Artificial intelligence (AI) has revolutionized the field of cybersecurity, providing advanced tools and techniques for detecting and preventing cyber-attacks. However, it has also become a double-edged sword, as cybercriminals are increasingly using AI to launch more sophisticated and targeted attacks.
One of the most significant ways that AI is wreaking havoc on cybersecurity is through the use of deepfakes. Deepfakes use AI to create highly realistic and convincing fake images, videos, and audio recordings that can be used to spread disinformation or impersonate someone else. This poses a significant threat to businesses, governments, and individuals, as deepfakes can be used to manipulate public opinion or conduct fraudulent activities.
Another way that AI is threatening cybersecurity is through the use of machine learning (ML) algorithms to automate attacks. Cybercriminals are increasingly using ML algorithms to analyze large amounts of data and identify vulnerabilities in systems. They can then use this information to launch highly targeted attacks that are difficult to detect and prevent.
For example, ML algorithms can be used to analyze network traffic patterns and identify abnormal behavior, such as a user logging in from an unusual location or a sudden increase in data transfer. Cybercriminals can use this information to launch targeted phishing attacks or distribute malware that is customized to bypass existing security measures.
In an article by ZDNet, they wrote, “Unrestrained by ethics or law, cybercriminals are racing to use AI to find innovative new hacks. Generative artificial intelligence is transforming cybersecurity, aiding both attackers and defenders. Cybercriminals are harnessing AI to launch sophisticated and novel attacks at large scale. And defenders are using the same technology to protect critical infrastructure, government organizations, and corporate networks.
Generative AI has helped bad actors innovate and develop new attack strategies, enabling them to stay one step ahead of cybersecurity defenses. AI helps cybercriminals automate attacks, scan attack surfaces, and generate content that resonates with various geographic regions and demographics, allowing them to target a broader range of potential victims across different countries. Cybercriminals adopted the technology to create convincing phishing emails. AI-generated text helps attackers produce highly personalized emails and text messages more likely to deceive targets.
Defenders are using AI to fend off attacks. Organizations are using the tech to prevent leaks and find network vulnerabilities proactively. It also dynamically automates tasks such as setting up alerts for specific keywords and detecting sensitive information online. Threat hunters are using AI to identify unusual patterns and summarize large amounts of data, connecting the dots across multiple sources of information and hidden patterns.
The work still requires human experts, but Christopher Ahlberg, CEO of threat intelligence platform Recorded Future, says the generative AI technology we’re seeing in projects like ChatGPT can help.
“We want to speed up the analysis cycle [to] help us analyze at the speed of thought,” he said. “That’s a very hard thing to do and I think we’re seeing a breakthrough here, which is pretty exciting.”
Ahlberg also discussed the potential threats that highly intelligent machines might bring. As the world becomes increasingly digital and interconnected, the ability to bend reality and shape perceptions could be exploited by malicious actors. These threats are not limited to nation-states, making the landscape even more complex and asymmetric.
AI has the potential to help protect against these emerging threats, but it also presents its own set of risks. For example, machines with high processing capabilities could hack systems faster and more effectively than humans. To counter these threats, we need to ensure that AI is used defensively and with a clear understanding of who is in control.
As AI becomes more integrated into society, it’s important for lawmakers, judges, and other decision-makers to understand the technology and its implications. Building strong alliances between technical experts and policymakers will be crucial in navigating the future of AI in threat hunting and beyond.
AI’s opportunities, challenges, and ethical considerations in cybersecurity are complex and evolving. Ensuring unbiased AI models and maintaining human involvement in decision-making will help manage ethical challenges. Vigilance, collaboration, and a clear understanding of the technology will be crucial in addressing the potential long-term threats of highly intelligent machines.”
AI Could Automate 25% of All Jobs
In a separate (disturbing) article, ZDNet wrote, “Artificial intelligence is all the buzz lately. Generative AI chatbots like ChatGPT can summarize scientific articles for you, debug your faulty code, and write Microsoft Excel formulas at your command. But have you considered how many jobs AI can replace?
According to the investment bank, about 300 million jobs could be lost to AI, signaling that the technology can and will upend work as we know it. Like past technological revolutions, AI can help companies decrease costs by automating specific processes, freeing companies to grow their businesses.
A global economics research report from Goldman Sachs says that AI could automate 25% of the entire labor market but can automate 46% of tasks in administrative jobs, 44% of legal jobs, and 37% of architecture and engineering professions. Of course, AI is the least threatening to labor-intensive careers like construction (6%), installation and repair (4%), and maintenance (1%).
