generative ai courses 1

Generative AI and LLMs: The ultimate weapon against evolving cyber threats Technology

Generative AI to Combat Cyber Security Threats

As AI becomes increasingly integrated into legal practice, understanding and following relevant guidelines is crucial. By staying informed and implementing appropriate safeguards, legal professionals can leverage AI tools effectively while maintaining their professional obligations and protecting client interests. Regarding billing practices, Opinion 512 introduces an interesting intersection between cost efficiency and technological competence. This parallels how electronic legal research and e-discovery tools have become standard expectations for competent representation. The Opinion anticipates that as GAI tools become more established in legal practice, their use might become necessary for certain tasks to meet professional standards of competence and efficiency.

This feature allows the AI to operate semi-autonomously, performing scheduled actions and maintaining ongoing responsibilities without constant user prompting. While still in its early stages, it points to a future where AI systems combine the creative and analytical capabilities of generative AI with the autonomous decision-making of agentic AI. I put the word “know” in quotes to emphasize that today’s AI is not sentient and doesn’t know things in the same manner that humans do. AI is essentially software running on computer hardware and generative AI is specifically all about pattern-matching computationally on human writing such as essays, poems, stories, and the like. If you’ve used ChatGPT or any of the popular LLMs then you are familiar with how amazingly fluent generative AI seems to be, though realize that the computational and mathematical effort is a form of mimicry regarding human natural language.

This evolution could lead to unprecedented levels of human-machine synergy, where AI becomes less of a tool and more of a partner in problem-solving and innovation. The practical applications of agentic AI are potentially far-reaching and transformative. Imagine an AI system that doesn’t just help schedule your meetings but actively manages your entire workflow, anticipating bottlenecks, suggesting process improvements, and autonomously handling routine tasks without constant supervision. In manufacturing, agentic AI could manage entire production lines, not just by following pre-programmed routines but by actively optimizing processes and responding to unexpected challenges in real time. From OpenAI’s ChatGPT to Google’s Gemini and Anthropic’s Claude, artificial intelligence is increasingly changing the ways in which businesses operate. As shown in the response by ChatGPT, the method of figuring out what would happen once the rubber ball was let go consists of using the data training that the AI underwent when first being set up.

“My best business intelligence, in one easy email…”

The dual nature of generative AI in cybersecurity underscores the need for careful implementation and regulation to harness its benefits while mitigating potential drawbacks[4] [5]. In the realm of threat detection, generative AI models are capable of identifying patterns indicative of cyber threats such as malware, ransomware, or unusual network traffic, which might otherwise evade traditional detection systems [3]. By continuously learning from data, these models adapt to new and evolving threats, ensuring detection mechanisms are steps ahead of potential attackers. This proactive approach not only mitigates the risks of breaches but also minimizes their impact. For security event and incident management (SIEM), generative AI enhances data analysis and anomaly detection by learning from historical security data and establishing a baseline of normal network behavior [3]. Moreover, generative AI’s ability to simulate various scenarios is critical in developing robust defenses against both known and emerging threats.

Think of generative AI as a highly skilled assistant waiting for instructions, while agentic AI is more like a colleague who can take the initiative and work independently toward broader objectives. Hark back to my dialogue with ChatGPT about how the AI figured out that a rubber ball would drop to the ground and bounce. The AI told the truth, namely that it was speculation based on text-based pattern-matching of content that ChatGPT had been initially data trained on. A heated debate in the AI community entails whether we truly need to have AI essentially “embody” physicality considerations such as my point earlier of generative AI by words versus generative AI by deeds.

• Hark back to my dialogue with ChatGPT about how the AI figured out that a rubber ball would drop to the ground and bounce.
• They can break down complex tasks into manageable steps, prioritize actions, and even recognize when their current approach isn’t working and needs adjustment.
• You see, since the days of being a baby and a toddler, you eventually figured out that dropping a rubber ball from shoulder height will fall to the ground and then bounce.
• Whether analyzing network traffic for anomalies or identifying phishing attempts through advanced natural language processing (NLP), LLMs have proven to be invaluable tools.

The integration of artificial intelligence (“AI”) into legal practice is no longer a future prospect. As law firms and legal departments begin to adopt AI tools to enhance efficiency and service delivery, the legal profession faces a critical moment that demands both innovation and careful consideration. Recent months have brought landmark guidance from major institutions, offering crucial frameworks for how legal professionals can ethically and effectively incorporate AI into their practice while maintaining their professional obligations. Large Language Models, including prominent examples like GPT-4, Falcon2, and BERT, have brought groundbreaking capabilities to cybersecurity. Their ability to parse and contextualize massive amounts of data in real time allows organizations to detect and counteract a wide range of cyber threats.

