Identifying security challenges against data lakes

  • These online spaces are popular among businesses for internal data processing and storage.
  • Although data lakes provide a simplified format for organizations to store their huge data but this also opens doors to cyber threats.

Driving digital information each day became a challenge with a rise in the number of online users, devices, and applications. In order to meet this challenge, data warehouse and data lakes became relevant.

These online spaces are popular among businesses for internal data processing and storage.

Data warehouse and Data lake

  • A data warehouse is a traditional approach used by service providers to store data. It consists of a single repository that can be used to analyze data, create reports and consolidate information.
  • However, with zettabytes of data in cyberspace, data going into a warehouse needs to be pre-processed. Thus, Data lakes were proposed to solve the problem.
  • Unlike warehouses, data lakes can store raw data of any type. The technique has been opted by many organizations trying to drive innovation and new services for users.
  • Data lake architecture is comprised of three components: data ingestion, data storage, and data analytics.

Increasing threats to data lakes

  • Although data lakes provide a simplified format for organizations to store their huge data but this also opens doors to cyber threats.
  • These valuable repositories remain exposed to an increasing amount of cyberattacks and data breaches. For instance, compromised data lakes have huge implications for healthcare, because any deviation in data can lead to a wrong diagnosis or even casualties.
  • Similarly, the government, finance, defense, and education sectors can also be vulnerable to data lake attacks.

Types of threats

Malware obfuscation: Due to advances in malicious software, it can be easy for hackers to hide dangerous malware within a harmless-looking file.

False data injection attacks: This type of attack happens when a cybercriminal exploits freely available tools to compromise a system connected to the internet. The compromised system is injected with false data in order to gain unauthorized access to the data lake and further manipulate the stored data.

Mining sensitive information: Data lakes are a rich source of sensitive data. Lack of proper security controls can allow cybercriminals to corrupt IT specialists’ or organizations’ businesses by mining unprotected data. Later, they can sell it on underground market places or rival companies for their monetary benefits.

Bottomline

Given the huge trove of data stored in data lakes, the consequences of cyberattacks are far from trivial. While the amount of data generated in today’s world is inevitable, it is crucial that data lake architecture should try harder to ensure that these data repositories are correctly looked after.