10 Types of Generative AI Risks and Strategies for Mitigation

2022 marked the start of a new area in generative AI. This period witnessed the rapid advancement of LLMs (Large Language Models) like GPT-3, GPT-4, BERT, Claude, Gemini, Llama, Mistral, and others. These LLMs showcased remarkable capabilities in natural language processing (NLP), image generation, and creative content creation. As a result, AI-driven tools have spread across various industries, enhancing productivity and innovation in content creation, customer service, development, and more. They also have the potential further to revolutionize sectors like healthcare, finance, and entertainment.

The transformative impact of this modern technology is yet to be fully understood. Yet, organizations looking to maintain a competitive advantage should plan to incorporate GenAI in their operations sooner rather than later. At the same time, they should address GenAI’s security risks.

The use of Gen AI applications and LLMs, either public or by in-house development and/or deployment, can pose risks to organizations. These Gen AI risks include:

1. Privacy Concerns

Generative AI relies on vast amounts of data, often harvested from various sources. These might contain personal information, including PII. If this data is used in outputs, it can inadvertently expose sensitive details about individuals, leading to privacy breaches and potential misuse. The black-box nature of many GenAI models further complicates transparency and accountability, making it difficult to trace how specific data points are used or stored.

2. Phishing Emails and Malware

Generative AI allows cybercriminals to craft highly convincing and sophisticated attacks. Before generative AI, one of the tell-tale signs of a phishing email was poor grammar and phrasing. However, phishing emails generated by AI can mimic the tone, style, and format of legitimate communications. This makes it difficult for individuals and security systems to detect them.

Additionally, attackers can use GenAI to develop and debug malware that can bypass traditional security measures. This AI-generated attack malware can adapt and evolve, making it even more difficult to protect against.

3. Insider Threats and Employee Misuse

Insider threats are individuals within the company who exploit their access to sensitive information and systems. These threats can be intentional, such as data theft or sabotage, or unintentional, like accidental data leaks due to negligence. The insider’s familiarity with the organization’s security measures often allows them to bypass defenses more easily than external attackers.

In the context of GenAI, insiders can inadvertently type or paste sensitive data into GenAI applications. This could include source code, sensitive business information, financial data, customer information, and more.

4. Increased Attack Surface

Generative AI systems can increase the attack surface for cybersecurity threats, as they often integrate with various data sources, APIs, and other systems. This creates multiple entry points for potential attacks. The complexity of these integrations can lead to vulnerabilities that malicious actors might exploit, such as injecting malicious data to manipulate AI outputs or accessing sensitive information through weak links in the system. 

5. Output Quality Issues

Output quality issues in generative AI systems arise when the AI generates text, images, or other outputs that are inaccurate, incorrect, misleading, biased, or inappropriate. Factors contributing to poor output quality include inadequate training data, insufficient model tuning, and the inherent unpredictability of AI algorithms.

In critical applications such as healthcare, finance, and cybersecurity, inaccurate AI outputs can result in severe financial losses, legal liabilities, business crippling, and even endanger lives. But even in non-critical applications, incorrect results and the dissemination of incorrect or misleading information can have consequences on people’s work and lives and businesses’ performance.

6. Made-up “Facts” & Hallucinations

An extreme example of the aforementioned quality issue is the generation of “made-up facts”, called “hallucinations”. This happens when the LLM generates information that appears plausible but is entirely fabricated. These hallucinations arise due to the model’s reliance on patterns in the training data rather than a true understanding of factual accuracy. As mentioned, this can lead to the dissemination of incorrect or misleading information, which poses serious risks – especially in contexts where accuracy is critical, such as healthcare, legal, or financial sectors.

7. Copyright, Intellectual Property & Other Legal Risks

Generative AI systems often use vast amounts of data, including copyrighted material, to train their models. This can lead to the unintentional reproduction of protected content, potentially infringing on intellectual property rights. In addition, there’s the legal question of whether the LLM is legally allowed to be trained on copyright data. Finally, the generation of new content that closely resembles existing works can raise legal disputes over ownership and originality.

These challenges are compounded by the ambiguity in current copyright laws regarding AI-generated content. Currently, these issues are being debated in courts and in the public eye. For example, The New York Daily News, Chicago Tribune, Denver Post, and other papers are suing OpenAI and Microsoft for copyright infringement.