Enhancing Intrusion Detection Systems

Only when you suddenly slip on a sheet of ice or haphazardly trip on a banana peel do you find yourself jarred into the realization that you are immersed in a physical world that requires constant vigilance on how to move and exist in physical space. Later in life, during school and intense classes in physics and math, they will get a grand revealing of the explicitly calculated secrets underlying the physical world. Until then, it is mainly a sense of embodied presence in the real world, entailing physicality and nature’s intrinsic bodily dynamics, along with a spoonful of mindful geospatial mapping. Learn how Hewlett Packard Enterprise helps public sector entities transform how they deliver services to staff and citizens. Generative AI is already improving federal agency operations by streamlining processes, enhancing decision-making and improving service delivery. However, experts say success hinges on robust policies, targeted pilot programs and modernized infrastructure.

The AI maker of ChatGPT, OpenAI, had scanned the Internet widely and used the various data on the Internet to establish patterns of how people write and describe things. In there, certainly, there would be plenty of content about physics and how physical objects in the real world move and act. However, the complaint did not state that the plaintiffs have evidence of the shared InMail contents. The study evaluated the performance of 42 LLMs across various cybersecurity tasks, offering valuable insights into their strengths and limitations.

Moreover, generative AI technologies can be exploited by cybercriminals to create sophisticated threats, such as malware and phishing scams, at an unprecedented scale[4]. The same capabilities that enhance threat detection can be reversed by adversaries to identify and exploit vulnerabilities in security systems [3]. As these AI models become more sophisticated, the potential for misuse by malicious actors increases, further complicating the security landscape. Despite its potential, the use of generative AI in cybersecurity is not without challenges and controversies. A significant concern is the dual-use nature of this technology, as cybercriminals can exploit it to develop sophisticated threats, such as phishing scams and deepfakes, thereby amplifying the threat landscape.

PUBLISH YOUR CONTENT

The American Bar Association’s (“ABA”) Formal Opinion 512 (“Opinion”) provides comprehensive guidance on attorneys’ ethical obligations when using generative AI (GAI) tools in their practice. While GAI tools can enhance efficiency and quality of legal services, the Opinion emphasizes they cannot replace the attorney’s professional judgment and experience necessary for competent client representation. They can break down complex tasks into manageable steps, prioritize actions, and even recognize when their current approach isn’t working and needs adjustment. While generative AI tops the list of fastest-growing skills, cybersecurity and risk management are also surging in importance. Six of the top ten fastest-growing tech skills are cybersecurity-related, reflecting a business landscape where so many organizations have experienced identity-related breaches in the past year.

Additionally, generative AI systems may occasionally produce inaccurate or misleading information, known as hallucinations, which can undermine the reliability of AI-driven security measures. Furthermore, ethical and legal issues, including data privacy and intellectual property rights, remain pressing challenges that require ongoing attention and robust governance [3][4]. Efforts to strengthen models against adversarial attacks and refine their real-time application capabilities are critical for enhancing resilience. Finally, fostering collaboration between AI researchers and cybersecurity professionals will drive innovation and ensure that LLMs are effectively deployed to counter evolving cyber threats. In a novel approach to cyber threat-hunting, the combination of generative adversarial networks and Transformer-based models is used to identify and avert attacks in real time.

Before we dive into the AI considerations, I’d like to offer crucial insights regarding the physical world and how humans manage to operate within it. This analysis of an innovative AI breakthrough is part of my ongoing Forbes column coverage on the latest in AI including identifying and explaining various impactful AI complexities (see the link here). For my coverage of how generative AI such as ChatGPT, Claude, Llama, Gemini, and other major AI is increasingly being connected to robotic arms and akin robotic capacities, see the link here. The gist is that AI is becoming further data-trained on how to cope with the physical world, the real world in which we all live. In today’s column, I identify and explore a hot trend in the AI field that is variously referred to as Physical AI sometimes also known as Generative Physical AI (a mash-up of generative AI and a said-to-be additional physical AI capability). When AI-generated content competes with human creators, courts are unlikely to view its use of copyrighted material as fair.

How generative AI is paving the way for transformative federal operations

Fair use relies heavily on whether the use is transformative, meaning it adds new meaning, value or purpose to the original work. While human creativity often achieves this through intentionality—commentary, critique or parody—AI outputs rarely meet this standard. Amid the rush to devise and field Physical AI, it is incumbent upon AI makers and AI developers to keep in mind those wise words about what can happen once AI is operating in the physical world. It is one thing for someone to use generative AI that merely tells them to do something untoward, which is just all talk, while AI that instructs a robot to do something troublesome is going to be carried out in the physical world. Research on the enthralling topic of “embodiment intelligence” encompassing both humankind and artificial or AI kind is wrestling mightily with these provocative and unresolved questions. The generative AI uses ostensibly book learning to guess what will happen when a robot is instructed by the AI to lift a chair or hold aloft a dog.

