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AI Is the Top Cyber Security Innovation of 2021

Modern technologies like digitally connected devices (and broadly, the Internet of Things or “IoT”) have been integrated into every facet of modern life — personal, professional and societal. Along with this proliferation of advanced IoT devices comes increased cyber security risks for individuals, businesses and governments.

Because of this, cyber security professionals are in high demand. The online Master of Business Administration (MBA) with a concentration in Cyber Security Management from St. Thomas University (STU) is designed for professionals pursuing leadership roles in this burgeoning field.

These professionals must be experts regarding cutting-edge technologies used in cyber security. STU’s MBA in Cyber Security Management program helps students develop this expertise by examining innovative technologies used by cyber security professionals and malicious cybercriminals alike. Artificial intelligence (AI) is at the forefront among these innovations, central to today’s most advanced cyber security systems and programs.

What Is AI?

Briefly, AI and related concepts like machine learning and neural networks are used interchangeably for certain functions. But there are important distinctions when considering how AI is used in cyber security.

In general, AI refers to computer programs that can do things previously thought to require human intelligence. Machine learning (ML) is a subset or branch of AI and refers to computers’ ability to train themselves through experience, not human intervention, using complex algorithms to analyze vast amounts of data.

An artificial neural network (ANN) is an advanced type of deep learning and subset of ML. ANNs are designed to mimic the powerful neural networks of the human brain. The human brain can process and learn from immense amounts of integrated information gathered from many sensors (eyes, ears, skin, etc.). A neural network works similarly, integrating multiple layers of input from myriad sources to analyze, learn and make decisions.

What Does Cyber Security Protect Against?

Cyber security aims to protect the information assets of people, organizations and governments against attacks in cyber space. The current prevalence of cybercrime is directly tied to the modern explosion of data available via the internet and cloud services, social media platforms, internal networks, IoT devices and more.

There are many avenues for adversaries or “bad actors” to hack into such digital systems and access sensitive data. They may take advantage of vulnerabilities in a business’ building management system (BMS), a healthcare organization’s electronic health record system or a government agency’s database.

Personal digital devices and smart home devices can also be vulnerable to cyber threats. Ironically, even smart locks and home security systems may provide a cybercriminal with a virtual back door into someone’s private, sensitive data.

Cyberattacks can threaten personal, business and national security. Data can be used for malicious purposes, as evidenced by recent years’ data breaches, election interference, ransomware incidents and the hacking of federal agencies. These attacks may be financially or politically motivated, or they may be acts of outright cyber warfare or terrorism.

How Is AI Used in Cyber Security?

Cyber security strives to predict, detect and counter any threats on protected data. Thus, cyber security is involved with securing and monitoring substantial amounts of data flowing between many digital sources.

With ANN-based AI, cyber security systems can gather and process these vast quantities of data exponentially faster than humans. These systems excel at recognizing patterns in analyzed information and detecting, identifying and addressing anomalous events (i.e., threats) with unprecedented speed.

Using ML, computer programs can also learn how to analyze security systems to identify vulnerabilities. AI can even be used for cyber “red teaming,” simulating complex cyberattacks to expose a system’s vulnerabilities. This can greatly help cyber security professionals address potential problems before they turn into successful cyberattacks.

Neural networks also have immense potential in better securing interconnected networks and systems of devices like smart homes and other smart buildings. Using AI and the IoT, smart buildings can greatly improve efficiency, resource optimization, savings, safety, customer service, convenience and security for people and businesses. Yet, individual smart devices or components in such a system may be poorly secured against cyber threats, rendering the network and entire system vulnerable.

ANN-based security programs could be applied to the smart building system network, monitoring all components for security vulnerabilities and recognizing strange activity. System inputs and outputs are monitored and controlled to ensure only intended information goes in and out and all access is authorized and secure.

This means neural networks and other advanced AI types will be crucial to facilitate the integration of multiple systems, forming more effective and secure networks. This could include combining physical security, information security, data management, management information systems and cyber security programs. Clearly, AI innovations are central to the future of cyber security and individual, organizational and national security as a whole.

Learn more about St. Thomas University’s online Master of Business Administration with a concentration in Cyber Security Management program.


AI Trends: AI Can Help Protect Smart Home IoT Devices from Hackers

Brookings: How to Improve Cybersecurity for Artificial Intelligence

IBM: AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What’s the Difference?

IoT Security Foundation: Can You Trust Your Smart Building?

IoT Security Foundation: Machine Learning Will be Key to Securing IoT in Smart Homes

National Institute of Standards and Technology: IOT Security and The Role of AI/ML to Combat Emerging Cyber Threats in Cloud Computing Environment

Security Industry Association: Machine Learning, Artificial Neural Networks & Deep Learning in the Security Industry

Towards AI: Machine Learning (ML) vs. Artificial Intelligence (AI) — Crucial Differences

UpGrad: Machine Learning vs Neural Networks: What is the Difference?

Xlpat Labs: How AI and Machine Learning Can Help With Government Security Strategies

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