Understanding AI Security Made Simple – Your Questions Answered

There has been a lot of AI hype in recent years, ranging from fantasies of self-driving automobiles to fears of AI bots that could end the planet. Our fantasies, nightmares, and fancies have all been hijacked by AI. The truth is that AI is now considerably less developed than we had projected it to be by this point. For instance, autonomous automobiles, which are frequently cited as exemplifying AI’s boundless potential, represent a specific use case and are not yet widely used in the transportation industry as a whole.

AI vs. ML

The phrases “AI” (Artificial Intelligence) and “ML” (Machine Learning) are often interchanged with one another. They refer to distinct fields of computer science. The goal of artificial intelligence (AI) is to construct intelligent systems, which require cognitive abilities as well as the ability to pass the Turing test. To put what it has learned into action, it takes that knowledge and takes it to the next level. The purpose of utilizing AI is to duplicate human actions, such as developing a cleaning robot that functions in a manner that is comparable to that of a human cleaner.

ML is a subfield of artificial intelligence (AI). It is composed of mathematical models, and its capabilities are derived from the integration of data and machinery. In order for ML to function, it must first learn lessons from occurrences and then prioritize those lessons. As a consequence of this, ML is able to carry out tasks that humans are incapable of, including sifting through large amounts of data, recognizing patterns, estimating probabilities, and more.

General AI vs. Narrow AI

The theoretical idea of general artificial intelligence is the one that seems to worry the majority of people the most since it is the quintessential example of “robot overlords” taking the place of humans. However, despite the fact that this concept is feasible from a practical standpoint, researchers are not yet at that stage in development.

Narrow AI, as opposed to General AI, is a particular kind of artificial intelligence that is customized for very particular tasks. This concentration makes it possible for people to be supported, relieving us of work that is either too taxing or has the potential to be detrimental. It is not meant to take our place in any way.

Narrow AI has already been applied across a wide variety of industries, such as in the production of automobiles and the packaging of boxes. Narrow AI can examine activity data and logs in the field of cybersecurity, looking for anomalies or other telltale signals of an impending assault.

Models of AI and ML

1- Generative AI

The cutting-edge discipline of artificial intelligence known as generative AI is defined by models, such as LLMs, that are trained on a body of information. The artificial intelligence technique known as generative has the capability to produce new content by basing it on the information found inside the corpus. The concept of generative artificial intelligence has been likened to “autocorrect” or “type-ahead,” but on steroids. Applications such as ChatGPT, Bing, Bard, and Dall-E are all examples of generative artificial intelligence, as are more specialized cyber assistants such as IBM Security QRadar Advisor with Watson and Microsoft Security CoPilot.

For use cases like ideation, assisted copyediting, and performing research against a reliable corpus, generative AI is ideally suited. Generative AI can be used for research by cybersecurity professionals, such as SOC and fusion teams, to better understand network topologies, zero-day vulnerabilities, and new indications of compromise (IoC). It’s critical to understand that generative AI might occasionally produce “hallucinations,” or incorrect answers.

Etay Maor, Senior Director of Security Strategy at Cato Networks, is quoted as saying “Generative AI can also help criminals.” One possible application of this would be the composition of phishing emails. Prior to the development of ChatGPT, one of the most fundamental methods for detecting phishing emails was the presence of misspellings and poor language. These were the signs that something was fishy about the situation. Now, thieves can effortlessly compose phishing emails in a number of languages while maintaining immaculate grammar.

2- Supervised Learning

During the supervised learning process, the data used for training is assigned labels with input/output pairs. The accuracy of the model is dependent on the quality of the labeling as well as the completeness of the dataset. In order to analyze the output, improve its quality, and rectify any bias drift that may have occurred, human intervention is frequently required. The ability to make accurate forecasts is best served by supervised learning.

In the field of cybersecurity, classification is accomplished through the use of supervised learning, which assists in the detection of phishing and malware. Additionally, it can be utilized for regression, which entails forecasting the cost of a new attack based on the expenses of previous incidents.

Supervised learning may not be the most suitable approach in scenarios when time constraints prohibit extensive training or when there is a lack of available resources to label or train the data. Moreover, it is not advisable in situations when there is a requirement to evaluate substantial volumes of data, insufficient data is available, or the ultimate objective is automated classification or clustering.

3- Unsupervised Learning

Unsupervised learning in machine learning refers to a situation in which neither the training data nor the outputs are labeled. This method enables computer programs to draw conclusions from data on their own, without the assistance of a human analyst. These conclusions can be used to identify patterns, clusters, and linkages. Unsupervised learning is frequently utilized for dynamic recommendation systems, such as those seen on e-commerce websites.

In cybersecurity, unsupervised learning can cluster or group malware and detect new patterns, such as identifying all malware from a single nation-state. It finds data set associations and linkages. For instance, if phishing email clickers reuse credentials. An anomaly detection use case could detect an attacker using stolen credentials.

Unsupervised learning is not always the best approach. It is recommended to choose a different strategy whenever there is a need for short training timeframes, whenever full transparency is necessary, whenever there is a high effect associated with getting the output wrong, and whenever there are severe repercussions associated with doing so.

4- Reinforcement Learning

Reinforcement learning, sometimes known as RL, is a novel approach to machine learning that falls somewhere in the middle of completely supervised and unsupervised learning. Retraining a model involves doing so when the previous training did not adequately prepare it for particular usage cases. Even with access to vast datasets, deep learning still has the potential to miss atypical use cases that can be addressed by RL. The very existence of RL is an implicit acknowledgment that models can have errors in them; this is an important distinction.

Is AI and ML Really Solving Today’s Problem?

The most crucial question is whether or not your problem is being solved by the AI and whether or not it is doing it effectively. If the artificial intelligence is not successful in resolving the issue and performing as it should, there is no use in paying a premium or incurring additional overhead costs.

AI has the potential to advance our capabilities and make us more productive, but it is not a panacea. Because of this, it is essential for organizations to make well-informed judgments regarding the AI technologies they decide to deploy inside their own operations.

Check Out: OpenAI Launches Business-Focused ChatGPT for Enhanced Interactions.