5 Best Practices for Secure Data Sharing

In our globalized world, data sharing is table stakes for organizations that want to innovate and compete. Gartner has predicted that enterprises that share information will outperform those that do not on most business metrics, and leading organizations like Snowflake and AWS are already offering data exchanges to simplify the process. But as consumers become increasingly aware of how their data is being handled and threats abound, companies are accountable for ensuring their data sharing practices are secure and compliant.

In this blog, we’ll explore what it means to share information securely, the most common challenges to doing so, and best practices for incorporating data security into data sharing processes.

What is Data Sharing?

Data sharing refers to the exchange of information between individuals, departments, organizations, or systems in order to facilitate collaboration, decision making, and analysis. This involves putting standardized processes, technologies, and legal safeguards in place to provide access to data for internal and/or external colleagues, vendors, and partners. Data is most often shared internally, externally, and via data exchange platforms, which are environments where data is shared among multiple stakeholders on a broad scale.

Why is Secure Data Sharing Important?

More than 97% of today’s executives report investing in data, analytics, and AI initiatives, and a majority also recognize the negative repercussions of not doing so. Within these growing volumes of data, companies are bound to have sensitive information. Therefore, it’s paramount that their data sharing practices prioritize data security and privacy.

Amid growing consumer awareness of how personal information is used, data breaches at well-known companies, and mounting data-centric regulations, the link between data sharing and security has never been more clear. Failing to implement robust data security capabilities to prevent unauthorized access, mitigate threats, and achieve compliance could cost organizations millions in fines and lost revenue, not to mention unquantifiable damages to brand reputation and customer trust.

Recently, Meta was fined $1.3 billion for sending EU users’ data to the U.S. and GoodRX was ordered to pay $1.5 million for sharing personal health information (PHI) with third parties like Facebook and Google without consent. Avoiding situations like these can give organizations a competitive advantage simply by staying out of hot water with regulators and consumers.

Top Data Sharing Challenges

If data sharing is so mainstream, why is it such a challenge for leading companies to get right? Gartner surveyed nearly 300 Chief Data Officers (CDOs) and identified the following five challenges:

Data Governance & Management

As organizations collect more data, effective governance and access management frameworks are essential to scaling data use without losing control. But in a survey of more than 600 data practitioners, 41% said they did not feel that they have enough people to manage or analyze their data, and 36% reported simply having too much data. It goes without saying that if you can’t govern or manage data, you can’t ensure it’s being shared securely.

Not only does a lack of data governance and management frameworks make it more difficult for users to locate assets, but it also increases the likelihood that those users will create data copies that evade standard data access controls. Without visibility into how information is being accessed, duplicated, or exchanged, it’s substantially more difficult to proactively mitigate risks.

Data Compliance & Regulatory Requirements

According to the United Nations Conference on Trade and Development (UNCTAD), more than 70% of countries now have regulations protecting individuals’ data and privacy. But the number of laws globally doesn’t even scratch the surface of contemporary data sharing requirements. Data use agreements, contracts, and other non-federal mandates put additional guardrails on how organizations can and cannot handle data.

Data regulations require organizations to ensure transparency, informed consent, and comprehensive data monitoring and auditing capabilities. These can be elusive on their own, but the task of translating legal language into data access policies often proves to be an additional hurdle. The more standards that organizations are subject to, the more difficult it becomes to author sufficient policies, obtain legal sign off, and enforce rules at scale. If even one of these components is missing, it will likely halt data sharing efforts altogether.

Data Privacy Risks

The Pew Research Center reports that “81% of Americans think the potential risks of data collection by companies about them outweigh the benefits.” This comes as no surprise, given the uptick in data privacy violations by major companies in recent years.

Often, these violations occur because organizations are unaware of their levels of risk, and have failed to adequately assess their cyber threat landscape. For instance, data de-identification techniques alone cannot guarantee that data will be protected, nor can one-dimensional authentication methods. At the same time, data privacy is not one-size-fits-all. Organizations must thoroughly assess their unique risks, both internally and externally, and implement the appropriate controls to close privacy gaps and enable secure data sharing.

Insufficient Tools & Technology

The shift to the cloud from traditional on-premises architectures has greatly simplified data operations in some ways, but has made them more complex in others. Most leading cloud data platforms now offer at least some data governance features. However, in multi-cloud environments their capabilities are mismatched and disparate. Without consistent controls, it’s easy for sensitive information to be shared either inadvertently or intentionally.

“Despite most CISOs having a full arsenal of tools for protecting data in the cloud, the proliferation of cloud players such as Snowflake, Databricks, Google BigQuery, Amazon Redshift, and other cloud-based SaaS solutions has accelerated data sharing to a breaking point,” said Matthew Carroll, Co-Founder and CEO of Immuta, in the 2023 Data Access & Security Trendbook. “Traditional approaches that worked for on-premises environments just can’t keep up with the exponential growth in the number of users, data sources, and policies that must be governed, managed, and secured in today’s environment.”

