How Can AI Help Cyber Security in Solving Complex Security Problems?


Cyber-attacks have been creating a lot of impact on the business. These attacks have been creating a lot of pressure on security teams. Cyber attacks are not the only challenge faced by security professionals. In addition to them, there are a few more problems that make life challenging. We will share four main issues facing cybersecurity teams and how can AI help cybersecurity solve those problems with some real-world examples.

How Can AI Help Cyber Security in Managing the Massive Amount of Data?

As a security professional, it is your prime responsibility to ensure that the data and assets of your organization are protected and secure from cyber-attacks. Organizations generate tons of data each day. Data is buried in the petabytes of logs, network packets, and files generated every second by almost every device or software on your organization’s network. No matter how much data your organization develops, it’s a security team’s job to detect and prevent intrusions on the corporate network. Analyzing such a massive amount of data is impractical. This process can’t be human-driven. You may rely on several different tools which were designed to detect signs of suspicious activity. But, such devices have their own limitations. Their programmatic and rule-based approach does not scale to handle the massive amount of data efficiently. Let’s see how AI can help cybersecurity manage a massive amount of data efficiently.

For example, if your organization sets your goal to allow only legitimate traffic to your network, identify and stop malicious traffic. You can achieve this goal by deploying an Intrusion Detection/ Prevention System (IDS/IPS) on your organization’s network. IDS/IPS systems are designed to constantly scan and parse incoming network packets and match them with the known malicious signature stored in their database. In this way, IDS/IPS helps in identifying malicious traffic. The problem with IPS/IDS systems is if it fails to match a signature, or in the worst case, the signature doesn’t exist in the database, it fails to identify intrusion, and the attacks will go undetected. This proves that signature-matching approaches are constantly under stress.

Artificial intelligence can enormously lower the stress created by the signature-matching approach. When you deploy an IDS/IPS tool that is shipped with artificial intelligence techniques instead of pattern matching, Such IDS/IPS tools can create their own model by training themselves to apply the machine learning techniques on the incoming packet stream. As long as the model receives the incoming stream, it becomes perfect. At some point, your IDS/IPS arrives at a trained model that uses only the necessary parameters to detect an intrusion. The IDS/IPS systems determine if a new event is an intrusion.

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How Can AI Help Cybersecurity in Solving the Problem of Context in Cybersecurity?

Irrespective of your profession, whether you are a security professional, software developer, business analyst, HR manager, or top-level executive, being a responsible employee, it’s a primary duty to stop the leakage of an organization’s confidential data. As a security professional, how do you ensure that the employees of your organization leak confidential data to undesired recipients either intentionally or accidentally? This phenomenon is collectively termed a ‘data leak’.

 

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Data leak is not a new thing; It’s more common than you would think. Some common examples are employees unknowingly uploading an organization’s documents to their personal cloud storage or a disgruntled employee intentionally sharing confidential data with external third parties.

We have a custom-designed tool to manage data leaks which is Data Loss Prevention, also known as DLP. DLP solution consistently looks for signs of confidential data crossing the organization’s network. It blocks the transmission and notifies the security team if it finds that the activity is suspicious. Traditional DLP software uses a text-matching approach to look for fingerprints or patterns against predetermined words or phrases. The problem associated with this approach of detection is If you set the DLP thresholds too high, it will begin restricting even genuine messages. For example, an email from the CEO to your top customer could be blocked.
On the other hand, If you set the DLP thresholds too low, the organization will lose control over its confidential data. The confidential data of the organization starts appearing in the personal mailbox of your employees. This happens because traditional DLP doesn’t understand the context. It just works on a text-matching approach.

