Insight AI technology is being explored by businesses as a tool for balancing the benefits it brings with the risks that are associated. Amidst this backdrop, NetSkope Threat Labs has recently released the latest edition of its Cloud and Threat Report, which focuses on using AI apps within the enterprise to prevent fraud and other unauthorized activity. There is a lot of risk associated with the use of AI applications in the enterprise, including an increased attack surface, which was already discussed in a serious report, and the accidental sharing of sensitive information that occurs when using AI apps. 

As users and particularly as individuals working in the cybersecurity as well as privacy sectors, it is our responsibility to protect data in an age when artificial intelligence has become a popular tool. An artificial intelligence system, or AI system, is a machine-controlled program that is programmed to think and learn the same way humans do through the use of simulation. 

AI systems come in various forms, each designed to perform specialized tasks using advanced computational techniques: - Generative Models: These AI systems learn patterns from large datasets to generate new content, whether it be text, images, or audio. A notable example is ChatGPT, which creates human-like responses and creative content. - Machine Learning Algorithms: Focused on learning from data, these models continuously improve their performance and automate tasks. Amazon Alexa, for instance, leverages machine learning to enhance voice recognition and provide smarter responses. - Robotic Vision: In robotics, AI is used to interpret and interact with the physical environment. Self-driving cars like those from Tesla use advanced robotics to perceive their surroundings and make real-time driving decisions. - Personalization Engines: These systems curate content based on user behavior and preferences, tailoring experiences to individual needs.  Instagram Ads, for example, analyze user activity to display highly relevant ads and recommendations. These examples highlight the diverse applications of AI across different industries and everyday technologies. 

In many cases, artificial intelligence (AI) chatbots are good at what they do, but they have problems detecting the difference between legitimate commands from their users and manipulation requests from outside sources. 

In a cybersecurity report published on Wednesday, researchers assert that artificial intelligence has a definite Achilles' heel that should be exploited by attackers shortly. There have been a great number of public chatbots powered by large language models, or LLMs for short, that have been emerging just over the last year, and this field of LLM cybersecurity is at its infancy stage. However, researchers have already found that these models may be susceptible to a specific form of attack referred to as "prompt injection," which occurs when a bad actor sneakily provides commands to the model without the model's knowledge. 

In some instances, attackers hide prompts inside webpages that the chatbot reads later, so that the chatbot might download malware, assist with financial fraud, or repeat dangerous misinformation that is passed on to people by the chatbot. 

AI (artificial intelligence) is one of the most important areas of study in technology today. AI focuses on developing systems that mimic human intelligence, with the ability to learn, reason, and solve problems autonomously. The two basic types of AI models that can be used for analyzing data are predictive AI models and generative AI models. 

 A predictive artificial intelligence function is a computational capability that uses existing data to make predictions about future outcomes or behaviours based on historical patterns and data. A creative AI system, however, has the capability of creating new data or content that is similar to the input it has been trained on, even if there was no content set in the dataset before it was trained. 

 A philosophical discord exists between Leibnitz and the founding fathers of artificial intelligence in the early 1800s, although the conception of the term "artificial intelligence" as we use it today has existed since the early 1940s, and became famous with the development of the "Turing test" in 1950. It has been quite some time since we have experienced a rapid period of progress in the field of artificial intelligence, a trend that has been influenced by three major factors: better algorithms, increased networked computing power, and a greater capacity to capture and store data in unprecedented quantities. 

Aside from technological advancements, the very way we think about intelligent machines has changed dramatically since the 1960s. This has resulted in a great number of developments that are taking place today. Even though most people are not aware of it, AI technologies are already being utilized in very practical ways in our everyday lives, even though they may not be aware of it. As a characteristic of AI, after it becomes effective, it stops being referred to as AI and becomes mainstream computing as a result.2 For instance, there are several mainstream AI technologies on which you can take advantage, including having the option of being greeted by an automated voice when you call, or being suggested a movie based on your preferences. The fact that these systems have become a part of our lives, and we are surrounded by them every day, is often overlooked, even though they are supported by a variety of AI techniques, including speech recognition, natural language processing, and predictive analytics that make their work possible. 

