Data Engineering For Cybersecurity: How to Overcome Main Challenges By Following Best Practices

While all industries need to prepare for growth, data engineering for cybersecurity needs to set bullet-proof data security measures with real-time threat detection to safeguard data.

In this article, we explore the key data engineering requirements and challenges of cybersecurity firms. Plus, best practices to follow for data protection. 

Table of contents

• Data engineering in cybersecurity companies: What are key requirements?

• Key data engineering challenges in cybersecurity firms

• How NaNLABS helps cybersecurity clients improve their data engineering practices

• Data engineering for cybersecurity: Overcoming unique industry challenges

• Frequently asked questions about data engineering for cybersecurity

Build scalable, secure, and compliant cybersecurity applications with NaNLABS. We’ll help you overcome your unique data engineering challenges and design secure data architecture. 
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Data engineering in cybersecurity companies: What are key requirements?

As mentioned, the primary focus of cybersecurity companies is to prioritize data protection, secure data storage, and guarantee the integrity of data pipelines. “This involves implementing and maintaining strong encryption methods, data masking, and secure data transmission practices,” says Gustavo Alberola, Software Developer Advocate at NaNLABS.

A data engineering requirement in cybersecurity companies is data compliance. This means you need to adhere to local regulations, such as GDPR, HIPAA, or specific government security protocols. 

Another priority is to establish and follow an incident response plan in case of breaches or anomalies. “Ensure that the data engineering team is capable of quickly responding to data breaches or security incidents, with robust logging, monitoring, and forensics capabilities built into the data pipelines,” adds Gustavo.

Companies with a focus on cybersecurity also need to build on data engineering capabilities that support predictive analytics to anticipate and mitigate security threats. For example, processing large volumes of data to identify patterns and anomalies that could indicate potential security breaches. 

Lastly, you also need to stay ahead of the curve and adopt new security technologies as the industry evolves. For instance, integrating AI-driven threat detection into your processes and staying up to date on the latest developments in quantum encryption for further data protection.

Key data engineering challenges in cybersecurity firms

All businesses need data flow orchestration policies that define data ingestion or collection, transformation, and serving. And, like any other business, cybersecurity companies also need to build and follow ETL data engineering processes that are scalable, support real-time data processing, and collect client analytics. 

However, the main challenges of data engineering in cybersecurity reflect the pressing need to protect your and your customers’ data—and keep high-security standards. In-depth, these challenges include: 

• Assessing the integrity and authenticity of data sources

• Handling a variety of data formats and volumes without compromising security

• Extracting data by using encryption, secure protocols, and access controls

• Defining data latency and velocity requirements without putting information at risk

• Designing database architectures that can handle growing volumes of data and the increasing complexity of threats without compromising performance or security

• Ensuring data quality, accuracy, and reliability to improve threat detection and response

• Avoiding fines due to not following data regulations 

• Identifying unusual behavioral patterns in data or anomalies

• Reducing management by allocating enough computational resources and improving data proceeding pipelines

• Protecting Personal Identifiable Information (PII) and confidential data

• Safeguarding the company against Internet of Things (IoT) potential threats

• Making plans and processes for disaster recovery

Now, let’s take a look into how you can overcome these challenges and build more secure and compliant platforms. 

Data engineering best practices for cybersecurity

Proper data handling protects your business from potential breaches, ensures all information is accessed by the right people, and is processed efficiently according to business needs. 

Here are some data engineering best practices we recommend you follow when handling data in cybersecurity firms:

• Ensure data integrity and conduct regular validation to verify the accuracy of your information. Use checksums, cryptographic hashes, and secure logging. Regularly validate data inputs and outputs to detect any unauthorized alterations or corruption, which could indicate a security breach or malfunction.
  

• Minimize data collection and retention to avoid storing unnecessary sensitive information. You can also implement data retention policies that specify how long you can keep data and when it should be anonymized.
  

• Encrypt data at rest and in transit to add an extra layer of protection in case of breaches. We recommend you try AES-256 and robust key management practices, such as hardware security models (HSMs), regular key rotation, and secure storage of encryption keys. 
  

• Set up an incident response plan to train users on what to do in case of data breaches. Conduct regular drills and simulations to test your plan and assess its correctness.
  

• Perform regular audits to assess the efficiency of your data security controls. By continuously monitoring data access, you can identify potential vulnerabilities and anomalies. You can do this through a security information and event management (SIEM) tool to identify and react to threats in real time.
  

