5 best practices for securing your big data supply chain

Big data supply chain

In today’s business world, data is more valuable than ever before. But it is also more vulnerable than ever before.

Serving as a digital accelerant, COVID-19 propelled business transformation forward, prompted the adoption of new technologies and triggered the decentralization of workforces. This led to an explosion of global data creation, which is set to reach an all-time high of 74 zettabytes in 2021 and predicted to grow to more than 180 zettabytes by 2025. In correlation, however, are the growing threats and increased attacks on data assets. The rapid pace of change has highlighted the weaknesses of existing systems and frameworks in protecting personal information within the paradigms of a new data-led economy and placed the spotlight on the urgent need for modernization.

This year alone, 281.5 million people have been affected by some sort of data breach. Couple this with a quick scan of the insights in IBM Security’s Cost of a Data Breach Report 2021, and it is clear to see why securing data pipelines and protecting personal data is now business critical:

  • The most common type of record lost in a data breach is personal identifiable information (PII) belonging to customers
  • The average total cost of data breach now sits at $4.24 million, with healthcare breaches costing an average of $9.23 million
  • The presence of a high level of compliance failures is associated with breach costs; in fact, costs are $2.30 million higher in these instances
  • Out of the 25 listed cost factors that either amplify or mitigate data breach costs, compliance failures is top for amplifying the cost
  • The 3 most costly industries for data breaches are healthcare, finance and pharmaceutical

It’s little surprise, therefore, that securing data pipelines and protecting personal data is a top priority for those wanting to ensure business success. Here are 5 best practices for securing your big data supply chain:

1. Prioritize data-centric security

Conventional security tools and frameworks are struggling to keep up with the unforeseen pace of digital adoption; outdated approaches to security now need modernization to offer the flexibility, agility and resiliency required in an insights-driven economy. With today’s organizations understanding that their data is their lifeblood, security has fast become a business decision rather than a technical one. As such, priorities have shifted from protecting the perimeters of data (the hardware and network infrastructures) to protecting and securing the data itself. Not only are security budgets increasing at pace, but there are significant shifts in prioritizing that budget for information(data)-centric security strategies, encouraged by privacy concerns. And in order to build future-ready, data-centric security models, privacy mechanisms and technologies must be leveraged so that robust systems and infrastructures can be designed to ensure that sensitive data is protected throughout its lifecycle.

2. Bake privacy-by-design into operations and system infrastructures

In today’s world, an accidental data leak can be equally as harmful as a targeted data breach, which is why it is critically important that organizations keep the principles of privacy-by-design front and center when evaluating how best to prepare their business for success a data-led economy. According to insights compiled by Finbold, the EU GDPR fines for 2021 Q3 hit €984.47 million, which is almost twenty times higher than cumulative fines imposed during Q1 and Q2. To put this into perspective, the Q3 2021 GDPR fines are three times higher than the total fines issued across 2020 (€306.3 million).  With these figures only set to further rise as new privacy regulations are introduced and consumers continue to call for change, those who implement a pro-active and preventative approach to privacy across their business and IT systems will be better positioned than those who treat privacy as a bolt-on to their existing architecture.

3. Develop agile infrastructures that can rapidly adapt to change

By 2023, Gartner predicts that 65% of the world’s population will have its personal data covered under some form of modern privacy regulation, which will make operations all the more complex for data-driven businesses looking to use and share data across teams and geographies. When you couple this with the exponential growth of data that businesses are now acquiring and the rapid migration to the cloud, it is easy to see how organizations could quickly fall into big privacy problems unless the appropriate measures are taken. To protect and secure their data pipelines, businesses should be looking towards the best practice principles and design patterns of privacy engineering that can deliver scalable and cost-effective privacy-protected pipelines. By harnessing privacy-enhancing technologies that have been engineered to provide the automation, multi-cloud integration and ease of implementation that businesses now need, today’s big data challenges can be overcome and tomorrow’s emerging privacy requirements can be efficiently managed.

4. Automate and centralize privacy risk assessments & data access

Test, assess and evaluate your privacy risks – regularly. Risk quantification is a critical step in the process of securing data pipelines and shutting down vulnerabilities; it helps organizations to identify and quantitatively measure privacy risks so that informed decisions can be made about data access and data use. Under the GDPR, businesses are required to adopt measures that enable them to monitor the movement of personal data and track the flow of that data across their business ecosystem. By leveraging software that can automate privacy risk assessment, organizations can centralize and standardize data privacy management at speed. When you have the ability to conduct statistical risk analysis on datasets of any size, the regulatory guesswork, manual practices and subjectivity can be removed from the decision-making process. It is one of the fastest ways to operationalize privacy-compliant dataflows and develop an auditable trail of compliance.

5. Leverage next-gen privacy technologies that are purpose built to protect data pipelines

Navigating the complexities of a highly regulated, data-led economy while simultaneously preventing malicious actors from taking advantage of cracks in security can seem like a monumental ask for companies – no matter where they sit on the data maturity curve. However, ready-built solutions, such as Trūata Calibrate, are demonstrating the power of privacy-enhancing technologies that can seamlessly measure risks and mitigate risks so that businesses can ensure their data is protected – wherever it moves and whatever its business use. The best defense is a good offense, and in implementing pro-active strategies to mitigate risks, businesses are able to protect privacy and bolster security frameworks while simultaneously maximizing data utility for data-driven innovation.

When organizations understand the financial and reputational impact of failing to secure data and protect privacy, they can start to look at the cost-benefit and conduct an ROI analysis on investments in privacy-friendly technologies. Why not get in touch today or book a 15-minute demo to see how Trūata Calibrate can work for your business?