With each iteration of large language models (LLMs), we see the ability to create, convincing human content and insights. As Statista shows, the application of this technology is seen throughout pretty much every major sector, with businesses and investors having rightly seen this as a massive opportunity. This explains why OpenAI is predicted to reach a valuation level of $100 billion by the end of its next funding round.
Despite the hype, there are still many generative AI risks that need to be navigated. Whether a company is building its own GenAI model or utilizing a third-party solution like ChatGPT, many of the same GenAI risks crop up — hallucinations, reputational damage, legal risks, and more. In early 2024, a notable incident highlighted the challenges of generative AI in customer-facing applications. An AI-powered chatbot employed by Air Canada "hallucinated" a refund policy, providing incorrect information to a customer. The resulting legal case deemed Air Canada negligent. While the damages awarded were relatively modest, the case set a powerful precedent: organizations are now being held accountable for the errors made by their generative AI systems.
This underscores a critical need for companies to adopt robust frameworks that demonstrate proactive error prevention and a commitment to achieving performance that meets or exceeds human standards. Such frameworks are not just about mitigating risks—they represent a pivotal milestone in building trust and resilience in an increasingly AI-driven world.
Reflecting on the Gartner hype cycle, many business leaders and commentators are acknowledging that we may have reached the peak of inflated expectations and are now descending into a more grounded phase of reality. This perspective is becoming increasingly prevalent as the industry begins to confront the practical challenges and limitations of emerging technologies.
With a more realistic view of what GenAI can and cannot easily do, businesses can more accurately develop risk management frameworks to support robust systems. With a plan and scope for implementation, the right business resources, and a proper risk management framework in place, companies can sustainably automate business processes and achieve more user-experience and ROI-driven use cases.
After detailing some of the main risks that GenAI presents for businesses, we will take a look at the role an AI risk management framework plays, and how it can be used to develop proactive strategies to adeptly manage hazards and ensure your GenAI venture has the best possible chance of success.
McKinsey’s 2024 global survey shows a huge uptake in generative AI usage, with 65% of respondents reporting that they use it frequently. Considering that this number was 33% in 2023 and the survey was conducted in early 2024, we can quite confidently believe that this number is even higher now at the time of writing.
Whether GenAI is being used for content support in marketing, scientific research and review, or as a help desk chatbot, the risks associated with the various types of errors can be high. Listed below are some of the most common generative AI risks that are seen by all types of businesses.
Generative AI, trained on vast datasets, can produce coherent and contextually relevant text on a wide range of topics. However, its outputs are fundamentally predictions of the most likely sequence of words based on the prompt, surrounding context, and model parameters. This process often introduces challenges, most notably hallucinations– instances where the AI generates content that is nonsensical, factually incorrect, or contextually inappropriate but presented as plausible truth. These errors, stemming from the model’s reliance on statistical predictions rather than understanding, undermine reliability and user trust. Addressing these risks begins during training and fine-tuning, where alignment techniques and reinforcement learning with human feedback (RLHF) work to minimize misbehavior. At inference, additional safeguards like prompt engineering, context-enriching strategies such as Retrieval-Augmented Generation (RAG), and guardrails—spanning in-prompt instructions and post-processing validation—help anchor responses in factual and contextual accuracy.
Addressing the challenge of hallucinations requires distinguishing factual inaccuracies—such as misstatements of numbers, policies, or facts—from other anomalies, including misinterpretations or content that diverges from business rules. Mitigation begins with train-time interventions like fine-tuning and alignment strategies, which help reduce model misbehavior. At inference, safeguards such as prompt engineering, Retrieval-Augmented Generation (RAG), and guardrails—encompassing both in-prompt guidance and post-processing checks—serve to ground outputs in factual and contextual accuracy. However, even with these measures, hallucinations persist in an estimated 3%–5% of responses. This is particularly problematic when ambiguous or incomplete prompts prompt the AI to fabricate plausible yet unverifiable answers, potentially undermining trust and business outcomes. To address this, businesses must adopt a robust, multi-layered framework that integrates train-time interventions, real-time safeguards, and post-inference validation. Such an approach ensures outputs align with organizational policies, user expectations, and factual accuracy, enabling companies to harness the full potential of generative AI while effectively managing its risks.