• Stay compliant with local regulations regarding data to avoid fines. Regulations include, but aren’t restricted to GDPR, HIPAA, or CCPA. Establish clear legal agreements and terms of service with clients and partners regarding data usage, ensuring that all parties understand and adhere to data protection requirements.
  

• Determine how each class should be handled, stored, and transmitted to adhere to privacy policies. Define what makes data public, internal, confidential, or highly confidential. Share this information with your employees and contractors to ensure compliance.
  

• Set access control and identity management processes to guarantee only a handful of people can access highly sensitive information. Implement a Zero Trust architecture model, use multi-factor authentication (MFA), and set up role-based access control (RBAC) to restrict who sees your data based on user roles. 
  

• Anonymize personal data by masking or tokenizing sensitive data to reduce the risks in case of data breaches. You can also tokenize for roles that only require a small portion of the data for client verification, e.g., viewing the last four digits of a credit card to validate a customer’s identity.
  

• Set regular and secure backups and implement a disaster recovery standard operating procedure (SOP) to define what to do in case of data loss. Make sure your backups are stored securely and encrypted.
  

• Train your employees to promote a security-first culture. This should happen regularly and include information about handling sensitive data, phishing attempts, and incident response protocols.

By following these practices, you’ll be able to establish secure data engineering practices to protect your business information before, during, and after incidents. 

How NaNLABS helps cybersecurity clients improve their data engineering practices

At NaNLASB, we offer data engineering services for multiple industries, including cybersecurity. That’s why we understand this industry’s unique challenges and requirements. 

Both through data engineering development and consulting, we’ve helped clients improve their software architectures. Here are examples of solutions we offered to cybersecurity companies: 

Building a scalable application for a cybersecurity company 

This client is a Device Context startup for enterprise cybersecurity. It developed an Internet of Things (IoT) device security risk assessment tool that scans radio and networks to identify unmanaged devices. 

This client specializes in using and applying Zero Trust principles to guarantee everyone connecting to the device is authenticated and authorized. 

When we started working together, its main priority was to stabilize the tool’s cloud and performance to offer a better experience. 

This client’s main challenges included:

• Reducing the number of bugs to improve end-user experience

• Redesigning the platform for scalability so it could support large data volumes and user demands

• Reducing technical debt by refactoring legacy code and updating internal practices

To do so, the NaNLABS team came up with three main solutions:

1. A full stack upgrade including improved data handling and a new UI/UX design

2. Infrastructure improvements by introducing a serverless architecture design with AWS Lambda, dynamic real-time queries, and efficient data processing

3. Improvement of code quality 

This led to a 50-60% reduction of technical debt, streamlined user experience, 3x decrease in debugging time, and drop in costs due to the new serverless architecture.

Designing the software architecture for a privacy management firm

This client translates security clauses in policies into actionable steps for developers. This way, they can easily incorporate security and privacy requirements into software.

This privacy management client’s main challenges included: 

• Narrowing down the scope of the tool to meet realistic goals

• Designing the software architecture with scalability in mind

The NaNLABS team managed to solve those challenges by: 

• Defining the scope and designing a software architecture aligned with the client’s goals and timelines

• Introducing modern tools and technologies to accelerate development 

This led to high-quality scalable software with minimal technical debt, leveraging AWS tools to streamline development and deployment. We also improved this privacy management client’s AWS knowledge and implementation capabilities, using AWS’s cloud services to optimize development and costs. All of this without compromising data privacy or security.

Data engineering for cybersecurity: Overcoming unique industry challenges

Data engineering plays a crucial role in ensuring robust data protection, privacy, and compliance.

Throughout this article, we explored the key requirements and challenges data engineers face when working in cybersecurity firms. Including the need for advanced data encryption, secure data storage, and regular back-ups. 

To address these challenges, follow data engineering best practices such as ensuring data integrity, minimizing data collection, and implementing comprehensive encryption and access control measures. Plus, setting up solid incident response plans and regularly auditing data security controls.

If you don’t have the time or ability to handle these data engineering requirements in-house, let our team become your technical sidekick.

At NaNLABS, we understand the unique demands of data engineering in cybersecurity and can help you overcome your internal challenges.

With the right experience in developing scalable applications, designing secure software architectures, and leveraging advanced technologies, we can support you in building and maintaining secure platforms. This way, you can stay ahead of emerging threats and protect your business.

Build scalable, secure, and compliant cybersecurity applications with NaNLABS. We’ll help you overcome your unique data engineering challenges and design secure data architecture. 
software engineers.

Register for free