The ultimate answer against cybersecurity threats: big data security analytics

In today’s digitalized world, data security has found new challenges. There’s no denying that the data-driven mindset leads to a wider complexity. Volume and diversity of data increase. Technologies like cloud, AI, and Machine Learning blur corporate security frontiers.

And, as the possibilities got more fascinating, the cyber attacks got more sophisticated. Data security breaches, network intrusions, or compliance violations appear. As most big data tools are open source, danger is on the horizon.

But do not panic. Big data presents new risks but also the answer to fight them: big data security analytics. This paradigm shift in cybersecurity takes big data intelligence to greater highs. Let’s dive deeper.

What big data security analytics stands for

Big data security analytics are cybersecurity measures crafted to protect big data systems. They guard large amounts of data from attacks, thefts, or malicious activities. These tools can either work on the online or offline domain with no distinction.

The most common threats that big data security analytics fight against are:

Information theft
Distributed Denial of Service: when a cybercriminal hacks a big data system so that users can’t access it.
Ransomware: When the hacker encrypts the data and asks for money to give it back.
Data erasure

Big data security analytics brings a response to these challenges. This new generation of analytics face threats by collecting, storing, and analyzing huge amounts of security data in real time. Unlike traditional SIEM (Security Information and Event Management) solutions, big data security analytics generate and rank security alerts based on a risk model. This way, they detect and mitigate cyber attacks quicker.

It’s not that hard to imagine the importance of big data security analytics in cases where the information at risk is highly sensitive or confidential. For example, when there are contact details, credit card numbers, or national security documentation. Keeping data safe can be decisive in critical situations. Using big data security intelligence can be a smart choice against financial litigations or menaces to civil rights.

The big data security paradigm and its impact on cybersecurity

Back in the old days, security analytical techniques were mainly two:

Correlation rules: rules manually defined that point to a potential security incident relating it to a specific sequence of events.
Risk assessment: monitoring networks for known patterns and vulnerabilities.

And what do they have in common? Besides the fact of being extremely rigid (and, thus, prone to false positives), none of them had the ability to detect unexpected events. And here is the first game-changer effect of big data in the security sector: predictive detection.

With the help of advanced big data security analytics, now it’s possible to identify and analyze complex use patterns in constant change. These cybersecurity heroes take cyber resilience to new highs. Working in real-time, big data security analytics go beyond simple rule-based approaches. They execute sophisticated analyses of current and historical data using correlation algorithms. By doing so, they detect original anomalies across many sources of data from servers and network events to user activity.

The second important contribution of big data to cyber security is the fact of being able to operate beyond network borders – an upgrade from old network security strategies. With big data intelligence, achieving a flexible end-to-end paradigm is possible. This means taking care of the three data stages of any big data environment.

Data stages in a big data environment

Stage 1: incoming data

Big data systems feed from a large amount of different sources and data types:

CRM (Customer Relationship Management) data
ERM (Enterprise Relationship Management) data
Transactional and database data
Machine-generated data (from logs or sensors)
Unstructured data like emails and social media posts

The first step to secure a network is to check that the data in transit going to the system, is not malicious. With a big data security analytics solution like a firewall, it’s possible to run a security check of the incoming data before it hits the ground.

Stage 2: stored data

Once the data is inside the system, saving its integrity is important. Encryption at rest, for example, is a big data solution that facilitates confidentiality and strong authentication to prevent intrusion. It will also be advisable to run big data security checks across the following:

Log files
Internal analytics tools
Distributed cluster platforms with many servers and nodes

Stage 3: outgoing data

Every big data system has a massive amount of output results. In fact, one of its major functions is to process all this data in an effective way. The goal: make it available for end users through applications, reports, and dashboards. Flaws at this point can lead to information leakage. Also, they can result in other security compliance issues, with end users receiving malicious or altered data.

Encryption is, again, an appropriate big data security solution for this case. It’s perfect at guarding valuable output that can be an attractive target for hackers.

Main big data security challenges

We have gone through the core characteristics of big data security analytics. Now, it’s time to take a look at the main challenges this technology faces.

Distributed data

The majority of big data systems function in a distributed manner. Frameworks store and process data across many nodes to increase efficiency and velocity. Most distributed big data frameworks were designed without security in mind (like Hadoop). This fact makes them more vulnerable and difficult to secure.

Unstructured data

Another challenge for big data security is non-relational databases (NoSQL). This type of database overcomes the scalability dilemma of relational databases. They replace the traditional schema of rows and columns for optimized storage models that adapt to data type.

As they are still in the early stages of development, NoSQL databases focus on flexibility over security. For this reason, companies chose to place unstructured big data in a trusted environment with extra safety protocols.

