Securing the data while at rest and during migration should be a top priority of cloud data lake security protocols. CISOs should consider evaluating both existing and new security threats to develop robust data lake security practices.
Enterprises can develop robust data lakes to act as a repository of data from multiple sources and process them whenever required. Developing a data Lakehouse is not as challenging as protecting it from all the data breach risks.
Data lakes enable enterprises to store data in raw format for future analysis, which is easily accessible and usually unmanaged. Enterprises can store raw data in the lake to index, aggregate, and analyze whenever required to get actionable insights. A few organizations leverage data lakes as a data archive, while a few leverage it as a data repository of multiple sources, and for a few, it acts as a data mine for data scientists to dig in.
CISOs should consider security concerns as a top priority while managing, processing, and storing data, especially from a compliance and regulatory perspective. Here are a few ways to develop data breach resilient lakes:
Evaluate data lake through a cybercriminal perspective
Earlier businesses integrated data lakes into the IT infrastructure to gather, process, and manage temporary data. However, for many organizations, it did not remain a temporary invocation because the intent, purpose, and permanent data structures evolved as well. Data lakes are the easiest attack surface areas of cybercriminals because they can easily gain access to a massive repository of unprotected data. Once the malicious actors infiltrate the lake, they can easily gain access to the database it pulls from because enterprises tend to overlook the security measures. A few enterprises presume the data lake is protected by a perimeter security wall, which cybercriminals can exploit to infiltrate the network. CISOs should consider adding an additional layer of advanced perimeter security walls to minimize the threat of data breaches. Evaluating data lake through a cybercriminal perspective will help to spot the potential patterns and weaknesses that can be used as an attack vector.
Also Read: Why Businesses Need to Take Behavioral Risk Analysis Seriously
Design and implement a robust data lake management strategy
Enterprises need to get a better understanding of data usage, compliance requirements, security need, and access control derived from it to develop a secure data lake. CISOs should consider developing multiple data lakes that help to serve different purposes instead of keeping the data in one single cluster. For example, enterprises can integrate a different data lake for real-time production flows and develop others for other data requirements. With the availability of cost-effective and secure cloud storage solutions, many enterprises are migrating their data lake to the cloud. Managing authorized access and data governance efficiently is one of the challenges of data lake management. CISOs should consider exploring, evaluating, and implementing robust end-to-end security management solutions to avoid data breach incidents.
Adopting data lineage strategies will assist businesses in understanding the data origin and how it is stored, processed, and used throughout the entire data flow to identify security flaws in the entire process.
Evaluating and implementing robust cybersecurity tools
CISOs should consider integrating security controls such as data encryption, user authentication, and role-based access controls and security to enhance the security of data lakes. A few data lake vendors provide multiple security tools inbuilt with the solution to offer a comprehensive platform. Businesses find it challenging to integrate the same data lake protect features developed on open source components. However, businesses can select robust standalone products that seamlessly integrate with the enterprise’s security tech stack to protect the lakes.
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