Mastering ETL Security: Essential Challenges and Expert Solutions

According to Expert Market Research, the global big data & Analytics market is expected to grow at a CAGR of 10%. This report also forecasts that the global investment in big data and analytics will reach $450 Billion by 2026. Hence, considering the massive scope of the analytics and the amount of data you deal with, the need to ensure the data security of your data analytics stack is a necessity. 

Moving sensitive information such as login credentials, financial details, PHI (Protected Health Information), etc., requires close monitoring of all the techniques implemented for protecting the data. 

There is an exponential improvement in the security standards and features provided by the tech giants out there. Why don’t you leverage those to secure your business? Let this blog be the first step in that direction. 

Critical Factors to Ensure ETL Security While Building Your Data Analytics Stack

A safe approach to handling and storing data is required for this stack of layered technologies. Building a Data Analytics Stack that is both productive and secure necessitates a careful series of balancing acts.

Let’s explore some of the critical & extensively used Data Security techniques to build a secure Data Analytics Stack.

1) Safeguard Distributed Programming Frameworks 

Distributed programming frameworks contain software tools such as Apache Spark. These help to process and analyze large volumes of data simultaneously using clusters of machines that can auto-adjust based on the data requirement. 

You can employ authentication mechanisms such as Kerberos to protect these frameworks. Kerberos is a protocol for authenticating service requests over a network. In addition to this, ensure that all specified security regulations & policies are followed while building pipelines. You should also make sure that any sensitive data is masked using your internal mechanisms.

2) Data Encryption & Cryptography 

The Encryption technologies protect your data both in transit & at rest. The Encryption techniques/tools you develop or deploy should work with a wide range of data formats. These techniques should also be compatible with a variety of Analytics tools & their data outputs. 

Although Non-Relational Databases (NoSQL) are widely used, they are subject to Injection attacks. You can use methods such as Advanced Encryption Standard (AES), RSA algorithm, or Safe Hash Algorithm 2 to encrypt or hash passwords. This will help you ensure end-to-end encryption. Moreover, enterprises should also use the Searchable Symmetric Encryption (SSE) protocol to execute Boolean searches on encrypted data by establishing a way to scan & filter encrypted data. 

3) Endpoint Filtering & Validation

Endpoint Security in general implies the practice of safeguarding the endpoints or entry points of end-user devices like desktops from being attacked by fraudulent campaigns. It is critical for your data stack.

You can implement it by using trustworthy certificates. This involves resource testing & connecting only trusted devices to your network via a Mobile Device Management (MDM) solution. You can then filter dangerous inputs using Statistical Similarity Detection & Outlier Detection techniques. This will guard you against Sybil (one entity posing as numerous identities) & ID-spoofing attacks. 

4) Centralized Key Management 

For many years, Centralized Key Management has been considered the best strategy to ensure ETL Security. It holds true in data situations as well, particularly those with a vast geographic dispersion. Policy-driven Automation, Logging, On-Demand Key Supply, and abstracting Key Management are all good strategies for empowering Data Security. 

Further, organizations can employ analytics to keep track of real-time data. They can use mechanisms such as Kerberos, Safe Shell & Internet Protocol Protection to keep data safe. It’s then easy to keep an eye on logs & set up front-end security measures like routers & server-level firewalls. The insights generated can be used to implement security controls at the Cloud, Network, and application levels. 

5) Granular Access Control 

Although User Access Control is the most fundamental network security measure, most firms often employ it to a limited extent. This is often due to the high operational cost. It is risky enough at the network level, but it may be devastating for the Big Data platform. 

User Access Control follows a policy-based approach for Access Control. It automates the access as per the user & role-based parameters. 

This helps ensure effective access management for users. In addition, you should always maintain a good labeling strategy to complement this. You can use Single Sign-On (SSO), manage secrecy needs & assure proper implementation. SSO brings in user-friendly characteristics. Users need to input credentials just once to access a variety of services & apps. This is highly appreciated by IT leaders. 

6) Granular Auditing 

Granular Auditing is critical in data security, especially after a system attack. To minimize incident response time, organizations should build a single Audit view following an attack. It should include a comprehensive Audit trail that makes data easy to access. Further, Audit Record Integrity & Security are also critical. Data from audits should be kept separate from other data. It should be protected with Granular User Access rules & Periodic Reporting. 

Granular Access Audits can aid in the detection of an attack & disclose why it was not detected adequately in the first place. To achieve this, you need to instill appropriate approaches & technologies in your Data Analytics Stack. For example, Application Logging, Security Information & Event Management (SIEM), Forensics tools, & more. You can also activate System Logging Protocol (Syslog) on routers to guarantee a successful Audit.