There is a great deal of hype surrounding artificial intelligence and there is a lot of interest in the media regarding it, so it is not surprising to find that there are an increasing number of users accessing AI apps in the enterprise. The rapid adoption of artificial intelligence (AI) applications in the enterprise landscape is significantly raising concerns about the risk of unintentional exposure to internal information. A recent study reveals that, between May and June 2023, there was a weekly increase of 2.4% in the number of enterprise users accessing at least one AI application daily, culminating in an overall growth of 22.5% over the observed period. Among enterprise AI tools, ChatGPT has emerged as the most widely used, with daily active users surpassing those of any other AI application by a factor of more than eight. 

In organizations with a workforce exceeding 1,000 employees, an average of three different AI applications are utilized daily, while organizations with more than 10,000 employees engage with an average of five different AI tools each day. Notably, one out of every 100 enterprise users interacts with an AI application daily. The rapid increase in the adoption of AI technologies is driven largely by the potential benefits these tools can bring to organizations. Enterprises are recognizing the value of AI applications in enhancing productivity and providing a competitive edge. Tools like ChatGPT are being deployed for a variety of tasks, including reviewing source code to identify security vulnerabilities, assisting in the editing and refinement of written content, and facilitating more informed, data-driven decision-making processes. 

However, the unprecedented speed at which generative AI applications are being developed and deployed presents a significant challenge. The rapid rollout of these technologies has the potential to lead to the emergence of inadequately developed AI applications that may appear to be fully functional products or services. In reality, some of these applications may be created within a very short time frame, possibly within a single afternoon, often without sufficient oversight or attention to critical factors such as user privacy and data security. 

The hurried development of AI tools raises the risk that confidential or sensitive information entered into these applications could be exposed to vulnerabilities or security breaches. Consequently, organizations must exercise caution and implement stringent security measures to mitigate the potential risks associated with the accelerated deployment of generative AI technologies. 

Methods of Data Collection 

AI tools generally employ one of two methods to collect data: Data collection is very common in this new tech-era. This is when the AI system is programmed to collect specific data. Examples include online forms, surveys, and cookies on websites that gather information directly from users. 

Another comes Indirect collection, this involves collecting data through various platforms and services. For instance, social media platforms might collect data on users' likes, shares, and comments, or a fitness app might gather data on users' physical activity levels. 

As technology continues to undergo ever-increasing waves of transformation, security, and IT leaders will have to constantly seek a balance between the need to keep up with technology and the need for robust security. Whenever enterprises integrate artificial intelligence into their business, key considerations must be taken into account so that IT teams can achieve maximum results. 

As a fundamental aspect of any IT governance program, it is most important to determine what applications are permissible, in conjunction with implementing controls that not only empower users but also protect the organization from potential risks. Keeping an environment in a secure state requires organizations to monitor AI app usage, trends, behaviours, and the sensitivity of data regularly to detect emerging risks as soon as they emerge.

A second effective way of protecting your company is to block access to non-essential or high-risk applications. Further, policies that are designed to prevent data loss should be implemented to detect sensitive information, such as source code, passwords, intellectual property, or regulated data, so that DLP policies can be implemented. A real-time coaching feature that integrates with the DLP system reinforces the company's policies regarding how AI apps are used, ensuring users' compliance at all times. 

A security plan must be integrated across the organization, sharing intelligence to streamline security operations and work in harmony for a seamless security program. Businesses must adhere to these core cloud security principles to be confident in their experiments with AI applications, knowing that their proprietary corporate data will remain secure throughout the experiment. As a consequence of this approach, sensitive information is not only protected but also allows companies to explore innovative applications of AI that are beyond the realm of mainstream tasks such as the creation of texts or images.