The study also concludes that 18% of the global workforce could be automated with AI. And in countries like the U.S., U.K., Japan, and Hong Kong, upwards of 28% of the country’s workforce could be automated with AI.
However, the study shows potential for a balanced and mutually beneficial relationship between workers and AI. The study says that occupations that are partly exposed to automation will use their free time to increase their productivity at work.
But if you’re worried about your job being usurped by AI, Goldman Sachs anticipates that displaced workers will become reemployed in jobs that emerge as a direct result of widespread AI adoption. Displaced workers might also see higher levels of labor demand due to nondisplaced workers becoming more productive.
Think about how IT innovations created a demand for software developers and, with an increased income, directly increased the need for education, which created a demand for higher education professionals. It’s a domino effect, but an alarming one nonetheless.
AI’s potential to displace 300 million jobs is a primary concern for workers and tech moguls alike. Last week, notable names in the industry, like Steve Wozniak, Rachel Bronson, and Elon Musk, co-signed an open letter to pause AI experiments. The letter comes out of fear that AI development is moving too quickly for humans and can topple our society as we know it.
Last month, the U.S. Chamber of Commerce called for intense AI regulation at the federal level to ensure job, national, and economic security. Generative AI is arguably the most gaming-changing technology humans have created in a long time. And although impressive chatbots lack true intelligence, the technology is reshaping our world every day.
What is Generative AI?
ZDNet had this to say about the topic, “Generative AI is the process of AI algorithms generating or creating an output, such as text, photo, video, code, data, and 3D renderings, from data they are trained on.
The purpose of generative AI is to create content, as opposed to other forms of AI, which might be used for other purposes, such as analyzing data or helping to control a self-driving car.
The term generative AI is causing a buzz because of the increasing popularity of generative AI programs, such as OpenAI’s ChatGPT and DALL-E. The conversational chatbot and AI image generator both use generative AI to produce new content, including computer code, essays, emails, social media captions, images, poems, Excel formulas, and more within seconds, drawing in people’s attention.
ChatGPT has become extremely popular, accumulating more than one million users a week after launching. Many other companies have also rushed in to compete in the generative AI space, including Google, Microsoft’s Bing, and Opera. The buzz around generative AI is sure to keep on growing as more companies join in and find new use cases.
Machine learning refers to the subsection of AI that teaches a system to make a prediction based on the data it’s trained on. An example of this kind of prediction is when DALL-E is able to create an image based on the prompt you enter by discerning what the prompt actually means. Generative AI is, therefore, a machine-learning framework.
Recent text-based models, such as ChatGPT, are trained by being given massive amounts of text in a process known as self-supervised learning. In these cases, the model learns from the information it was fed to then make predictions and provide answers in the future.
One concern with generative AI models, especially those that generate text, is that they are trained on data from the entire internet. This includes copyrighted material and information that may not have been shared with the owner’s consent.
Some Shortcomings of AI
Generative AI models take a vast amount of content from across the internet and then use the information they are trained on to make predictions and create an output for the prompt you input. These predictions are based on the data, but there are no guarantees the prediction will be correct. The responses might also incorporate biases inherent in the content the model has ingested from the internet, but there is often no way of knowing that.
These models don’t necessarily know whether the things they produce are accurate, and we have little way of knowing where the information has come from and how it has been processed by the algorithms to generate content. There are plenty of examples of chatbots, for example, providing incorrect information or simply making things up to fill the gaps. While the results from generative AI can be intriguing and entertaining, it would be unwise, certainly in the short term, to rely on the information or content they create.
In conclusion, the use of artificial intelligence (AI) in cybersecurity has its benefits and drawbacks. While AI provides advanced tools for detecting and preventing cyber attacks, it also poses significant threats as cybercriminals continue to use AI to launch more sophisticated and targeted attacks, such as deepfakes and automated attacks using machine learning (ML) algorithms. The development of AI technology has also brought up concerns about job loss and potential negative impacts on society.
As AI continues to evolve and become more integrated into society, it is crucial to ensure that it is used ethically and with a clear understanding of its implications. Building strong alliances between technical experts and policymakers will be vital in navigating the future of AI in threat hunting and beyond. It is also essential to address potential long-term threats of highly intelligent machines and to ensure unbiased AI models while maintaining human involvement in decision-making.
While AI has its limitations, such as incorporating biases inherent in the content it has ingested from the internet, it also has significant potential for innovation and advancement in various fields. As with any technology, it is essential to use AI responsibly and with a clear understanding of its benefits and drawbacks. As AI technology continues to evolve, we must continue to explore ways to harness its potential while mitigating its potential risks.
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