As detailed by McKinsey, more countries are taking AI regulation seriously, where non-compliance could result in large fines and/or legal action. While regulations such as GDPR are well-established, there are new laws such as the EU’s AI Act, which will see amendments as the technology and usage develops.In the U.S., California’s Assembly Bill 331, an AI safety bill aiming to regulate the development and deployment of AI systems, was recently voted down but will likely be revisited in future legislative cycles. Staying on top of this will present challenges for companies utilizing generative AI.
Data leakage is a significant risk for organisations leveraging generative AI, as it can expose far more sensitive information than anticipated. Whether it’s employees working on proprietary code or executives using the AI to refine confidential documents like speech notes, any data entered into a large language model (LLM) carries potential exposure risks. This issue is compounded by the fact that the data fed into these models can persist within the system, creating vulnerabilities that may not be immediately obvious.
One of the primary concerns is the exposure of client or internal data. Sensitive information, such as trade secrets, financial data, or customer details, can inadvertently become part of the AI’s training or inference outputs, making it retrievable in unintended contexts. For instance, a query from a user asking the AI what it knows about their company could prompt the system to surface confidential information that had previously been inputted, even if it was provided by a different user or team.
Additionally, there is the risk of prompt instruction leakage, where the internal workings of the AI—including prompts designed to guide its behavior—are accidentally revealed in its output. This creates an avenue for potential exploitation, as hackers or bad actors could reverse-engineer these instructions to manipulate the system or bypass safeguards. Such vulnerabilities can undermine the security of the generative AI product itself, opening the door to breaches or malicious misuse.
Prompt injection is a technique used to manipulate AI language models and conversational agents by injecting malicious or manipulative input into the prompt—the initial text or instruction that guides the AI's response. This attack aims to override or alter the model's intended behavior, often causing it to produce unintended, incorrect, or disallowed outputs.
A striking example of prompt injection’s real-world consequences occurred at a Californian Chevy dealership. Their chatbot was manipulated into offering cars for $1, presenting the response as a legally binding offer. This not only led to confusion among customers but also showcased the financial and reputational risks of insufficiently safeguarded generative AI systems.
Moreover, leading researchers have demonstrated how easily prompt injection can bypass even advanced guardrails. For instance, by subtly rephrasing queries or embedding instructions within the input, they’ve successfully prompted language models, including those from OpenAI, to discuss restricted topics or provide disallowed information. These findings highlight how adaptable and pervasive this threat is, requiring constant vigilance and innovation to mitigate.
As we covered above with Air Canada, a single hallucination can have wide-ranging consequences. Many companies are torn between the desire to implement solutions quickly and keep up with competitors while attempting to foresee and mitigate any issue that could put their company at the top of people’s X feeds, or simply leave current customers in doubt about the reliability of their brand.
Connected to the concerns of reputation damage above, bias and discrimination (real and perceived) is a huge challenge for those developing and using these systems. Many of these issues are said to come from training on and then reproducing existing biases that are present in the real world. There have been claims that AI recruiting tools discriminate against female applicants and AI in criminal sentencing recommends more severe punishments for Black offenders.
However, attempts to deal with these issues through the use of guardrails can bring their own problems. Amazon’s Alexa was recently in the news for showing political bias, touching on issues of who controls the rules for a system’s output, and how this impacts free speech, itself a form of bias.
With so much data used for training, can you say for sure that a model has excluded all copyrighted material? This raises intellectual property (IP) concerns, which come with both ethical and legal considerations.
As businesses and individuals become more knoweldgeable about the data that is being trained, the more we will see everyone from journalists to artists suing based on IP claims, leading to an atmosphere of mistrust and caution around interacting with these products.
Generative AI, while powerful, often operates beyond its intended job function, introducing risks like market confusion and lack of topic adherence. In sales and marketing, AI-generated content without personalization can blur differentiation, as ads or product descriptions that mimic competitors may fail to highlight unique value, damaging brand identity and risking legal challenges over intellectual property. Compounding this issue, generative AI can stray into unintended uses, as seen with a Chevrolet dealership chatbot that not only offered cars for $1 but was also manipulated to generate Python code—an entirely unrelated task. To mitigate these risks, organizations must enforce strict topic adherence through robust safeguards, contextual grounding, and validation layers to ensure AI outputs remain aligned with their intended function, business objectives, and brand standards.
