One buzzword that is common these days in technology discussions, business strategy sessions, and future projections on industry growth, stocks, and innovations is “DATA.” With close to half a billion terabytes of data created and a significant portion transacted every day, the need for effective ways to collect, store, and analyze data is immense. A data lake has emerged as one of the efficient and effective mechanisms/technologies to enable this. With this gigantic growth of data comes an equal, if not greater, necessity to secure it from breaches and cybersecurity threats. This article outlines potential security threats and risks around data lakes and some effective ways to tackle them. Let’s dive in!
First, let us understand why a data lake has become an important aspect of today’s modern data architecture.
Structured data has long been managed and used effectively for day-to-day operations and accurate decision-making. It is the unstructured data that has relatively recently made its presence felt in data analytics. This has also made data analytics more complicated, which is why a repository such as a data lake becomes critical. A data lake serves as a centralized repository for both structured and unstructured data, storing them as-is in open-source file formats to enable direct analytics. Using big data via tools such as data lakes has propelled technological advancements, allowing organizations to uncover insights about their customers’ needs and fulfill them while growing their revenues.
However, this advancement in data analytics and science does not come without challenges. It complicates information security and data security in multiple ways. The information below focuses on these complications related to data lake security and potential remediations.
Many companies nowadays have shifted their data lakes to cloud platforms as they have discovered the core advantages of cloud computing and storage. Lower infrastructure and maintenance costs, great customizability, and accessibility have allowed them to manage and store huge volumes of data effectively while saving on data infrastructure costs. But as companies are allured by the promise of cloud technology, many still don’t understand the vulnerabilities and challenges associated with the migration and integration process—especially the security risks it entails. All this means more data vulnerabilities, which creates a need for robust data security policies. From data loss to defending against cyberattacks during migration and operation, inherent security vulnerabilities should be understood.
Like many standard information security programs, data lake security comprises a set of processes and procedures to ensure data protection from cyberattacks. Depending on the industry or organization deploying a data lake, enormous and sensitive information such as credit card numbers, medical test results, customer data, and more may be at stake, potentially creating many cybersecurity risks.
Given below are some of the best practices to remediate key risks emerging from cyberattacks:
- Data Governance and Compliance
- Data administration program structure
- Data access and control processes
- Data protection controls
Data Governance and Compliance
Data governance involves managing an organization’s data assets to ensure that data is accurate, reliable, and secure. This is crucial for making informed decisions, complying with regulatory requirements, and driving business success. By establishing a robust and comprehensive data governance framework, an organization can effectively and efficiently manage its data, protect sensitive information, and meet regulatory requirements, thereby enhancing its overall performance and reputation.
Policies, procedures, and standards are three critical components of any data governance framework. Data governance serves as a guiding document for organizations to navigate the abundance of data. In the current digital era, data is both a valuable asset and a potential liability. Therefore, effective data governance is essential for managing, utilizing, and protecting data precisely and purposefully. It involves maintaining data authenticity and complying with regulations. Data governance fosters trust, enables informed decision-making, and ultimately guides organizations towards success. Amid all this, the increasing interconnectedness of operational and information technology systems presents key security risks that must be addressed.
Data Administration
Data administration focuses on managing data from a conceptual, database-independent perspective. It coordinates strategies for information and metadata management by overseeing requirements gathering and modeling functions. Data modeling supports individual application development with tools, methodologies, naming standards, and internal modeling consulting. Additionally, it provides upward integration and bridges disparate application and software package models into the overall data architecture. This overall data architecture, known as the enterprise data model, is critical for an organization’s ability to assess business risk and understand the impact of business changes. Each data tool may require a unique approach to administration. Data administration allows the organization to maintain consistent security standards throughout the data lake. Another important aspect of data lake administration is auditing data lake usage. This helps in understanding the importance of the data asset and defining best practices for securing it.
