Data Analytics, AI, & ML in Cybersecurity

CIAT. Edu offers program paths for people seeking a career in cybersecurity or data analytics.

Students considering a cybersecurity data science and analytics career will discover an incredible future in this domain. 

The future of all organizations, no matter what marketplace today and in the future, will be to leverage artificial intelligence (AI) and machine learning (ML) to create valuable data analytics. 

This article will outline the basics of AI and ML; how AI and ML will impact the marketplace; and how cybersecurity careers can leverage AI and ML in the future.

What are AI and ML?

Artificial Intelligence (AI) is a concept that has existed for quite some time. It was only recently able to harness enough computing power to create something that could be considered artificial intelligence, or intelligence (perceiving, inferring, and synthesizing information) demonstrated by machines. AI needs a massive amount of data to be relevant. Analysis from security alerts, financial transactional data sets, and customer information all feed into AI. 

Machine learning is a field of AI and computer sciences that uses computers to emulate human learning processes. Data from AI feeds into machine learning models to create data sets for practical application. 

Let’s take, for example, an organization that has terabytes of information containing customer sales information for the last three years. With AI, that organization can process the data and look for valuable components and elements. AI can then feed the resulting features into machine learning. The resulting data set could include which products sold the most in a specific peak time during the year for the organization.

Deep down, machine intelligence is just another type of AI. It mimics how our brains function by using multiple levels of processing to solve problems.

Where are AI and ML Going in the Marketplace?

All organizations generate a massive amount of data. Finding a statistical analysis structure leveraging machine learning models, business intelligence, human intelligence, and predictive analysis provides a means for organizations to monetize their data. AI processes the data sets into rational components that feed into machine learning to create valuable analytics data. 

Developing ML-powered analytic applications for business users has required deep knowledge of both ML and business processes. It also involved complex collaborations among people who were more comfortable using workflow and visualization tools than prompts and command lines.

The principles of statistics can be used across almost any industry. Businesses use statisticians to analyze trends and patterns to inform future business decisions. Usually, existing historical records are examined so existing trends can be detected.

Students studying data analytics should have a strong background in statistics and probability mathematics, computer science, Python, and the basics of data science. Each of these domains comprises the data analytics role within an organization.

What is the Role of Data Analytics in Cybersecurity?

What if, for a moment, a hacker compromised the AI engine before sending the data components to machine learning? What if a cybercriminal had been manipulating the data lakes and data warehouses in the cloud for over a year? What would happen to the organization if the output from machine learning data sets showed sales and business trends that were not true? 

The role of a cybersecurity analyst with a degree in cybersecurity and data analysis would become invaluable to the organization. Protecting the big data, ensuring the AI engines do not become compromised, and safeguarding machine learning algorithms are critical to any modern organization. Companies spend millions of dollars on data analytics projects. A cybersecurity event would ruin these projects and, more importantly, the organizations themselves. 

Security professionals know machine learning tools only work if they are protected. Most organizations’ potential vulnerability to AI and ML systems is relatively high. A cybersecurity strategy designed to protect AI and ML systems from ransomware and malware attacks is vital.

With the increasing adoption of data analysis tools, cyber security analysts are increasingly viewed as experts who cover multiple facets of cyber security.

Threat Intelligence

There are several different kinds of security issues that could affect a network. Knowing the different types of security issues and how they might appear in a network is the first thing security analysts should do when trying to detect any problems.

Intrusion Detection and Response

Intrusive monitoring tools may be better suited for detecting known attacks, but they’re still helpful for any security analyst.

Risk Analysis

When dealing with potential cyberattacks, security analysts must be able to convey the level of risk. A company cannot afford full-scale responses for every attack. Security has become a business imperative, and so companies need to build a financial case for taking action (or not) against threats.

Data Analysis

To understand why something happened, you need to know what usually happens. AI and automated systems use past performance to predict future outcomes. Knowing how to create and update models is an advanced skill set that can pay off for any security analyst.

What is the Role of a Cybersecurity Data Analyst Engineer?

By 2023, there will be an estimated 1.5 million cybersecurity professionals shortage. Don’t expect the need for cybersecurity experts to decrease anytime soon.

A cybersecurity data security analyst works to protect organizations from cyber attacks and security breaches by protecting their organization’s network infrastructure, including computers, servers, and software applications. A cybersecurity data analyst should be able to identify common vulnerabilities, suspicious activities, and future attacks. Bad actors recognize the value of hacking into organizations’ big data and AI systems.

The cybersecurity analyst also focuses on several sub-cybersecurity domains, including:

• Configuring SecOps, DevOps, and NetSecOps tools: These might include antivirus software, password protection, and vulnerability management software, which will evaluate what the company requires and then provide them with the right security solutions. 
• Analyzing security alerts
• Managing adequate security measures
• Performing security monitoring and reporting: An expert will analyze the current state of the network and determine whether anything unusual is happening. They will then identify which parts of the network are protected and which aren’t.
• Evaluating weaknesses and vulnerabilities in critical systems: A good security team doesn’t just focus on one aspect of security; they look for vulnerabilities everywhere. They constantly test their systems and find ways to improve them so that no one can get into their system.
• Staying current on security risks, network security standards, and cybersecurity issues impacting their industry

Knowledge for Today and in the Future

Data analytics is an exciting new field. It’s increasingly complex, yet it also adds incredible value to any organization. Students looking for a career with unlimited growth for years to come should consider becoming a cybersecurity data analyst engineer. The need for a qualified engineer could not be greater than now. The cyber threat landscape continues to grow across the globe. Entry-level and specialized positions in cybersecurity continue to be in great demand.