Our daily lives are being impacted by artificial intelligence in many ways, from small-scale AI systems on mobile phones that suggest the next word in our sentences to large corporations using AI to improve industrial processes. Without good technical standards and best practices, AI can undermine existing security measures, change how we interact with technology, improve our quality of life, and change security.
As the volume and complexity of cyberattacks increase, AI in security is helping operations analysts stay ahead of threats. AI tools, such as machine learning and natural language processing, are used to cut through the noise of daily alerts and dramatically reduce response times by extracting threat intelligence from millions of academic papers, blogs, and new articles. To learn more about AI in security and its role, read on.
What is AI Security?
At a basic level, AI in security involves training systems to distinguish between “safe” and ” hostile” user activity by comparing the actions of users in one environment with those in a related environment. “Deep learning as a service” is an important tool for detecting fraudulent behavior in some AI platforms, including Vectra. Deep learning uses large interconnected networks of synthetic neurons that mimic the biological structure and function of neurons in the brain. These neurons are arranged in layers, and each layer is connected to every other layer by a set of weights that change in response to incoming inputs.
Large data sets can be computed and analyzed by sophisticated AI in security solutions, enabling them to create patterns of activity that flag potentially malicious behavior. In this way, AI mimics the ability of its human counterparts to detect threats. AI in security can be used for automation, triage, alert aggregation, alert sorting, response automation, and more. AI is often used to support the early stages of an analyst’s work.
Common Applications of AI in Cybersecurity
In recent years, the use of AI and AIaaS in security has become increasingly popular. In order to mitigate cybersecurity threats before they materialize, investments have been made to create and enhance AI-powered cybersecurity systems. Let’s look at some of the key applications of AI in cybersecurity.
Malware is classified as a ransomware attack when the attacker plans to demand a sum of money in exchange for restoring the owner’s files or system access. While today’s methods can easily detect traditional malware or ransomware attacks, constantly changing hostile agents are much more difficult to detect.
AI in security systems is more successful at identifying malicious characteristics. Researchers used neural networks to detect malware with an overall accuracy of 74% across all file types, with .doc and .pdf files having the highest accuracy at 91.7% and 94.1%, respectively.
Phishing attacks are a popular technique hackers use to distribute and activate malware. Phishing is the process by which a hacker sends malicious links to users (often via email) in an attempt to steal confidential information or cause system disruption. If a user clicks on the malicious link, the infection is launched.
AI in security algorithms can detect phishing traps in emails and websites. Researchers at the University of North Dakota have proposed a machine learning-based phishing detection method. This method analyzes the structure of emails to determine whether an email is authentic or fraudulent. The accuracy was 94%, and the researchers used 4000 training samples.
Recognizing and classifying new threats
As software structures become more complex, new vulnerabilities can emerge. With so many potential digital threats, cybersecurity professionals cannot keep up. However, cybersecurity systems based on machine learning can monitor all local and sectoral threats.
Data on the latest threats and vulnerabilities are constantly updated to the AI models, enabling them to defend against new threat actors and thwart imminent attacks.
Prediction of data breach risk
Large organizations have huge inventories of IT assets, and it can be difficult to assess the risk of security breaches for each item. AI algorithms can identify the parts most vulnerable to a breach and even predict the different types of attacks that are likely to occur.
Researchers have proposed models based on cognitive learning that monitor security access points for authorized logins. To stop a potential data breach, the model can detect remote breaches early, notify users and add more layers of protection.
Early detection of hacks and breaches can help companies allocate tools and resources more efficiently, fortifying themselves against future attacks and building significant cyber resilience.
The Benefits of Leveraging AI Technologies in Security
The use of AI in security has a number of benefits, such as:
Enhanced threat detection
AI-powered systems can analyze huge amounts of data and spot patterns that could indicate a cyber-attack. This can help detect attacks that human analysts might otherwise miss.
Faster detection and response time
AI can automate the detection and response to cyber threats. As a result, action can be taken faster than if a human analyst had to identify and address the threat manually.
AI Continuous learning
AI algorithms can learn from past cyber-attacks to detect and stop future attacks better. The system becomes better and more effective at identifying threats as it is exposed to and reacts to more data.
Reduced human errors
Humans are fallible, especially when it comes to the detection and response to sophisticated cyber threats. By automating the threat detection and response process, AI technologies can help reduce the risk of human error.
Why does AI Security matter?
AI security is becoming increasingly important as the world becomes more connected and dependent on technology. Organizations generate vast amounts of sensitive and confidential data, and cybercriminals are constantly developing new and more sophisticated techniques to steal or disrupt this data. By automating the detection and response process, artificial intelligence in security helps organizations stay ahead of potential threats.
AI-powered systems can identify trends that may indicate a cyberattack and take action faster than human experts. In addition, AI in security can continuously learn from past breaches to identify and prevent future ones. This minimizes the risk of human error, allowing organizations to protect themselves from future attacks and build significant cyber resilience.
Particular AI security risks
With greater reliance on AI comes greater cybersecurity risks. Let’s take a look at some of the biggest AI issues in security.
The lack of transparency in how AI works is one of the key issues. It can be difficult for humans to understand how an AI system arrived at a particular conclusion or recommendation, making it difficult to identify potential biases or flaws in the system. It is also difficult to identify and address potential security risks due to this lack of openness.
Privacy of data
AI systems often use large amounts of data to learn and improve their capabilities. This information is often sensitive and private, and there is a risk that it could be obtained or used inappropriately. In addition, there is a risk that data could be used to train AI programs that reinforce prejudices or target particular racial or ethnic groups.
Like any other software, AI systems can be attacked. Hackers could trick an AI system into acting in ways it wasn’t intended to or into accessing private information.
The improper use of AI
AI has the potential to be misused to promote misinformation or fabricate news. AI could also be used to automate cyber-attacks, making them more sophisticated and harder to stop.
Scope of AI in security
The scope of AI in security refers to the range of applications and use cases where AI can be used to enhance security measures. The scope of AI in security is broad and encompasses many different areas, including:
- Threat detection: AI can be used to detect and respond to security threats such as malware, phishing attacks, and other forms of cybercrime.
- Fraud prevention: AI can be used to analyze large amounts of data to identify patterns of fraudulent activity and prevent fraud before it occurs.
- Surveillance and monitoring: AI can be used to monitor and analyze video and audio data to detect suspicious activity and potential security threats.
- Access control: AI can be used to authenticate users and control access to sensitive data or systems.
- Incident response: AI can be used to automate incident response processes, enabling faster and more effective responses to security incidents.
- Risk assessment: AI can be used to analyze risks and vulnerabilities in security systems and recommend actions to mitigate those risks.
- Physical security: AI can be used to enhance physical security measures such as facial recognition systems, access control systems, and security cameras.
The growing use of artificial intelligence (AI) in security is helping analysts stay ahead of threats and mitigate cybersecurity risks before they happen. AI-powered systems can analyze massive amounts of data and identify patterns that could indicate a cyberattack. They can automate the detection and response to cyber threats, reducing the risk of human error. In addition, AI can learn from past cyberattacks, becoming more effective at identifying threats as it is exposed to more data.
AI applications in cybersecurity include detecting malware and phishing attacks, identifying and classifying new threats, and predicting the risk of data breaches. With the increasing volume and complexity of cyberattacks, using AI in security has become essential to protect sensitive and private data and prevent data theft. However, good technical standards and best practices are essential to ensure that AI does not undermine existing security measures and improves our quality of life.