With organizations persistently moving their workloads to the cloud for analytics, lending a thought to cloud security becomes imperative. In a 2021 survey, about 64% of respondents named data loss/leakage as their foremost cloud security worry. Data privacy and confidentiality concerns, as well as inadvertent exposure of credentials, ranked second and third, respectively.
The amount of data that can be stored, processed, and analyzed on the cloud is enormous. But this also paves the way for a much broader attack surface. A study carried out by the cloud security company Ermetic revealed that about 80% of businesses experienced at least one data breach incident between 2019 and 2020. About 43% admitted to more than ten breaches within the same period.
So, it’s natural to question if data analytics on the cloud is a safe bet and what measures can be taken to ensure security concerns don’t escalate.
Security Concerns with Data Analytics on the Cloud
Insufficient Due Diligence
Many enterprises are looking to move analytics to the cloud, and understandably so. However, without sufficient due diligence, this move can prove to be detrimental on the security front. For example, enterprises:
Can find the security infrastructure of the cloud extremely different from the on-premise setups. Inadequate training on operating the same can pave the way for misconfigurations in security settings.
May not be able to completely take care of their share of security responsibility. Cloud platforms operate on a shared responsibility model. While they will secure the infrastructure, ensuring that the customer data is safeguarded is something that enterprises need to consider.
The proliferation of data breaches impacting cloud services is escalating. IBM’s Cost of a Data Breach Report 2023 ascertains that 82% of breaches are concerned with data stored in the cloud.
When companies move analytics to the cloud, it’s usual to have workloads spread across multiple cloud environments. While it benefits from the parallelism perspective, the distributed nature of analytics workloads calls for a more robust security framework in place. For example, enterprises need to ensure that their least privilege access mechanisms are well implemented across the distributed environment.
Compliance with Data Protection Regulations
As businesses leverage the capabilities of data analytics on the cloud, they must also address the critical aspect of compliance with data protection regulations. For example, GDPR in Europe and CCPA in California stand as powerful benchmarks for data privacy and security. Non-compliance can result in fines. GDPR administrative fines can reach up to €20 million or 4% of the annual turnover.
These types of regulations can present a big challenge for analytics initiatives on the cloud. The CCPA, for instance, requires consent letters to be provided to all individuals whose data is processed. GDPR mandates similar communication and transparency for all organizations.
Some cloud service vulnerabilities can stem from misconfigured security settings, unpatched software, insider threats, or shared responsibility misunderstandings. These risks are amplified by the presence of highly sensitive data.
Addressing Security Concerns (Tools & Techniques)
Identity and Access Management (IAM) Solutions
In 2020, McAfee reported a 630% rise in attacks on cloud accounts. Favorably, identity and access management (IAM) solutions can safeguard cloud workloads by ensuring that only authorized individuals can access sensitive data and systems. These solutions provide organizations with the requisite administrative tools to effectively manage user access privileges to applications. They also possess reporting capabilities to better accommodate compliance audits as needed.
Threat Detection and Monitoring Tools
These sophisticated tools continually scan networks, systems, and applications for anomalies and potential security breaches. By studying patterns and behaviors, they can quickly detect unauthorized access, malware, or strange activities, enabling prompt action to repel dangers. They can correlate all activities across an enterprise’s analytics ecosystem to ensure that security policies are consistently enforced.
Data analytics on the cloud offers unprecedented flexibility and scalability, but it also introduces unique security challenges. To navigate this environment effectively, organizations need a set of best practices that not only protect their data but also foster a secure and compliant environment in which insights can flourish. Here’s a rundown of such best practices:
Implement a strong access control policy
Regularly update security patches and configurations
Conduct employee security training
Establish an incident response plan
Collaborate with cloud service providers for security
Regularly conduct vulnerability assessments and penetration testing
Implement data encryption to protect the data in transit as well as at rest
Monitor the access logs to ensure that nobody who’s unauthorized has access to sensitive data
Enable data lifecycle management and control
Analytics on the cloud can immensely benefit businesses as they gain the power to process and manage a massive amount of data. However, it is important to implement the right security strategies to ensure that the sensitive data remains secure and compliant.
Enterprises need a robust security strategy that encompasses the above-mentioned tools and best practices and works to maximize the value of their analytics programs while protecting their data against potential threats. This is where an expert technology partner like Recode can help. We partner with organizations to help them move analytics to the cloud and actively work towards establishing a secure, compliant, and scalable analytics ecosystem. Get in touch today for more information.