Current Date :February 29, 2024

Significance of Artificial Intelligence in Information Security

Introduction to Artificial Intelligence

One of the most buzzing terms of the 21st century is Artificial Intelligence (AI).

There is an obvious mistake when it comes to words such as AI, Machine Learning (ML), and Deep Learning (DL).

Artificial Intelligence is the method of mimicking a human brain in understanding, thinking, deciding, and working to solve a difficulty. It is the ability of a machine to imitate human behavior.

Machine Learning is the subset of AI. It doesn’t deal with specific programming but is the core of AI where the principal functionality is to identify the pattern and use it for predictions. This phenomenon mainly concentrates on using and developing algorithms in determining the patterns. Some of the algorithms generated for this are Support Vector Machines (SVM), K-Means, and Naïve Bayes methods, etc.

Deep Learning helps in resolving the difficulties like Feature Extraction. The ability to learn and focus on the appropriate feature data by the guidance of a programmer is the USP of Deep Learning. The ability is accomplished by learning from the experience. Deep Learning is a subset of Machine Learning.

Goal of AI: Providing a machine the capacity to think, decision making, etc.

Goal of ML: Providing a machine the ability to read from patterns and predictions

Goal of DL: Providing machines the ability to learn by several approaches quickly

AI ML & Deep Learning in Data Security

Methods of AI in Information Security

Information Security is all about preserving data from unauthorized access. Required level of security is determined depending upon the company, projects they handle, data they process, etc.

A lot of security practitioners are being lured into thinking that AI strategies are the magic silver bullet we have been waiting for to solve all our security difficulties. Artificial Intelligence will be leveraged significantly to help with improvements in security around two dimensions – accurate identification of defects (reducing false-positive rate) and pro-active or immediate discovery of threats.

The traditional security tools are predefined with a collection of plugins, signature patterns and follow standard methods in evaluating the security of the application irrespective of the functionality and performance of the applications and report fake positives in large amounts. Security analysts are using most of their time to eliminate false positives from the noisy report produced by the automated tools. It needs some time for the security experts to recognize potential threats and to perform in-depth security evaluations. But Deep Learning or any other Machine Learning is the procedure, which has to be included with the security expert’s knowledge for algorithms to show actual security insights.

In information security, we first require to look at our goals. To make it brief, we try to use Machine Learning to recognize malicious inputs and application behavior.

Within Machine Learning, there are three ways that are categorized and they are Supervised, Unsupervised and Reinforcement models.

Supervised Learning

The area where Machine Learning has created the biggest impact on information security is Supervised Learning. Supervised Machine Learning concentrates on classifying data (labeled data). These labeled samples are obtained from existing common vulnerabilities and vulnerable applications enabling us to train the algorithm to analyze the responses from the report generated by automated tools. AI integrated automated tools reduce the false positives by examining the findings with the training data set. This approach has greatly benefited incorrect identification of flaws from Deep Learning where it has helped decrease false positive rates to a very easy number. This method gives time for the security analyst to evaluate the security loopholes at various levels by performing in-depth security assessments and other sorts of social engineering attacks.

Examples

IBM AppScan, Wallarm, and Pentoma Immuni-Web are some of the AI-driven automated tools, which operate efficiently to pinpoint the defects in applications.

IBM AppScan is an automated penetration testing tool combined with Artificial Intelligence. This tool works in-depth scanning and creates the report of potential vulnerabilities with a lot of noise and redundancy.

But instead of giving the noisy report immediately, it is being analyzed by the Intelligent Finding Analysis (IFA) and gives a refined report to the security analyst.

IFA is equipped with existing common vulnerabilities from various applications using a supervised Machine Learning model. IFA analyzes the conclusions in the below two cycles.

Cycle 1 – It utilizes pre-defined signatures and filters to set findings as not interesting (false-positive).

Cycle 2 – By utilizing supervised learning and training data set, IFA examines the responses and payloads used on every finding and determines the findings as Vulnerable, likely Vulnerable, and not Vulnerable.

IFA also gives a clear description of all the findings that are being incorporated and excluded in the final report dashboard.

Still, AI can only predict the flaw (It cannot confirm) as human intervention is needed to reduce the false positives and to train the algorithm more efficiently with refined result data sets forward with new attack patterns (update trained data set and re-start the server on each update so that the algorithm learns from the updated trained data set).

Unsupervised Learning

Unsupervised approaches are excellent for data exploration and use unlabeled data. They can be utilized to interpret a large amount of data by arranging the records together and extracting characteristics using clustering and association rules. These algorithms help at the cyber level to identify malwares and zero-day attacks as AI has the capability to inspect the data and extract characteristics at a deep level.

Examples: SPLUNK and IBM Qradar

These tools are integrated with semi-supervised ML models (provided with the labeled and a large amount of unlabeled data) and interpret all incoming data at the infrastructure level to identify malicious actions and generates alerts. These alerts are analyzed by security analysts to determine whether the alert is actionable or not.

Reinforcement Learning

Reinforcement Learning employs AI agents to take actions and improvise from each hacking attempt based on the feedback provided by the environment.

Example:

Deep Exploit and Deep-Pwning are some of the Artificial Intelligence-driven tools, which work efficiently to recognize security flaws at the infrastructure level.

These tools are integrated with Metasploit framework and utilize the reinforcement learning model to perform intelligence collection, threat modeling, vulnerability analysis, exploitation, and reporting.

Limitations

As Artificial Intelligence learns from experience, security experts are needed to assure that the algorithm is learned only in a defined and ethical form. Security experts require to train the algorithm with novel attack patterns to operate more efficiently.

All automated and intelligent tools are not effective enough to perform social engineering attacks, authorization checks, business logic checks, etc., which require to be performed manually to assure security at multiple levels.

Conclusion

Artificial Intelligence can identify many security flaws and escalate the matter to the attention of security analysts. AI can not only preserve significant amounts of time for security analysts but also recognize the vulnerabilities, which are not easily recognized by any manual techniques. However, it cannot completely substitute dedicated security professionals.

Due to the rise of new attacks, we always require security analysts to analyze the report generated by the tool and reduce false positives and train the machine accordingly. So, AI alone cannot fix the problems in information security. The machines require inputs from security analysts to get expertise and to learn from the analysis produced by them.

A completely secure system can be achieved only when the expertise from security professionals and Machine Learning available tools work together.

When it comes to QA, nothing is better than having the correct people in charge. That’s why we make sure that everyone in our team is qualified and accredited on some of the industry’s best practices.

At TestUnity we have an expert team of QA Engineers. This enables us to give our clients the support they require to make sure that their software hits the market in the right circumstances. Contact us for a free consultation and see for yourself why TestUnity’s QA approach is the best choice for your software.

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