There is no doubt that generative artificial intelligence is one of the most revolutionary branches of artificial intelligence, capable of producing entirely new content across many different types of media, including text, image, audio, music, and even video. As opposed to conventional machine learning models, which are based on executing specific tasks, generative AI systems learn patterns and structures from large datasets and are able to produce outputs that aren't just original, but are sometimes extremely realistic as well.
It is because of this ability to simulate human-like creativity that generative AI has become an industry leader in technological innovation. Its applications go well beyond simple automation, touching almost every sector of the modern economy. As generative AI tools reshape content creation workflows, they produce compelling graphics and copy at scale in a way that transforms the way content is created.
The models are also helpful in software development when it comes to generating code snippets, streamlining testing, and accelerating prototyping. AI also has the potential to support scientific research by allowing the simulation of data, modelling complex scenarios, and supporting discoveries in a wide array of areas, such as biology and material science.
Generative AI, on the other hand, is unpredictable and adaptive, which means that organisations are able to explore new ideas and achieve efficiencies that traditional systems are unable to offer.
There is an increasing need for enterprises to understand the capabilities and the risks of this powerful technology as adoption accelerates.
Understanding these capabilities has become an essential part of staying competitive in a digital world that is rapidly changing. In addition to reproducing human voices and creating harmful software, generative artificial intelligence is rapidly lowering the barriers for launching highly sophisticated cyberattacks that can target humans.
There is a significant threat from the proliferation of deepfakes, which are realistic synthetic media that can be used to impersonate individuals in real time in convincing ways.
In a recent incident in Italy, cybercriminals manipulated and deceived the Defence Minister Guido Crosetto by leveraging advanced audio deepfake technology. These tools demonstrate the alarming ability of such tools for manipulating and deceiving the public.
Also, a finance professional recently transferred $25 million after being duped into transferring it by fraudsters using a deepfake simulation of the company's chief financial officer, which was sent to him via email.
Additionally, the increase in phishing and social engineering campaigns is concerning. As a result of the development of generative AI, adversaries have been able to craft highly personalised and context-aware messages that have significantly enhanced the quality and scale of these attacks.
It has now become possible for hackers to create phishing emails that are practically indistinguishable from legitimate correspondence through the analysis of publicly available data and the replication of authentic communication styles.
Cybercriminals are further able to weaponise these messages through automation, as this enables them to create and distribute a huge volume of tailored lures that are tailored to match the profile and behaviour of each target dynamically.
Using the power of AI to generate large language models (LLMs), attackers have also revolutionised malicious code development.
A large language model can provide attackers with the power to design ransomware, improve exploit techniques, and circumvent conventional security measures. Therefore, organisations across multiple industries have reported an increase in AI-assisted ransomware incidents, with over 58% of them stating that the increase has been significant.
It is because of this trend that security strategies must be adapted to address threats that are evolving at machine speed, making it crucial for organisations to strengthen their so-called “human firewalls”. While it has been demonstrated that employee awareness remains an essential defence, studies have indicated that only 24% of organisations have implemented continuous cyber awareness programs, which is a significant amount.
As companies become more sophisticated in their security efforts, they should update training initiatives to include practical advice on detecting hyper-personalised phishing attempts, detecting subtle signs of deepfake audio and identifying abnormal system behaviours that can bypass automated scanners in order to protect themselves from these types of attacks. Providing a complement to human vigilance, specialised counter-AI solutions are emerging to mitigate these risks.
In order to protect against AI-driven phishing campaigns, DuckDuckGoose Suite, for example, uses behavioural analytics and threat intelligence to prevent AI-based phishing campaigns from being initiated. Tessian, on the other hand, employs behavioural analytics and threat intelligence to detect synthetic media. As well as disrupting malicious activity in real time, these technologies also provide adaptive coaching to assist employees in developing stronger, instinctive security habits in the workplace.
Organisations that combine informed human oversight with intelligent defensive tools will have the capacity to build resilience against the expanding arsenal of AI-enabled cyber threats. Recent legal actions have underscored the complexity of balancing AI use with privacy requirements.