Fear

Finally, organizational cultures that are rooted in legacy processes hinder some teams from taking steps toward secure data sharing. In these cases, ingrained processes and mistrust of third parties often make decision-makers uncomfortable with exchanging information, especially externally. Ultimately, Gartner notes, this leads to data hoarding and a reluctance to adopt next-gen tools that allow data sharing to be done safely and efficiently.

5 Best Practices for Secure Data Sharing

1. Build Data Security Measures Into Tech Stacks

As the challenges mentioned above make clear, traditional approaches to data security, like perimeter defenses and static access controls, will no longer cut it for cloud data protection. At the same time, protecting against every potential risk to your data stack is nearly impossible. The most effective mitigation tactic is to build security measures into the foundation of your tech stack, so as to proactively protect data no matter where it lives or what state it is in.

“Data sharing is going to get bigger, but there have to be more security controls and mechanisms around it. I think it’s still new and it sounds good, but there are still a lot of unknowns.”
-Scott Barsness, Architect/Solution Engineer at BOK Financial, 2023 Data Access & Security Trendbook

Leveraging a dedicated data security platform that can consistently enforce controls across any platform and any data user – whether internal or external – should be fundamental to your data architecture.

2. Understand Your Data Through Discovery & Classification

With data volumes growing exponentially and cloud ecosystems becoming increasingly complex, platform, security, and governance teams need a way to identify and classify the sensitive information in their possession. Data discovery tools provide visibility into the types of data in your ecosystem, so you can classify and tag it accordingly. This capability is especially powerful when the process is automated, helping to eliminate bottlenecks caused by manual inspection.

Having insights into the type of sensitive data that exists in your ecosystem allows you to proactively identify potential vulnerabilities and ways to mitigate them. This is a critical step in establishing the governance and access control frameworks required to enable secure data sharing.

3. Implement Flexible Data Access Controls

With internal and external data sharing now central to successful business operations, organizations have a need for dynamic data access controls that are both granular and scalable. This is underscored by the popularity of distributed data architectures like data mesh, in which data owners are able to create and enforce their own domain-centric controls.

Attribute-based access control (ABAC) is the best solution for these scenarios, as it offers flexibility, agility, and minimal overhead. Compared to RBAC (role-based access control), ABAC requires 93x fewer data policies to accomplish the same security objectives. By basing access permissions on several dimensions, including metadata about the object, user, and purpose for access, this approach ensures that users can only access the right data at the right time and for the right reasons. This simplifies not just secure data sharing, but compliance with rules and regulations as well.

4. Continuously Monitor & Regularly Audit Activity

Data discovery and access control capabilities alone can’t eliminate threats entirely. To avoid becoming the next headline for violating data sharing standards, organizations must take a proactive approach to data monitoring and auditing for compliance.

Continuous monitoring allows data teams to detect and address anomalies in real time, so as to contain the potential fallout from unauthorized access or sharing. Regular audits further reinforce data security efforts by providing a comprehensive assessment of data sharing practices and access controls. This can help verify compliance with internal policies and external regulations or agreements, as well as highlight any gaps in coverage. Together, monitoring and auditing strengthen data security posture management and ensure data sharing is done securely and with integrity.

5. Facilitate Collaboration Across Data Platform, Security, and Governance Teams

As with most business functions, enabling secure data sharing is a team effort. The data platform, security, and governance teams play interconnected roles in ensuring that data sharing frameworks work seamlessly for all stakeholders.

By working collaboratively, these teams can establish efficient processes, collectively identify potential risks, and align data security and sharing efforts to business objectives.

• Data platform teams are responsible for communicating data requirements and implementing the appropriate controls
• Data security teams oversee the security of the organization’s data infrastructure, and assess and create plans to address potential risks
• Data governance teams are tasked with ensuring policies align with regulatory requirements and carrying out audits to prove compliance

Together, these stakeholders have a full view of data sharing practices, any security gaps or vulnerabilities, and what it takes to remain compliant. Therefore, their guidance and collaboration is essential.

Next Steps for Enabling Secure Data Sharing

Whether you’re a large enterprise with thousands of data users or a small startup that’s putting a roadmap in place for future data sharing needs, both security and privacy should be top priorities. The good news? You can begin overcoming common challenges and simplifying secure data sharing processes by following these five best practices. The even better news? A single platform can help streamline those best practices and put them into practice.

Immuta helps organizations unlock value from their data by providing an integrated data security platform for sensitive data discovery, security and access control, and activity monitoring. Automated data classification, dynamic attribute-based access controls, and always-on anomaly detection capabilities take the guesswork out of the most critical aspects of data security, while plain language policy authoring enables better collaboration across technical and non-technical stakeholders. With better data security and streamlined operations, organizations can get the right data to the right people so they can share information and maintain a competitive edge.

To see how Immuta enables data sharing for Snowflake, check out this blog.