8. Biased Outputs

Biased outputs in AI systems often originate from skewed or unrepresentative training data that reflects historical prejudices and systemic inequalities. When AI models generate biased outputs, it can lead to discriminatory practices in areas such as hiring, lending, law enforcement, and healthcare, unfairly impacting marginalized groups. This poses a serious threat to fairness and equity, as they can perpetuate and even amplify existing societal biases.

9. Compliance

When sensitive information is processed by AI systems, there is a potential for data leaks, unauthorized access, and misuse of confidential data. This risk is exacerbated if the AI service provider lacks robust security measures and compliance certifications. Therefore, sharing data with generative AI tools can significantly elevate the risk of breaching compliance regulations and data protection laws, especially in industries with stringent data protection requirements.

10. Cost of Expertise & Compute

When developing, training and deploying LLMs internally, the cost of expertise and computing can be substantial. Advanced AI systems require high-performance GPUs, specialized hardware, and cloud computing services, which can incur hefty expenses. In addition, highly skilled professionals, such as data scientists, ML engineers, and domain experts, command premium salaries. The global shortage of both GPUs and talent further raises these costs. This presents significant barriers to entry for many organizations. 

After outlining the risks, let’s discuss strategies for protecting against them. 

• Inventory – Identify the areas of the business where gen AI is used. From employees querying popular Gen AI applications like ChatGPT, Claude or Gemini to your engineering teams developing your own LLMs to using commercial or open-source LLMs on your data.
• Risk Assessment – Map and assess the potential security risks associated with each type of use. You can use the list above to help.
• Implement Access Control – Use verification mechanisms to govern which gen AI systems your employees can access and how. For example, an enterprise browser extension can prevent your employees from installing a malicious extension masquerading as a legitimate ChatGPT extension.
• Implement Policies – Enforce policies for how GenAI applications can be used in the organization. For example, an enterprise browser extension can prevent your employees from pasting sensitive code into gen AI applications.
• Software Patching – Update and patch systems, to enhance your security posture against AI-driven (and non-AI-driven) attacks.
• Monitoring – Track and detect unusual incidents and suspicious behavior, from unauthorized access attempts to abnormal behavior patterns to pasting of sensitive data into gen AI tools.
• User Education – Train employees about gen AI risks regularly, through talks, drills, and ongoing support. An enterprise browser extension can support online training by explaining to employees why actions, like pasting source code into ChatGPT, are being blocked.

• Data Quality Assurance – Use datasets that are diverse, balanced, and free from biases or inaccuracies. Implement strict data validation processes for the data, such as automated checks and manual reviews. Continuously update and refine datasets to reflect current and accurate information. 
• Evaluation Metrics – Employ comprehensive evaluation metrics such as precision, recall, F1 score, and BLEU to identify accuracy and performance issues with the model and its outputs.
• Incorporate Human-in-the-Loop Systems – Involve human experts in the training, validation, and fine-tuning phases of model development. Humans can provide critical contextual insights, identify subtle issues that automated systems might miss, and offer suggestions that improve model responses.

• Compliance with Legal Regulations –  Ensure compliance with data protection laws such as GDPR and CCPA. This means ensuring that data used for training is obtained and processed legally, with appropriate consent and anonymization.
• Establish Clear Ethical Guidelines – These guidelines should encompass principles such as fairness, transparency, accountability, and the avoidance of bias. Implementing ethical AI frameworks can provide a structured approach to ensuring ethical considerations are addressed.

• Ensure Infrastructure Scalability, Optimization, and Reliability – Use robust cloud services, high-performance computing resources, efficient data storage solutions, and scalable AI pipelines. For example, adopt a pay-as-you-go model, negotiate volume discounts with cloud providers, and use GPU provisioning.

LayerX is an enterprise browser extension that protects against web-borne threats at the point of risk – the browser. LayerX provides a DLP solution specifically designed for Generative AI tools like ChatGPT, aiming to protect sensitive data without hindering user experience.

Key capabilities:

• Data Mapping and Definition – Identify and define sensitive data like source code and intellectual property for protection.
• Customizable Data Controls – Implement controls like pop-up warnings or blocking actions when sensitive data is detected.
• Secure Productivity – Enable safe use of GenAI tools by applying DLP-like measures to prevent unintentional data exposure.
• Browser Extension Controls – Manage access and actions within GenAI to secure data interactions.
• Granular Risk Mitigation – Detect and mitigate high-risk activities like pasting sensitive data, while maintaining a seamless user experience.