The ideal DLP must understand the context on the spot to work perfectly. Let’s see how an AI-powered DLP is trained and then used to identify sensitive data based on the context. The machine learning model is fed multiple sets of training data. The first training set includes words and phrases that must be protected, such as technical information, personal information, intellectual property, etc. The second set includes unprotected data that must be ignored. The third and last model is fed information about semantic relationships among the words using a technique known as word embedding. The model is then trained using a variety of learning algorithms. The AI-powered DLP can be capable of assigning a sensitivity level to a document. Based on the sensitivity level, the DLP makes decisions to block the transmission and generates a notification or merely lets the transmission go.

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How Can AI Help Cyber Security in Solving the Problem of Precision and Accuracy?

It’s always tricky for security professionals to be accurate in their decisions while dealing with a massive amount of data. For example, how sure is the security professional about the hidden vulnerabilities in the code before going to the developer? Similarly, how sure is the security operations team about a security breach discovered from the network logs? Was that a real cyber-attack or a legitimate regular, occasional activity that hasn’t been captured before? We are talking about the problem of false positives. Security professionals should be very cautious about false positives. Reporting false positives could increase the burden on the organization’s resources and mislead the security team about a real issue that may be hidden behind the scenes.

Let’s illustrate the need for accuracy and precision by looking into a phishing attack. For enterprises, phishing attacks are extremely dangerous because such attacks use normal channels of communication such as emails or messaging apps. For example, an unsuspecting person receives an email that says that his/her personal information has been part of a security breach. To an untrained eye, the email looks entirely credible. Furthermore, the email invites him/her to enroll for free identity protection monitoring. But, the attacker has composed the email with a fake enrollment page URL which goes to a fake website to capture personal information.

 

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The traditional approach to catch such fake websites used for phishing attacks is to compare the URL against blocklists. But the problem is that the blocklists can get outdated quickly and lead to statistical errors. A false positive error means blocking a simple website because the phishing detection algorithm failed to classify it correctly. A false negative error means failing to detect a fraudulent website. So how do you come up with a solution that is intelligent enough to analyze a website on many different dimensions and categorize that as genuine or fake?

False-positive problems can be managed efficiently by using an AI-enabled trained phishing detection module. A website that is genuine and trustworthy is going to exhibit a pattern of attributes along three domains. For example, its reputation in the form of incoming links, certificate provider, and Whois records. Similarly, such a website will also exhibit a pattern in its network characteristics and its site content. Of course, you wouldn’t know to begin with which of these features correlate with the genuineness of a website. But by experimentation with a group of features, you arrive at a trained model that is accurate enough for the phishing use case. When a user tries to access a website before the content of the website is returned to the browser, the web server queries the anti-phishing system whether the requested URL is permitted. If the system approves it, the user is shown the content. Otherwise, the user is notified that the contents of the website are malicious and, therefore, will not be displayed.

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How Can AI Help Cyber Security in Solving the Problem Speed?

For a security professional, ‘time’ is as important as context and accuracy. It would be of no use if they were a bit too late. By the time you are able to detect a security threat in your network, the attacker has stolen the sensitive data of your organization and sold it on the dark web.

Most of the time, attackers operate with patients, are persistent, and are in stealth mode. On top of that, the organization’s noisy environment will provide a safe pass to achieve their task. Let’s see how AI can help cybersecurity in solving the problem of speed. AI doesn’t help in improving your response time after an incident has occurred. But, it helps you to be prepared by adding the ability to predict a future incident by analyzing the behavior and events ongoing in your environment. You can reasonably predict whether the circumstances point to a feature attack.

Let’s understand how this predictive analysis can help with a real-world example. We all know that the authentication system works by verifying valid credentials. Valid credentials don’t necessarily mean that the requester is the person we think he or she is. It could be possible that an attacker has compromised the credentials and impersonated the actual user. How can we confirm that? A user’s predictive model that learns from the characteristics of previous logins by organizational users. Some examples of those characteristics are the IP address of the user, his geo-location, typical days of login and times of login, and so on. A trained AI model can find patterns across many dimensions beyond the reach of a human being and a rule-based pragmatic approach.

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