This methodology is particularly effective in intrusion detection systems (IDS), especially in the rapidly growing IoT landscape, where efficient mitigation of cyber threats is crucial[8]. While generative AI offers robust tools for cyber defense, it also presents new challenges as cybercriminals exploit these technologies for malicious purposes. For instance, adversaries use generative AI to create sophisticated threats at scale, identify vulnerabilities, and bypass security protocols. Notably, social engineers employ generative AI to craft convincing phishing scams and deepfakes, thus amplifying the threat landscape[4].

While the technology holds immense potential, its current reliance on copyrighted works without permission makes fair use a weak defense. While fair use—a legal framework allowing limited use of copyrighted material without permission—has long been a pillar of creativity and innovation, applying it to generative AI is fraught with legal and ethical challenges. Coursera’s data reveals an interesting geographical spread of AI learning, with India leading the charge, followed by the US, Canada, and the UK. What’s particularly noteworthy is that more than half of all generative AI course enrollments now come from learners in India, Colombia, and Mexico.

Generative AI is revolutionizing the field of cybersecurity by providing advanced tools for threat detection, analysis, and response, thus significantly enhancing the ability of organizations to safeguard their digital assets. This technology allows for the automation of routine security tasks, facilitating a more proactive approach to threat management and allowing security professionals to focus on complex challenges. The adaptability and learning capabilities of generative AI make it a valuable asset in the dynamic and ever-evolving cybersecurity landscape [1][2]. The future of generative AI in combating cybersecurity threats looks promising due to its potential to revolutionize threat detection and response mechanisms. As organizations continue to leverage deep learning models, generative AI is expected to enhance the simulation of advanced attack scenarios, which is crucial for testing and fortifying security systems against both known and emerging threats [3]. This technology not only aids in identifying and neutralizing cyber threats more efficiently but also automates routine security tasks, allowing cybersecurity professionals to concentrate on more complex challenges [3].

This capability is critical, given the sophisticated nature of threats posed by malicious actors who use AI with increasing speed and scale[4]. In areas of particular interest to legal practitioners, the Report offers substantive analysis of data privacy and intellectual property concerns. On data privacy, the Task Force emphasized that AI systems’ growing data requirements are creating unprecedented privacy challenges, particularly regarding the collection and use of personal information. The intellectual property section addresses emerging questions about AI-generated works, training data usage, and copyright protection, with specific recommendations for adapting existing IP frameworks to address AI innovations.

In a new video interview for FedScoop, Department of Homeland Security Deputy CTO for AI & Emerging Technology Chris Kraft shared insights into DHS’s pioneering efforts with generative AI. “A lot of the work we’re doing now stems from our AI Task Force established in early 2023,” says Kraft. This task force laid the groundwork for initiatives like DHSChat, an internal AI tool supporting nearly DHS 19,000 employees, and three generative AI pilot programs. Filed in California’s federal court on Alessandro De La Torre’s behalf, the lawsuit alleged that InMail messages were fed to neural networks based on LinkedIn’s disclosure last year. The class-action lawsuit also claimed that LinkedIn concealed critical facts and attempted to cover its tracks after violating users’ privacy rights. These systems can set their own goals, develop strategies to achieve them and adapt their approach based on changing circumstances.

The concept of utilizing artificial intelligence in cybersecurity has evolved significantly over the years. One of the earliest types of neural networks, the perceptron, was created by Frank Rosenblatt in 1958, setting the stage for the development of more advanced AI systems like feedforward neural networks or multi-layer perceptrons (MLPs)[1]. With the advent of generative AI, the landscape of cybersecurity has transformed dramatically. Generative AI, particularly models such as ChatGPT that use large-scale language models (LLM), has introduced a new dimension to cybersecurity due to its high degree of versatility and potential impact across the cybersecurity field[2]. This technology has brought both opportunities and challenges, as it enhances the ability to detect and neutralize cyber threats while also posing risks if exploited by cybercriminals [3].

7 Free Generative AI Courses For 2025 – Forbes

7 Free Generative AI Courses For 2025.

Posted: Mon, 02 Dec 2024 08:00:00 GMT [source]

The key distinction between generative and agentic AI lies in their approach to tasks and decision-making. Generative AI, which powers popular tools like ChatGPT, Google Gemini and Claude, works like an incredibly sophisticated pattern-matching and completion system. When you prompt it, it analyzes vast amounts of training data to generate appropriate responses, whether that’s writing a poem, creating an image, or helping debug code.

EWeek stays on the cutting edge of technology news and IT trends through interviews and expert analysis. Gain insight from top innovators and thought leaders in the fields of IT, business, enterprise software, startups, and more. Even if some uses of generative AI were deemed legal under fair use, ethical concerns remain. Should creators have the right to opt out of having their works used in AI training datasets? These questions highlight the broader moral implications of AI’s reliance on copyrighted material. In the realm of cyber forensics, LLMs assist investigators by analyzing logs, system data, and communications to trace the origin and nature of attacks.