Data Security Challenges and Solutions

Let’s consider different situations and understand how to ensure data security in each stage.

1. Retaining Data in the Data Pipeline

You can make sure that you retains customer data temporarily only while moving to the destination under. In case of Hevo, we store it only during the following scenarios:

• In a staging area, before uploading to the Destination -: The data is stored in an encrypted (coded) format in a temporary space (referred to as staging area in ETL parlance). Once this data is uploaded to the Destination, it is permanently deleted within 24 hours.
• Failed Events in a Pipeline-: The data is stored encrypted and is retained for 30 days until it is either fed back to the data warehouse or dropped from the pipelines. Failed Events in a Pipeline are highlighted in the Pipeline Overview tab (as shown in the image below):

• Samples for Transformations-: Hevo retains a few sample events for users to test the transformation code in Hevo’s Transformation UI. All these events are stored in an encrypted format and cleared at regular intervals as new sample events arrive.  Refer to the image below to see how users test their Transformation code.

2. Compliance with Security Standards

Make sure that you ensure the data security and privacy for all the data processed by your systems and applications. We comply with all the major security protocols, including HIPAA, SOC II, GDPR and CCPA.

3. Ensuring Data Sovereignty 

To address concerns related to data sovereignty, its good to have application instances running in multiple geographies. This way, you can create accounts in the US, India, Asia, EU regions. For example, when you don’t want your data to go out of Europe, Hevo will spin an account for you in the EU region. That way, the data does not leave the EU. Hevo is hosted in different regions in AWS, which includes the US, EU, IND, and Asia. This applies to all other regions as well.

4. Different Encryption and Hashing Mechanisms for Data In-Flight

Your data pipelines should encrypt the data and use techniques like hashing for protecting data in pipelines.

• Data in transit: The data in transit is SSL encrypted.
• Establishing connection with sources and destinations: For database sources and few destinations, Hevo can establish the connection via both SSH and SSL, which ensures an encrypted and secured connection. The steps for the same are listed in this link Connecting Through SSH.

Hevo supports only hashing when users are performing any transformations before loading data into a warehouse. We support this as this is a one-way function that scrambles plain text to produce a unique message digest, whereas encryption is a two-way function that can be decrypted with a proper key. 

5. Disaster Recovery

If there are any outages, you should set up a mechanism to not on lose your account data, which includes pipelines set up and models built. Hevo gives a 99.9% uptime. Since we have redundant storage, you won’t lose an account or any information related to Pipelines or models. 

6. Data Masking

Masking data is a good way to securely move data. You can do this using data transformations. With the Python console, you can write the code for masking field data. For example, when you test the Transformation, the value of the email field will be masked, as shown below.

Consequences of a Data Breach in ETL 

An exposure of sensitive data to an unauthorized party such as hackers, rogue employees, or any person who isn’t authorized to access the data will result in a data breach. 

Dealing with a data breach is expensive. IBM estimates the global average cost of a data breach in 2023 was USD 4.45 million, which is a 15% increase over 3 years.  This figure includes immediate restorative action, as well as loss of business and reputational damage that arises from losing customer data. Regulatory fines can push this figure even higher. 

Data breaches can also cause real human suffering. The identity theft your customers experience will damage your company’s reputation more than churn of customers. When a customer provides you with their private data, they’re trusting you to keep them safe.

Any stage of the ETL process is prone to data breach. This includes when data is extracted from your source systems, when data is transformed or cleaned, or when data is loaded into the data warehouse. The reasons could be inadequate access control, human error, or malicious attacks. This will in turn have severe impacts on the compliance of the organization, as well as legal and financial liabilities. The penalties associated with violating the compliances are huge. Therefore, have a good ETL security strategy in place and continuously monitor that. 

Conclusion

The global Big Data and Analytics market is growing exponentially. This emphasizes the critical need for robust data security in the realm of data analytics.

We discussed the key factors for ensuring ETL security in a Data Analytics Stack, particularly focusing on ETL (Extract, Transform, Load) processes. These include safeguarding distributed programming frameworks, data encryption, endpoint security, centralized key management, granular access control, and granular auditing.

Temporary data retention, compliance with security standards (e.g., HIPAA, SOC II, GDPR), data sovereignty, encryption, and disaster recovery mechansism are also equally important.

Considering the consequences of data breaches in ETL processes, including financial costs and damage to reputation, you need to implement and continuously monitor robust security measures at all stages of the ETL process to mitigate these risks effectively.

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