Data Access and Control
Generally, an organization can define data access and controls through authentication and authorization.
- Authentication: Verifies the user identity. Doing it through a multifactor authentication mechanism is a norm these days.
- Authorization: Determines each users level of access to the data based on specified policies and also the actions the user can take on it. Security principal-based authorization, where the system evaluates permissions based on a policy designed order is one of the effective ways of authorization.
Authentication and authorization need to be properly implemented across the organization to ensure effective and adequate data access controls for the data lake. Additionally, no single approach to managing data lake access suits everyone. Different organizations require varying levels of governance and control over their data in the data lake. Organizations must choose an approach that meets their required level of governance without causing undue delays or friction in gaining access to the data.
Data Protection
Encryption of data at rest is a requirement of most information security standards. Traditionally, this has been implemented through third-party database encryption products. However, enterprises using cloud data lake vendors can often take advantage of encryption at rest as a bundled free service.
For data lake security, while encryption is desired and often required, it is not a complete solution, especially for analytics and machine learning applications. Security must be a primary focus of data operations, and this applies to data lakes as well. This can be achieved through a simple ‘always on’ security posture that makes security a default option, integrating a prescriptive approach to securing sensitive assets. Following industry best practices, encrypting data in transit and at rest is essential. With this encryption comes the necessity to protect and secure the encryption keys. Both on-premises and native cloud operations need to provide secure storage and management of encryption keys for both internal and third-party applications.
With encryption, there are two main challenges:
- The changed data field format may cause many applications to break.
- Encryption is only as secure as the key used to encrypt and decrypt, which presents a single point of failure.
Unlike encryption, tokenization keeps the data format intact, so even if a hacker obtains the key, they still do not have access to the data.
Best practices include using the built-in encryption provided by the cloud provider and adding additional security from a third party. This vendor should decrypt the data, tokenize it, and provide custom views depending on the user’s access rights, all done dynamically at runtime.
Importance of Metadata
A significant aspect of data lake security and general data security can be achieved through data governance, with metadata being an integral part of this process. Let’s delve into metadata and its role in enabling effective data governance.
Metadata simply means data about data. For example, it helps in understanding the characteristics of the data in consideration. The format of data, length of data fields, number of data fields, and type of data are all part of metadata. Metadata provides information about data assets across various dimensions, including technical information (such as data structure, data schema, technical data field characteristics, data transfer protocols) and business information (such as data owner, data processor, types of data access roles).
When used strategically, metadata can help in understanding key attributes of data such as:
- Data ownership
- Data accuracy
- Data classification
- Effective approaches to data governance
- Data sources and destinations
- Reliability of data
Managing metadata is critical for effective data governance. This management is facilitated through various metadata management processes, including access controls, data control processes, data schema management, data field edit management, data classification, data quality management, data inflow and outflow, data search features, and required data compliance controls.
Effective metadata management leads to a solid data governance structure, which ultimately results in high-quality data, data accuracy, data integrity, and intelligent usability. These elements are essential for various users to utilize data assets to their full potential.
Conclusion
Due to technical advancements and various innovative data management techniques, data lake security has become a dynamic and challenging topic. To ensure appropriate controls around data lake security, organizations must adopt a well-balanced combination of processes, tools, integrations, and skill sets. This comprehensive approach can lead to a better security posture and a reduction in data security risks and vulnerabilities for the organization as a whole.
References
- EIM Institute. (n.d.). Information resource management: Data administration versus database administration. https://www.eiminstitute.org/library/white-papers-articles/information-resource-management-data-administration-versus-database-administration/view
- Snowflake. (n.d.). Data lake security. https://www.snowflake.com/guides/data-lake-security/
- lakeFS. (2022, May 11). Metadata management in data lakes: Challenges. https://lakefs.io/blog/metadata-management-data-lakes-challenges/
- Exploding Topics. (n.d.). Exploding Topics blog. https://explodingtopics.com/blog/
