation. Moreover, a data breach can erode customer trust and make it difficult to rebuild relationships and stay competitive.

Velotix offers organizations a unique AI-powered data protection and access permissions platform that mitigates risks, automates policy management at scale, and provides controlled access to data. It helps enterprises easily track which data is available, where it’s located, and who should be granted access.

Our policy-based access control (PBAC) solution dynamically determines access privileges based on policies and rules versus role-based access control (RBAC). It helps you meet today’s data sharing challenges, including regulatory compliance and maximum data utilization with minimum risk. With better data sharing security and streamlined operations, organizations can get the right data to the right people at the right time so they can share information and maintain a competitive edge.

In the end, your organization’s data security is only as robust as the elements and people that support it. Investing in proven solutions that ensure your data remains secure from start to finish helps you avoid data compromise and ensure safe data sharing.

Interested in learning more about specific use cases for sensitive data sharing? Contact Velotix today to learn more or book a demo.