Using Artificial Intelligence and Machine Learning as a Powerful Cybersecurity Tool
Fight sophisticated cyber attacks with AI and ML
When “virtual” became the standard medium in early 2020 for business communications from board meetings to office happy hours, companies like Zoom found themselves hot in-demand. They also became prime targets for the next big cyber attack.
In the case of Zoom, hackers succeeded in April 2020, with a large data breach that exposed an estimated 500,000 user passwords.
This breach, along with the thousands of daily reported cyber attacks in the last year show that of the major threats facing businesses today, cybercrime nears the top of the list. Due to a combination of factors, including operations that have moved online and sensitive information crossing remote networks, opportunities for cybercrime are rife and the attacks are becoming more advanced and difficult to contain.
The average cost of a data breach was $3.86M in 2020, and it took an average of 280 days for a company to identify and contain a breach. With just these two statistics, it’s clear that cybersecurity is a landmark issue for companies of all sizes, deserving of attention and major investment.
As more companies embrace and deploy artificial intelligence (AI) and machine learning (ML) within everyday operations, there is a fear that they open themselves up for more cyber attacks. There is also concern that attackers are using AI and ML technology to launch smarter, more advanced attacks.
If this is the case, are we able fight fire with fire and use AI and machine learning within cybersecurity responses to react and prevent attacks in a more sophisticated way?
The Constant Threat of Cyberattacks
Cyber attacks come in all forms, but data breaches seem to grab the biggest headlines these days. In 2020, data breaches resulted in the exposure of 36 billion records, the majority including personally identifying information (PII), based on recent data from Risk Based Security.
Zoom wasn’t the only company to be impacted. Twitter, Marriot, Easy Jet and Nintendo all suffered from data breaches of varying sizes and consequences, shaking public trust in technology infrastructures, and bringing data privacy concerns to the forefront.
Other types of cyber attacks come in the form of phishing scams, malware and IT infrastructure hacks that can cause businesses to come grinding to a halt. Several factors are contributing to the increase in cybercrime:
- Remote operations leave businesses more vulnerable.
- Geographically distributed employees are working on their own, less secure, networks and devices
- Advancements in ways for hackers to hide their identities make anonymous attacks even easier
- COVID-19 is being used in phishing, malware and ransomware attacks
Companies are clearly responding to the increased need for cybersecurity investment. The global cybersecurity market size was valued at USD 167.13 billion in 2020 and is expected to register a CAGR of 10.9% from 2021 to 2028.
The AI Factor
The reality is that the more technology used in business operations (including AI and ML), the more opportunities there are for cybercrime. AI and ML models are vulnerable because they can be manipulated, most often through the data used to train them, to produce desired results. Then, because AI and ML models learn from previous experiences, the models continue to perpetuate the attack.
AI and ML can also be reverse engineered to reach the data that was used to train the model, providing even more opportunity for data breaches.
Like other technologies, if not used securely, AI and ML models do create vulnerabilities that can be exploited in a cyber attack. However, advancements in AI and ML have also been made within the area of cybersecurity, allowing companies to detect, identify and respond to threats faster than ever before.
As cyber-attacks evolve, cybersecurity efforts must evolve with them.
Understanding How Artificial Intelligence in Cybersecurity Works
In cybersecurity, artificial intelligence, machine learning and deep learning models can be used to create impressive tools to identify and then fight cyber attacks. AI models and ML algorithms can analyze data, detect and recognize complex patterns within it, and predict future outcomes based on the data.
The major advantage is that the models and algorithms learn as they go, becoming smarter and more sophisticated, gaining the ability to not only recognized specific cyber-attacks, but also predict what future attacks might look like.
A basic example of an application of machine learning in cybersecurity is the spam filter in email inboxes. After being trained to identify illegitimate messages, your email platform then notifies you that the sender is unsafe, and the content might be harmful.
When it comes to specific machine learning algorithms within cybersecurity, most perform either regression, classification or clustering to identify threats and how to respond to them.
Regression models compare various datasets and identifies the relation between them, generating accurate predictions and detecting when outcomes differ from the expected behavior.
Clustering saves serious time in data analysis by grouping together similar and/or related data, revealing when there are patterns of unique activity and behavior.
Classification uses labels from previous data to classify new data into groups. As an example, certain types of files can be classified as spyware based on previous data.
Advantages of Using AI Within Cybersecurity
Using these techniques (and others) AI and ML can be used to collect and process large amounts of information – and then create meaningful cybersecurity insights and suggestions from it. AI models learn to identify threats in the same way that people can and are able to understand what is a risk versus what isn’t, and identify links between threats. Additionally, AI bring the following benefits:
AI learns from experience.
Rather than needing to wait for new threats to be detected before being able to identify them, AI analyzes the data and uses past attacks to accurately predict new ones.
Data creates accurate threat profiles
Due to their ability to analyze huge amounts of information quickly, AI algorithms can predict behavior in ways that humans can’t. AI and ML models can create detailed threat profiles from existing data, which allows cybersecurity teams to identify where the next threat might come from – and be ready to react quickly.
AI and ML models are fast.
Ai and ML models can sift through large amounts of data quickly, which means they can identify potential threats earlier and make decisions that lead to the most effective response quickly. And they can monitor threats 24/7.
AI Applications in Cybersecurity in the Real World
Facial recognition technology uses image and video annotation to train models to identify individual faces for a variety of uses within cybersecurity. Facial recognition is used to track behavior of a specific person in security footage. It is also commonly used as a personal security measure in unlocking smartphones, and authorizing banking transactions, for example.
Within the banking and finance industries, AI and ML models are being used as effective tools in identifying and preventing advanced attempts at fraud. Through predictive forecasting, models can build threat profiles to prevent fraud before it happens.
When used to detect and protect against viruses, spam, and malware, a major advantage of AI is that it not only protects against known threats, but also learns to detect malicious programs over time.
Network threat analysis
Used to identify vulnerabilities in networks in businesses across industries, AI and ML models can identify threats faster and more effectively than manual analysis or software.
AI for Smart Cybersecurity
Cybersecurity is an essential consideration for any organization – especially as we become a more digital world. Cyber attacks are getting more sophisticated, requiring companies to up their game and respond in the same way. Whether preventing a future attack or analyzing why and how one happened in the past, using AI and ML models creates a faster, more comprehensive cybersecurity response.