The Role of AI in Data Security

Artificial Intelligence (AI) is considered by many to be the present and future of the tech industry. Many industry leaders use AI for various applications to provide valuable services and prepare their businesses for the future.

One of the most common and crucial uses of AI is in data security, which is one of the most essential assets of any tech-focused organization. With sensitive data ranging from organizational secrets to customer data (such as credit card information) stored online, safeguarding data is critical for meeting both the legal and operational obligations of a business. Nowadays, this task is as challenging as it is critical, and many companies use AI-based security solutions to keep their data away from the wrong hands.

What Is Artificial Intelligence?

Artificial Intelligence (AI) is a branch in computer science dedicated to making electronic machines and software smart and perform human tasks. AI is a very broad concept and a core area of computer science, it can be applied to many fields such as learning, planning, problem-solving, speech recognition, object detection and tracking and other security purposes.

There are many subsets of artificial intelligence. In this article, we will cover two of them:

• Machine Learning (ML)

• Deep Learning (DL)

We will explain the basics of each subset and how they can be applied in AI data security solutions.

What Is Machine Learning?

Computer systems based on Machine Learning (ML) are able to learn and perform tasks without explicit instructions. To do so, these systems use ML algorithms and statistical models to identify, analyze and understand patterns in data. Machine learning tools are widely used to automatically perform many tasks that are traditionally done by humans.

Unsupervised learning is a machine learning technique that allows ML algorithms to perform tasks without specific instructions on how and achieve desired results. This technique learns the best solutions to a problem based on experience and analysis. The model is fed with an input (a task that it needs to complete) and it can make autonomous decisions on the best course of action. The more time the model solves the task, the better it is trained and the more efficient it becomes.

The advantage of ML for many tasks is obvious, machines do not get tired and aren’t bothered doing the same repetitive tasks numerous times. Additionally, they help reducing workloads significantly by automating many procedures in work chains. For example, security teams can use AI-based solutions (which will be discussed later) to automatically detect threats and deal with some of them, reducing the amount of human interaction required for certain security tasks.

What Is Deep Learning?

A subtype of machine learning, Deep Learning (DL) extracts attributes and features from raw data using Artificial Neural Networks (ANNs). The idea behind deep learning networks is to mimic the connections and functions of the neurons of the human brain.

Convolutional Neural Networks (CNNs) are a type of ANN comprised of multiple layers that is commonly used to perform DL tasks. The depth of the CNN is determined by the number of layers within the network. Typically, deeper networks are better at more complex tasks but are more difficult to manage.

Deep learning is a newer and more complex and more advanced form of AI compared to machine learning. Deep learning tasks are highly intensive and were out of reach for many private users and even businesses. This whole changed with the introduction of more power and cheaper computer hardware solutions and most importantly making Graphics Processing Units (GPUs) stronger and more accessible.

GPUs are designed to perform 3D models and tasks in computing environments and are most widely used for computer games and 3D modelling. However, the architectures of GPUs make them an excellent choice to power deep learning platforms, although they can be expensive.

You can make deep learning even more manageable by using deep learning platforms, such as MissingLink, which offer features such as automating experiments and tracking them across multiple machines. Deep learning platforms can help you reduce costs of on-premise GPU-clusters or cloud-based GPU services and run your deep learning algorithms more efficiently.

The Role of AI In Data Security

Nowadays, the role of artificial intelligence in the industry is crucial as the world is smarter and more connected than ever before. Many reports estimate that cyberattacks will become more tenacious as time goes by and security teams will have to rely on AI solutions to keep systems and data in check.

Threats

The first step to understand the role of AI in data security is to learn about the various types of threats to data security:

• Social Engineering ‘a technique bad actors use to manipulate users into granting them access or valuable information. This technique can be combined with other types of cyberattacks. For example, attackers can disguise as legitimate sources and convince users to download and activate malware or enter malicious sites.

• Phishing ‘a type of social engineering, phishing is the most common threat and it is typically performed by sending messages and emails cloaked as legitimate to trick victims into giving valuable data or downloading malware that can steal the data itself, such as trojan horses.

• Malware (malicious software) ‘a general term that describes any type of software designed mainly to damage networks and devices such as computers and smartphones.

• Ransomware ‘a form of malware designed to extort victims for money by blocking access to settings and files until a ransom is paid. To regain access and control of their system, users must pay the ransom (typically within a time-frame) and activate a decryption key they are typically (not always!) given by the bad actor after payment is due. It is important to remember that even if access is restored, the attacker still had free access for all the data in the system and could still have a copy stored in their system so valuable information is still at risk.

• Advanced Persistent Threats (APTs) ‘a type of attack used by actors to infiltrate the network undetected and maintain their position for long periods. This technique is efficient because it can be harder to detect since the attacker can steal valuable data without raising alerts.

• Zero-day exploit ‘time-sensitive attacks that leverage vulnerabilities soon after they are discovered before a patch and fix were created. These attacks are hard for security teams to mitigate and could lead to serious damage to valuable data.

• SQL injection ‘attackers use Structured Query Language (SQL) injection to access an SQL server and run malicious code. The idea behind this attack is to leverage a vulnerability within SQL servers and force them to reveal information hidden information.

• Denial-of-Service (DOS) attack ‘bad actors use this technique to overflow networks and servers with traffic so they will run out of resources and become unavailable to users.

• Insider threat ‘a type of attack in which organizations are targeted by people they employ directly or indirectly. There are various types of insider attacks and they typically target valuable and sensitive data to the organization

• Data breach ‘a vulnerability that allows unauthorized parties to access valuable and sensitive data such as user information, passwords, credit card or any sensitive and private information.

AI-Powered Data Security Solutions

AI-driven security tools are capable of reducing the risks and even manage many of the threats to data security. They can do this either by themselves through automation and detection or by providing security teams and Security Operation Centers (SOCs) with enhanced capabilities.

• Security Information and Event Management (SIEM) ‘a security tool that uses rules and statistical correlations to actionable information on security events and helps security teams deal with events across the entire organizational environment. With the information provided by SIEM, SOC staff are more equipped to deal with data security threats in real-time.

• User and Entity Behavior Analytics (UEBA) ‘a tool that uses AI to collect, track and analyze data from computer activities to indicate suspicious behaviours. UEBA learns patterns of legitimate access usage and uses these patterns to detect complex attacks like insider threats by recognizing behaviors that indicate malicious intent and jeopardize valuable data.

• Security, Orchestration, Automation, and Response (SOAR) ‘a cybersecurity solution used by organizations for data collection and alerts on threats. SOAR can detect threats and automatically deal with low-level threats quickly and efficiently.

Wrap Up

Nowadays, organizations are more aware of the importance of data and give more value to protecting it than ever before. With cyberattacks becoming ever more common and dangerous, there has never been a better time to adopt AI-driven security tools. These tools are sophisticated and are capable of protecting your valuable data from being accessed or damaged by unauthorized third-parties.