Despite the fact that major LLM providers generally ensure that their AI aligns with pro-social ideals, these can be countered through prompt injections mentioned above, which are often gleefully shared online.
Additionally, the values that an LLM is trained to reflect may simply not perfectly align with your corporate ideals and internal policies. This is a crucial point to consider, especially in industries where socially sensitive topics or potential abuse are present, from social media and dating apps to customer communication in a service or support function.
For businesses that have implemented new technology or systems before, the concept of risk and its mitigation shouldn’t be a foreign one. With foresight and diligence, these hazards can indeed be accounted for and managed.
As we’ve seen, artificial intelligence has the potential to slip up, but this is being countered by increasingly advanced monitoring systems and content filters. These tools can help detect and mitigate content that is inappropriate, off-topic, potentially biased, or hallucinatory, and they typically require significant human intervention.
Just because something can be automatically generated, it doesn’t mean that humans shouldn’t be involved. People are critical to managing various risks by taking into account context, judgement, and ethical considerations that the technology might not be able to do. This is seen prominently in the healthcare sector, where AI-generated diagnostic suggestions are often reviewed by medical professionals to ensure patient safety and accuracy.
Once again leaning on human intervention, output should be periodically evaluated for accuracy and appropriateness. This is often done through combining the power of automated tools with human oversight. For example, a professional may be tasked with frequently monitoring the dashboard of an AI detection system to ensure that things are running smoothly, and identifying risks that should be raised with stakeholders.
As we will talk about in more detail below, quality data is fundamental to minimizing biases and ensuring fair outcomes. Diverse datasets help systems produce text in a way that better reflects the context of the situation, which is applicable across all sectors, especially those where AI is used in customer-facing components.
Having a framework for the deployment and ongoing use of AI is paramount to ensuring that everyone in the company is on the same page, and that customized moderation efforts can be agreed upon.
Guidelines frequently outline acceptable use cases, ethical considerations, and compliance requirements.
As mentioned above, the regulatory landscape for artificial intelligence is fast evolving, necessitating — especially for larger companies — the involvement of legal and compliance teams. They will help to navigate the complex landscape surrounding generative AI, taking into account things such as data privacy and intellectual property rights.
As we will explore below, all these elements should come together with clarity through a dedicated GenAI risk management framework. This allows businesses to systematically identify, assess, and mitigate risks associated with employing this technology.
No business would go without a risk management framework for a new project or the adoption of a technical solution, so why should it be any different for GenAI?
As we’ve seen so far, the generative AI risks to a business are apparent, but so are the tools and practices to proactively counter them.
Want to see how we sure up any GenAI risk management framework?
Generally, the components of a risk management framework include risk identification, measurement, mitigation, reporting & monitoring, and governance. That means, we need to know what the risks are, how they are measured, what steps are to be taken to reduce exposure to them, how (and how often) reporting will take place, and who is responsible for establishing and enforcing a business environment that minimizes threats.
As far as mapping different risks and understanding their severity, a risk assessment matrix is often used, giving a clear visual overview of the threat landscape.
However, there are other ways of organizing threats. As shown below, businesses utilizing artificial intellgence may wish to delineate primarily between the types of threats, showing how they come about, where AI is at fault, and where human (mis)use is the main threat. The Four Types of AI Risk matrix published in the Harvard Business Review shows this well.
So what aspects should be considered when assessing generative AI risk management? While not an exhaustive list, these are some of the main areas.
Data governance, provenance, and quality are essential considerations that have flow on effects to areas like ethical guidelines, bias and compliance.
Questions that should be asked include:
Bias is a tricky one as there are many cases where it comes down to personal interpretation, but that doesn’t mean the risk of obvious bias can’t be reduced through the use of accurate data, as mentioned above, and proper monitoring.
As talked about in the above section, many of the identified risks can be easily mitigated, or at least lessened in effect, by incorporating strong human oversight. Things like wide stakeholder engagement and human-centered quality control are important here.
