Introduction

As generative artificial intelligence (GenAI) continues to evolve at lightning speed, usage by businesses will become increasingly widespread.

Its uses will include automating customer service through chatbots, generating marketing content, and analysing large datasets to produce valuable insights that drive business decisions.

However, these applications, while great at streamlining processes and enhancing efficiency, also introduce risks if relied upon without proper human oversight.

As AI usage continues to grow it’s important that consideration is given to how best to achieve a proper balance between maximising the technological innovations associated with the technology, and inadvertently developing an overreliance on it. The latter can have some broader and deeply concerning societal impacts.

It’s A Matter of Trust

Humans have a natural tendency to trust information when it is presented with confidence. However, caution and verification are necessary before trusting information that comes from sophisticated AI systems.

One of the most significant challenges facing generative AI (GenAI) is the tendency of large language models (LLMs) to hallucinate. These models are trained on vast amounts of data from the internet, enabling them to understand and generate human language.

However, the quality of this data can be variable, leading to the creation of misleading or illogical information. When presented with confidence, these hallucinations can be difficult to distinguish from factual statements. This has already resulted in instances of misplaced trust and, in some cases, dangerous consequences.

While hallucinations represent errors from AI systems, there’s an equally concerning issue related to AI’s deliberate use to manipulate information, also known as deepfakes. Deepfakes and voice cloning technologies have already been weaponised to mimic political candidates, manipulate public opinion, and sow discord.

Whether it’s the unintentional hallucinations of LLMs or the intentional deception through deepfakes, the broader implication is clear: as AI technology advances, so too must our mechanisms for ensuring trust and safeguarding against both accidental and malicious misuse.

The Security Challenge of AI Usage

To ensure businesses, governments and healthcare systems understand the caution needed when integrating AI, they must understand the necessity of maintaining human oversight as part of the process. Security risks for businesses leveraging GenAI add an extra layer of consequences to overreliance, including:

• Data breaches: AI tools often handle vast amounts of sensitive information, ranging from personal customer data to proprietary business information. This makes them prime targets for cyberattacks and, if a breach occurs, the consequences can be severe.
• Credential Stuffing: This is popular among attackers as it takes little effort for a potentially big payoff.  Cybercriminals usually gain access to stolen login credentials by purchasing a list from the dark web.  They then use the stolen information for illicit activities, including account takeovers, phishing, spam and crypto mining.
• Biases and fairness challenges: AI systems learn from the data it is trained on, and if this data contains biases, the AI can perpetuate or even amplify these biases. This can create a continuous loop of misinformation that can be extremely harmful if perceived as truth.
• Vulnerabilities: AI systems, like any other software, can contain errors or weaknesses that malicious actors might easily exploit. These vulnerabilities can lead to incorrect or malicious outputs, thus manipulating decision-making processes, spreading misinformation, or generally disrupting business operations.

These risks must be carefully managed to ensure the safe and ethical use of AI technologies. Addressing these issues is crucial to harnessing the full potential of AI while safeguarding sensitive information, promoting fairness, and ensuring system integrity.

Overcoming AI-related Security Challenges

As AI technologies continue to evolve, security teams must approach AI-specific risks with a multifaceted approach. The risks associated with overreliance on AI can be addressed through the following security tactics and methods:

• A strategy of knowledge management: To maximise the effectiveness of AI while minimising risks, businesses should focus on knowledge management strategies that customise AI systems to their specific problem domains. This can be achieved by employing Retrieval-Augmented Generation (RAG) to integrate domain-specific knowledge bases or by fine-tuning models to align with organisational needs.
• UEBA Detection: User entity and behaviour analytics (UEBA) can identity a legitimate user account exhibiting anomalous behaviour by using behavioural profiling and analysis to provide insights.  It can also view multiple systems as a whole and identify the anomalous activity as it moves laterally across the network.  Overall, it improves the speed of threat detection and response, making cybersecurity more effective and efficient in a rapidly evolving threat landscape.
• 24/7 monitoring: To detect and respond to threats as they happen, security teams should prioritise continuous, real-time monitoring. Additionally, employing bias detection and mitigation techniques can ensure fairness and reliability in AI results.
• Staff training: Awareness of the limitations and best practices for AI use is crucial. This includes training users to cross-verify AI outputs while remaining sceptical of overly confident responses.

By utilising a cautious and innovative security plan, businesses can maximise the potential of automated technology without jeopardising sensitive information or negatively impacting business operations.

Maintaining Security As Usage of AI Rapidly Grows

The threats posed by AI are distinct in many ways from those that target user identity, software code, or business data. While traditional cybersecurity risks often focus on protecting specific assets, AI introduces new challenges – such as hallucinations, deepfakes, and ethical concerns – that can impact decision-making and public trust.

The key for many businesses is remaining proactive, leveraging AI for innovation while safeguarding against potential risks. Addressing the risks of overreliance on hallucination-prone LLM and AI technologies requires a comprehensive, multi-faceted approach. This challenge is best met through technological advancements, active user involvement, transparent communication, and thorough user education.

In differentiating between overreliance on AI and its use for innovation, it’s important to collectively commit to fostering an ongoing dialogue about AI strategies and continuously adapt to new challenges.

By doing this, organisations can enjoy the massive benefits offered by the technology while at the same time keeping any associated risks to a minimum.