How to Use Generative AI Safely in Your Business

August 23, 2023

5 min read

Alexandra Khomenok

Generative AI Risks for Business


Generative AI has opened up a world of business possibilities, promising innovation and efficiency across various sectors. However, businesses must also remain vigilant about the potential risks it introduces. 


From data biases to security vulnerabilities, the landscape of AI presents its challenges. This article delves into the potential Generative AI risks in businesses and explores practical mitigation strategies.


1. The biased landscape: poisoning the training dataset, vocabulary limitations, and biases

Generative AI systems, including the fascinating Large Language Models (LLMs), heavily rely on their training data. Their performance is directly influenced by the quality and composition of the text, images, and other datasets used for training.


Training datasets sourced from the internet can inadvertently carry the biases and prejudices present in the data sources. Consequently, these biases can seep into the AI’s responses, resulting in skewed or discriminatory outcomes. 


Gender and race biases, in particular, can emerge due to skewed data representation. For instance, datasets might inadvertently reflect developers’ biases, a majority of whom are male. Statistics from the Alan Turing Institute indicate that only 22% of AI and data science experts are women.


However, biases extend beyond gender. Racial biases also play a significant role. The Gender Shades initiative, for example, exposed how datasets predominantly featuring white and male faces led to notably reduced facial recognition accuracy for women, particularly from diverse backgrounds.


Moreover, concerns arise when AI tools are involved in recruitment, posing a risk of biased outcomes in AI-driven decisions.


Mitigation strategies:

In-context learning

Real-time provision of positive examples allows organisations to guide the AI’s behaviour and rectify errors as they arise.

Diverse training datasets

The inclusion of diverse perspectives in training datasets counteracts biases.

Routine bias detection and addressing

Regularly monitoring and updating training data are essential to enhance fairness by addressing biases.


2. Data leakage and privacy risks

Privacy risks associated with Generative AI constitute a significant concern for businesses. When AI systems gain access to sensitive information, there’s an inherent risk of unintentional data leakage.


GPT-4’s exclusive cloud-based deployment may raise concerns for some organisations. Specific sectors like finance, banking, insurance, and healthcare might prioritise on-premise deployment to maintain stricter control over data security.


Tovie AI offers the LLMs’ on-premise solution that allows you to maintain complete control over how your data is used.


Mitigation strategies:

On-premise deployment

Stricter security requirements can be met through on-premise deployment, offering enhanced data control.

– Access control measures

Adherence to access control policies ensures that AI-generated responses protect sensitive data.

Data masking and anonymisation

Techniques such as data masking and anonymisation prevent the exposure of sensitive information.


3. Hallucinations and misinformation

Generative AI algorithms occasionally produce “hallucinations” when confronted with insufficient information, leading to the generation of false or misleading content.


Several prompt techniques help mitigate this problem and get more accurate results. Check out the blog post with tips to avoid model hallucinations. 


The proliferation of deepfake technology exacerbates misinformation as AI-generated content becomes more convincing and challenging to distinguish from reality. Many commentators have pointed to Generative AI as a tool that could be used for fraud and abuse at scale. 


Mitigation strategies:

Grounded responses

Providing specific context and source material ensures accurate and well-grounded AI responses.

Source citation

Encouraging AI to provide quotations enhances transparency and credibility.


4. Malicious code

Generative AI’s capabilities extend beyond text generation to code creation, introducing the risk of insecure or malicious code generation.


A user study by Cornell University found that participants with access to an AI code assistant wrote code with significantly more security vulnerabilities than those who did not have access to the AI. The researchers believe that AI code assistants can give users a false sense of security. When users rely too heavily on the AI, they may be less likely to carefully review the code that the AI generates, which can lead to errors and vulnerabilities.


The study’s findings have implications for developing and using AI code assistants. Developers of AI code assistants need to be aware of the potential for these tools to lead to less secure code.


Mitigation strategies:

– Regular dataset review

Consistent review of training datasets identifies and eliminates malicious content, improving code quality.

– Human-curated code samples

Incorporating human-curated and reviewed code samples enhance code quality and security.

– Human-in-the-loop code review

Human review of generated code before deployment detects potential security flaws.


5. A new era of cybersecurity challenges

The advanced capabilities of Generative AI extend beyond positive applications, as hackers can now harness AI to create intricate and scalable cyberattacks, prompting concerns regarding cybersecurity preparedness.


Traditionally, hacking has been a time and labour-intensive process. However, AI accelerates and streamlines these activities, from gathering target information to devising an attack strategy.


While ChatGPT incorporates protocols to prevent the generation of malware, if a detailed prompt instructs the bot on the steps to create malware rather than providing a direct prompt, the model may respond accordingly. As a result, the model can potentially generate harmful content, such as phishing emails, social engineering attacks, or even malicious code.


Mitigation strategies:

– Zero-trust policy

A zero-trust approach ensures proper identity verification for all elements.

– AI-driven defence

Utilising AI for cybersecurity defence effectively detects and counters evolving threats.

– Bug bounty programs

AI-driven bug bounty programs proactively identify vulnerabilities.


Mitigating Generative AI risks in business and safeguarding the future

While Generative AI offers transformative potential, navigating its risks is essential. Diverse training datasets, on-premise deployment, and robust security measures enable organisations to harness Generative AI’s power while mitigating risks. As technology evolves, proactive risk management remains crucial for reaping the full benefits across industries.


Tovie AI offers expert guidance through AI readiness evaluation for businesses considering Generative AI integration. Our experts identify optimal use cases, ensuring a seamless and impactful adoption journey. 

Contact Tovie AI today to embark on your AI-powered journey confidently and successfully

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