Within a rapidly evolving market, there is room to move fast while remaining compliant. Once again, this comes back in part to the data collected and used, as well as the human oversight of systems. Especially for larger teams, the risk management framework will also need to make reference to members of the company’s legal team who can ensure that the company is complying by the standards of the day.
As seen with the above Four Types of AI Risk matrix, mapping of threats and misuse is a way of clearing marking out a company’s ethical approach to the use of AI.
Lastly, continous monitoring and evaluation is not just a great defence against threats, it simply demonstrates good business practice. While monitoring is a route to consistent improvement, evaluation allows all stakeholders to be brought on board to assess how things are going and make the necessary strategic adjustments.
As we’ve seen, an AI risk management framework needs to cover a lot of ground. Luckily, there are new developments such as the ISO standard on AI and other region-specific frameworks to provide guidance. With these in hand, you’re well on your way to implementation, as we’ll see in these four key steps.
Begin by identifying and evaluating the potential risks associated with generative AI in your business context. This includes understanding the ethical, legal, and operational implications. Assess the likelihood and impact of each risk, considering factors such as data privacy, model biases, misuse and unintended consequences. A comprehensive risk assessment will provide a solid foundation for developing effective strategies to counter threats.
This will likely involve implementing robust data governance policies, conducting thorough testing, ensuring human oversight, mandating transparency in decision-making processes, and establishing clear accountability mechanisms. These can be coupled with technical measures such as regular audits and AI error detection platforms.
Responsible use needs to come at an organizational level, which is why educating your team about the risks associated with generative AI and the importance of managing them effectively is paramount.
In addition to ensuring technical proficiency, all employees should understand ethical practices, the importance of handling data, and the specific strategies for mitigation that your organization has adopted. This will lead to greater proficiency and a responsible culture in regard to AI use.
Implementing the risk management framework is the first step, but continued monitoring of metrics and key performance indicators (KPIs) is a necessity. Doing so will help you to track progress, identify areas for improvement, and ensure you can be proactive and measured in the face of any issues.
Lastly, we can address some best practices that businesses should adopt when looking at the risks of generative AI. No modern company operates within a silo, which is why these tips involve partnerships as a way to improve chances of success.
Engaging with AI experts is crucial for effective risk management. These professionals bring deep technical knowledge and practical experience, helping organizations identify potential risks and develop robust mitigation strategies. Collaboration with experts ensures that the latest advancements and best practices are integrated into the AI systems, enhancing their reliability and safety. For example, partnering with academic institutions or industry leaders can provide valuable insights and foster innovation while maintaining a strong focus on risk management.
An ethical culture is essential to promoting fairness, transparency, and accountability in all AI-related and wider business activities. Encouraging open discussions about different dilemmas related to the implementation and use of GenerativeAI, as well as providing training on ethical AI practices, means all employees in your organization should feel empowered to positively contribute to your business’s efforts.
Implementing risk management tools is essential for monitoring and mitigating risks throughout the AI lifecycle. These tools work to identify vulnerabilities and make clear the implications of particular strategies with intuitive monitoring dashboards.
Related to the risk management tools above, reliability platforms offer a powerful way to counter GenAI risks. There is a clear need for constant real-time monitoring and robust reporting, going beyond security to look at things like scope violation, factual grounding of AI outputs, and policy compliance validation.
As the most advanced of these solutions on the market, Qualifire has an error detection rate of 99.6%. This outpaces competitors offering detection rates in the low- to mid-90s and provides much needed guardrails to LLMs that, as we’ve seen, can suffer from hallucinations, biases, prompt leakage, and more.
Separating themselves from simple security systems, reliability platforms like Qualifire are able to learn and improve. All errors aren’t created equal, which is why it’s important for systems to be responsive to individual requirements. After all, even with a high error detection rate, the few issues that slip through could be calamitous for a business’s operations and brand reputation. Qualifire ensures that all critical issues are eliminated in real-time and custom use cases are enforced around the clock.
A key part of any AI risk management framework should be the use of expert tools for on-the-spot detection of GenAI errors. Qualifire integrates with your exiting workflow in minutes and operates as a trusted safeguard, ensuring real-time enforcement of your custom policies. This means you can accelerate the time to market for your GenAI solutions and use them with confidence, as we catch all critical errors and protect your brand’s reputation around the clock.