It was raised by OpenAI that when a judge ordered ChatGPT to keep all user interactions, including deleted chats, they might inadvertently violate their privacy commitments if they were forced to keep data that should have been wiped out.
AI companies face many challenges when delivering enterprise services, and this dilemma highlights the challenges that these companies face.
OpenAI and Anthropic are platforms offering APIs and enterprise products that often include privacy safeguards; however, individuals using their personal accounts are exposed to significant risks when handling sensitive information that is about them or their business.
AI accounts should be managed by the company, users should understand the specific privacy policies of these tools, and they should not upload proprietary or confidential materials unless specifically authorised by the company. Another critical concern is the phenomenon of AI hallucinations that have occurred in recent years.
This is because large language models are constructed to predict language patterns rather than verify facts, which can result in persuasively presented, but entirely fictitious content.
As a result of this, there have been several high-profile incidents that have resulted, including fabricated legal citations in court filings, as well as invented bibliographies. It is therefore imperative that human review remains part of professional workflows when incorporating AI-generated outputs.
Bias is another persistent vulnerability.
Due to the fact that artificial intelligence models are trained on extensive and imperfect datasets, these models can serve to mirror and even amplify the prejudices that exist within society as a whole. As a result of the system prompts that are used to prevent offensive outputs, there is an increased risk of introducing new biases, and system prompt adjustments have resulted in unpredictable and problematic responses, complicating efforts to maintain a neutral environment.
Several cybersecurity threats, including prompt injection and data poisoning, are also on the rise. A malicious actor may use hidden commands or false data to manipulate model behaviour, thus causing outputs that are inaccurate, offensive, or harmful. Additionally, user error remains an important factor as well. Instances such as unintentionally sharing private AI chats or recording confidential conversations illustrate just how easy it is to breach confidentiality, even with simple mistakes.
It has also been widely reported that intellectual property concerns complicate the landscape. Many of the generative tools have been trained on copyrighted material, which has raised legal questions regarding how to use such outputs. Before deploying AI-generated content commercially, companies should seek legal advice.
As AI systems develop, even their creators are not always able to predict the behaviour of these systems, leaving organisations with a challenging landscape where threats continue to emerge in unexpected ways. However, the most challenging risk is the unknown. The government is facing increasing pressure to establish clear rules and safeguards as artificial intelligence moves from the laboratory to virtually every corner of the economy at a rapid pace.
Before the 2025 change in administration, there was a growing momentum behind early regulatory efforts in the United States. For instance, Executive Order 14110 outlined the appointment of chief AI officers by federal agencies and the development of uniform guidelines for assessing and managing AI risks. As a result of this initiative, a baseline of accountability for AI usage in the public sector was established.
A change in strategy has taken place in the administration's approach to artificial intelligence since they rescinded the order. This signalled a departure from proactive federal oversight. The future outlook for artificial intelligence regulation in the United States is highly uncertain, however. The Trump-backed One Big Beautiful Bill proposes sweeping restrictions that would prevent state governments from enacting artificial intelligence regulations for at least the next decade.
As a result of this measure becoming law, it could effectively halt local and regional governance at a time when AI is gaining a greater influence across practically all industries. Meanwhile, the European Union currently seems to be pursuing a more consistent approach to AI.
As of March 2024, a comprehensive framework titled the Artificial Intelligence Act was established. This framework categorises artificial intelligence applications according to the level of risk they pose and imposes strict requirements for applications that pose a significant risk, such as those in the healthcare field, education, and law enforcement.
Also included in the legislation are certain practices, such as the use of facial recognition systems in public places, that are outright banned, reflecting a commitment to protecting the individual's rights. In terms of how AI oversight is defined and enforced, there is a widening gap between regions as a result of these different regulatory strategies.
Technology will continue to evolve, and to ensure compliance and manage emerging risks effectively, organisations will have to remain vigilant and adapt to the changing legal landscape as a result of this.
