Mirza Salihagic l Jul 4, 2024 l Data Protection, Big Data Analytics

Privacy-Enhanced Data Sharing: How to Drive Business Growth by Protecting Your Data

If data is the new oil, then organizations will get little benefit from hoarding it. They need to share it between individuals, departments, organizations and/or systems to improve decision making and drive growth. But there are risks. To avoid major financial, reputational and legal repercussions, these same enterprises need to build “secure pipelines” down which that data can travel.

Why data sharing matters

We live in a hyper-connected world. Data supply chains extend across the planet, from the smallest business to the largest enterprise. In this context, there are few organizations that would actively seek to restrict the flow of all of their data. It can help improve the customer experience, reduce fraud, streamline operations and much more. Consider a healthcare provider sharing patient records to another provider to ensure they get the best treatment. Or a bank sharing customer info with a credit agency to speed up approvals for a new line of credit.

Non-profit the Open Data Institute lists several reasons businesses might want to boost data sharing practices, including:

Innovation: Collaborating with third parties to explore new technologies and glean new insights.

Market reach: Sharing information on the organization’s services with prospective customers, to build awareness and grow the business.

Benchmarking: Sharing data with trusted intermediaries to compare key metrics with peers in the same industry.

Regulatory mandates: Sometimes regulations require data sharing, such as open banking rules.

Supply chain optimization: Improving data access for partners and suppliers can boost efficiencies, reduce operational costs, enhance sustainability initiatives and build trust.

Tackling industry-wide challenges: Sometimes there’s a shared interest in exchanging data, such as cyber-threat intelligence, which can benefit all parties.

Data-driven insight: The benefits of AI-powered analytics are now well understood. Unlocking data from organizational silos and ensuring it is shared with these systems can open the door to tremendous insight, with which better business decisions can be made.

The risks of data sharing

However, enterprise data can also be incredibly valuable to rival companies, nation states and cybercriminals. That means organizations need to be aware of the risks posed by data sharing. They include:

Data breaches: It could take just one ill-advised click on a phishing link by a single employee to give hackers access to critical enterprise data stores. Often it is a supply chain partner that is targeted, as they may have fewer safeguards in place. In May 2024, the UK’s Ministry of Defence revealed a massive state-backed cyber-espionage operation targeting a payroll contractor. It led to the compromise of details on over 270,000 service personnel.

Data leaks: Sometimes data is accidentally exposed to the public, such as a misconfigured cloud infrastructure account, or a misdirected email.

Compliance risk: In order to improve standards of data handling, security and privacy, regulations like the GDPR in Europe, California’s CCPA, and industry rules like HIPAA in the US enforce strict requirements around secure data sharing. Major financial penalties and reputational damage could result from non-compliance.

Best practices for privacy-enhanced data sharing

To mitigate the above risks whilst supporting important data sharing initiatives, consider the following steps:

Data discovery and classification: The first step is understanding what the organization holds and where it is stored. This will need to be a continuous process, given the dynamism of modern data environments.

Apply strong protection: Use privacy-enhancing tools such as encryption, tokenization and differential privacy to ensure that, even if it is accessed by a malicious third party, data remains secure. Solutions which enable the data to be used in analytics and other scenarios whilst remaining secure (ie tokenization) are preferred.

Access controls: Ensure access to data is managed with a “least privilege” policy, according to role, supported by multi-factor authentication.

Continuous monitoring and auditing: Deploy detailed logging and alerts to quickly detect any digital break-ins and contain the threat before it can impact the organization.

Data governance: Build a comprehensive program to ensure regulatory compliance and continuous staff awareness training.

Comforte’s Data Security Platform offers AI-powered data discovery and classification, and tokenization as a protection method. This substitutes real data with a non-sensitive “token” – enabling organizations to benefit from AI analytics without exposing themselves to extra risk.

With comforte in place, organizations can:

  • Accelerate AI analytics projects for improved decision making and innovation
  • Secure data sharing processes and collaboration for multiple business benefits
  • Mitigate significant financial and reputational risk stemming from data theft
  • Minimize the management/cost burden of data protection regulatory compliance and eliminate the risk of enforcement action/non-compliance
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