Endpoint vulnerabilities

Big Data systems receive information from various sources and send data to numerous destinations. All these ports that deliver to and receive data from the system are called endpoints. As it’s not difficult to imagine, endpoints are potential big data security threats. Hackers are especially fond of finding vulnerabilities at endpoints. For example, they manipulate information to send false data to data lakes.

Data mining solutions

Data mining is at the very core of big data systems. It consists of analyzing huge volumes of unstructured data to find trends and patterns. This way, it’s possible to gain valuable insights for prediction and decision-making.

It’s common for big data administrators to mine data without permission or notification. As information can be private or confidential, it’s vital to check on unusual access activity no matter where it comes from.

Granular access

Most organizations have the best practice of restricting access to sensitive data. But, it’s also common for them to have external stakeholders that need some kind of touch point with their internal information. In this case, the usual solution is to grant granular access which means a specific person can access and see only the data they need. As big data technologies are not designed for granular access, it’s key to find a security solution like duplicating the required data.

Real-time security

“Real-time” is big data’s middle name. A clear big data security challenge is to provide cybersecurity to a system that processes tons of data flowing by the second. And everything, of course, without compromising its performance.

8 big data security solutions

We have navigated the principal challenges of big data cybersecurity. Let’s jump to the bright side and take a glimpse at the main solutions designed to face these issues.

1. Encryption

Data encryption is the process of translating data from plaintext to ciphertext. A well-implemented encryption process will make encrypted data unreadable. Yes, even when a hacker achieves access to data bulks of sensitive files. In big data environments, scalable encryption is a key factor. Why? Because it becomes essential to protect large volumes of data from many types and formats:

In-rest and in-transit data
Analytics toolsets and their output
User and machine-generated data
Stored data in relational database management systems (RDBMS)
Stored data in non-relational databases (like NoSQL)
Stored data in specialized file systems (like Hadoop)

2. User access control

This is one of the most basic and important solutions for big data cybersecurity. With it, it’s viable to gain robust protection by managing control access over big data pipelines—all thanks to automated role-based settings and policies.

For example, a good approach is the principle of least privileges. With this approach, individuals are limited only to the tools and data they need to perform their user’s tasks. A more complex user access control technique can manage multiple administrator settings.

3. Cloud security monitoring

Big data security analytics can monitor high-performance cloud computing services. It’s no news that these infrastructures store and process big data workloads. With automated scanning tools, securing public cloud assets is easier than ever. Advance analytics check blind spots like:

Exposed API keys
Tokens
Misconfigurations

4. Centralized key management

Key management is the technique of securing encryption keys from loss or misuse. It has a differential approach to distributed or application-specific key management. Centralized key management uses a single point to ensure effective control. It goes from creation to key rotation. Best practices include:

Policy-driven automation
Bring Your Own Key (BYOK)
Logging
On-demand key delivery
Abstraction of key management from key usage

5. Network traffic analysis

The traffic flow in big data systems is constant. And, as we’ve seen, it comes from a varied range of sources, including fast-paced ones like social media platforms or user endpoints. Network Traffic Analysis is a powerful big data security analytic tool. It ensures network traffic visibility to detect potential big data security issues. For example, it identifies malicious data from IoT devices or unencrypted communication protocols.

6. Intrusion and insider threat detection

Due to the distributed nature of big data architecture, the danger of intrusion attempts is on the rise. What’s the solution? An Intrusion Prevention System (IPS) sitting behind firewalls. An IPS isolates the invader before it strikes. In case the intrusion succeeds, it’s possible to perform an IDS quarantine.

Moreover, big data systems can suffer from threats from malicious insiders. They can access sensitive data such as login credentials and insights attractive to competitors. Insider threat detection can disclose a lot of anomalies. For example, unexpected data downloads or unusual login times. The strategy consists in examining logs for common corporate applications like:

Remote Desktop Protocol (RDP)
Virtual Private Network (VPN)
Active Directory
Endpoints

7. Thread hunting

Thread hunting has been, for a long time, one of the main cybersecurity solutions for the search for undetected threats in networks. How does it work? With a specialized skill set, professional analysts hypothesize about potential risks. They use data from real-world attacks and correlating findings from security tools. Nowadays, thread hunting is a much more sophisticated solution for big data environments. All thanks to the power of big data security analytics. Now, with thread hunting, it is possible to monitor large volumes of security data in the blink of an eye.

8. User behavior analytics

Big data security analytics provide a way to run user behavior analysis in massive databases. They allow the creation of system user profiles to detect deviations from normal activity in applications and devices. This way, it’s easier to detect compromised user accounts. It’s time to say goodbye to threats to the integrity and confidentiality of big data assets.

Next-generation big data security analytics

There’s no doubt that big data security analytics is changing the cybersecurity landscape for the better. Today, the next generation of Security Information and Event Management (SIEM) systems is going beyond traditional correlation rules. Powered by machine learning, deep learning, and User and Entity Behavior Analytics (UEBA), they: