Data analytics and cybersecurity are two separate fields of tech; however, if desired, data analytics can lead to stronger cyber securitization practices. Today, data analytics is essential for modern cybersecurity practices. Through utilizing data analytics in cybersecurity, organizations can expand beyond perimeter-based security measures and embrace a data-driven securitization approach.
Data analytics improves cybersecurity by identifying patterns that signify potential threats, enabling proactive measures. For instance, machine learning algorithms can analyze network traffic to detect anomalies that may indicate a cyber attack, allowing for real-time threat mitigation.
According to a report by Cybersecurity Ventures, cybercrime is expected to cost the world $6 trillion annually by 2021. Implementing advanced data analytics tools can reduce the average cost of a breach by up to 30%.
Source: CyberCrime Magazine
In short, tech professionals should not shy away from the power of data in enhancing their ability to identify and mitigate cyber threats. That said, let’s start by breaking down these two fields of work and learn what role data analytics should play in cybersecurity.
What is Cybersecurity?
As the digital terrain grows, the threat of hackers and data breaches is only expanding. Thus, cybersecurity is an ongoing process that looks to protect information technology systems from malicious actors. It’s an umbrella term to describe the multiplicity of processes that work to secure digital data, computer systems, and networks from damage, theft, unauthorized access, and other disruptions.
It’s the responsibility of cybersecurity professionals to ensure the integrity and availability of digital data. Through active monitoring, assessment, and preventative work, cybersecurity professionals proactively establish strong defenses against online threats.
What Responsibilities do Cybersecurity Professionals Have?
Let’s break down some of the specifics of being a cybersecurity professional. Protecting and defending computer systems from the onslaught of digital threats is work that requires foresight, attention to detail, and an awareness of current cyber threats. Of course the work will vary based upon the organization, their work, and the type of computer networks used, but generally cybersecurity professionals’ responsibilities include:
Risk assessment and management: identifying potential security vulnerabilities and assessing their risk is core to a cybersecurity professional’s work. They must then develop strategies to mitigate risks and allocate resources effectively in order to protect companies from potential attacks.
Designing and implementing secure architectures: cybersecurity professionals must develop security policies, establish standards, and determine guidelines for the organization’s IT infrastructure.
Detecting threats: threat detection and prevention is reliant upon the regular monitoring of network traffic and user activities in order to identify and block unauthorized use or malicious behavior. Equally, cybersecurity professionals must stay informed about emerging threats and evolving attack techniques.
Response and recovery: refers to the process of developing and testing incident response plans in order to best confront and mitigate security incidents. After a security breach, cybersecurity professionals must determine the source, scope, and impact of the attack. This work requires strong coordination between internal teams and external stakeholders in order to contain and resolve incidents.
Identifying vulnerabilities: much of a cybersecurity professional’s work is about predicting and preventing cyber threats. This happens through vulnerability assessments and penetration testing that help identify vulnerabilities in network systems. Once those weaknesses are identified, cybersecurity professionals must patch and update systems and applications.
Teaching cybersecurity: an organization’s cybersecurity program is only as strong as the people who carry it out make it. Ultimately all employees play a role in securing network systems, and thus, they must be educated about cybersecurity best practices and safe online behaviors. Cybersecurity professionals often conduct security training workshops in order to promote a culture of cybersecurity throughout the organization.
Compliance and regulations: when it comes to cybersecurity, organizations must be in compliance with government regulations and other industry standards. Cybersecurity professionals must conduct regular audits and assessments to verify compliance with data protection and privacy laws.
Soft skills: cybersecurity professionals must possess strong interpersonal skills such as collaboration and communication in order to align security efforts. Collaborating with cross-functional teams and communicating security risks and mitigation strategies is crucial to an organization's security.
Cybersecurity professionals must be active and vigilant in their work in order to safeguard organizations from a vast range of cyber threats. It’s a career that requires both soft and technical skills in order to fully maintain the confidentiality, integrity, and availability of an organization’s digital assets.
Source: Photo by Petter Lagson on Unsplash
What is Data Analytics?
Data analytics is the process of breaking down, cleaning, processing, and transforming raw data with the intention of securing insights that help companies make stronger data-driven decisions. Data analytics is a huge field that uses a wide range of techniques and methodologies in order to uncover patterns, trends, relationships, and other valuable information from large datasets.
Today, data analytics is utilized across industries to support organizations in making data informed decisions, solving complex problems, and optimizing their operations. The verstitle ways in which data analytics can aid companies is what makes it one of the fastest growing fields in the United States. The U.S. Bureau of Labor Statistics (BLS) estimates a 22% growth in the industry through 2030, meaning that more than 3,000 new job positions in data science will need to be filled each year. Knowing that, what exactly does it entail to be a data analyst?
What Responsibilities Do Data Analysts Have?
Data analysts must collect, process, examine, and pull meaningful insights from raw datasets. The conclusions drawn from the trends, correlations, and relationships in data sets are then utilized to make informed recommendations to organizations on how to best optimize everything from production processes to marketing campaigns. The work varies depending on the organization, but key responsibilities include:
Data collection: it’s upon data analysts to collect relevant data from numerous sources. These include databases, spreadsheets, websites, and social media platforms, among others.
Data preparation: ensuring data quality and accuracy is part of the data preparation process. Raw data is often overwhelmed with inconsistencies, missing values, and other anomalies. Data cleaning is crucial in identifying and resolving issues pertaining to data accuracy and reliability.
Data filtering: generally data must be transformed and organized such that it’s suitable for analysis. This involves updating, filtering, and normalizing data to make the analysis manageable and valuable.
Exploratory Data Analysis (EDA): EDA is the process of visualizing and summarizing data sets in order to identify trends, patterns, and other correlations within the data set.
Machine learning: this data analysis technique utilizes algorithms in order to build predictive models that give organizations insights into future outcomes based on historical data. Machine learning and predictive analysis are often used in fraud detection and customer behavior prediction.
Visualization and reporting: this is a huge piece of data analysis. The insights uncovered are only as relevant as the analyst’s ability to communicate them to stakeholders. Data analysts use charts, graphs, and other images to communicate findings to non-technical stakeholders.
These are just a few of the many responsibilities of a data analyst. It’s important to note that the work straddles technical analysis work and tactical communication skills in order to present findings. The field is constantly evolving with the introduction of artificial intelligence and other techniques that aid analysts in extracting insights from data sets.
Source: Photo by Misha Feshchak on Unsplash
How Data Analytics Enhances Cybersecurity
Data analysis and cybersecurity are two distinct areas of work, however, they overlap and can complement each other. As we know, data analysts focus on the collection and exploration of data, while cybersecurity professionals look to protect and make more resilient computer networks. That said, data sets need to be secured in order to ensure their integrity, while threat detection processes can be enhanced utilizing data analytics.
In other words, if data analysts and cybersecurity professionals collaborate, they can strengthen the reliability of data analysis processes and improve network securitization systems. Let’s learn how!
How Can Data Analysis Improve Cybersecurity and Data Privacy?
Data analysis has shown to greatly improve cybersecurity through providing organizations with insights and tools needed to effectively detect, prevent, and address cyber threats and privacy breaches. If anything, this collaboration is only becoming more urgent as the industry standards surrounding cybersecurity and data privacy law transform. Thus, tech professionals should work together and build towards cybersecurity and data privacy.
Cybersecurity data science
Cybersecurity data science refers to the application of data science techniques to cybersecurity data. It utilizes statistical analysis, machine learning, and other data science methodologies to uncover patterns, anomalies, and relationships within cybersecurity data. From here, cybersecurity data analysts can create predictive models which aid in threat detection and can enhance the organization’s cybersecurity processes.
Knowing the importance of data analysis in cybersecurity, organizations may decide to hire a cybersecurity data analyst. A cybersecurity data analyst is a tech professional who specializes in analyzing and extracting insights from cybersecurity data in order to identify potential security threats, vulnerabilities, and other relevant trends. Ultimately, their role is to carry out cybersecurity data analytics to gain deeper insights into the organization’s security landscape, and better protect sensitive data from growing cyber threats.
Big data analytics in cybersecurity
To take it even further, cybersecurity data analysts can integrate big data analytics into cybersecurity. The focus of big data analytics in cybersecurity is on utilizing advanced data analytics techniques to break down vast volumes of data, in other words, “big data,” that many traditional data processing tools wouldn’t be able to achieve.
“Big data” includes the three Vs:
Volume, velocity, and variety: there’s a significantly greater amount of data than standard datasets, it’s generated at a high speed, and takes diverse forms (video, texts, pdfs, graphics, etc).
In the context of cybersecurity, big data analytics allows data scientists to extract insights from massive datasets including network traffic, logs, social media feeds, and threat intelligence feeds.
The process can enhance traditional cybersecurity practices, leading to a more resilient security posture.
These are a few of the many ways in which data analysis can improve an organization’s network security. If you’re interested in expanding your knowledge about these evolving industries, then you're in the right place! Ironhack offers bootcamps in both data analysis and cybersecurity that will give you the skill sets to jumpstart your tech career.
Implementing Data Analytics in Your Cybersecurity Strategy
Assess Your Needs: Identify the specific cybersecurity challenges your organization faces.
Choose the Right Tools: Select data analytics tools that fit your needs, such as SIEM systems, machine learning algorithms, or anomaly detection software.
Integrate and Train: Integrate these tools with your existing systems and train your team on their use.
Monitor and Adjust: Continuously monitor the performance of your data analytics solutions and make adjustments as needed to improve efficiency and effectiveness.
About the Author:
Juliette Carreiro is a tech writer, with two years of experience writing in-depth articles for Ironhack. Covering everything from career advice and navigating the job ladder, to the future impact of AI in the global tech space, Juliette is the go-to for Ironhack’s community of aspiring tech professionals.
