Generative Artificial Intelligence (GenAI) is revolutionizing operations within Global 2000 and Fortune 500 enterprises and offers innovative solutions across industries as seen in Google’s article on 321 real-world gen AI use cases. However, when enterprises adopt GenAI, it presents many risks that must be properly managed to effectively, efficiently, and safely harness the technology's benefits.
One misconception about GenAI and its associated risks is that third-party vendors such as OpenAI, Google Gemini and Microsoft Copilot are solely responsible for these risks. On the contrary, companies using these platforms are exposed to their risks and may be held responsible for any violations related to laws and regulations like the EU AI Act or the newly proposed Texas Responsible AI Governance Act.
To protect your business, especially at enterprise scale, your AI governance framework must manage the risks associated with GenAI.
9 Risks of Generative AI
- Data Privacy and Security Concerns
Generative AI systems require extensive data for training, which often includes sensitive or proprietary information. Without robust data security measures, there's a risk of exposing trade secrets and customer data. For instance, inadequate AI or data governance can lead to unauthorized access or data breaches, compromising both company and client information (Joyce et al., n.d.). Gartner predicts that improper use of GenAI will be responsible for at least 40% of AI-related data breaches across the world by 2027 (Gartner, n.d.). - Intellectual Property (IP) Infringements
Generative AI models can unintentionally replicate existing content, posing risks of IP violations. For instance, if an AI system produces content that closely resembles a copyrighted work, it may lead to legal disputes, particularly in industries like media and entertainment, where originality is crucial (Lawton, 2024). A notable case occurred in 2023 when Samsung Electronics banned employees from using ChatGPT after discovering that some had inadvertently fed sensitive IP data into the system. By default, ChatGPT retains user data to improve its models unless users opt out, highlighting concerns around data security and intellectual property protection (Ray, 2023). - Bias and Discrimination
AI models trained on biased data can perpetuate or even amplify existing prejudices, leading to discriminatory outcomes. For instance, a generative AI used in recruitment might favor certain demographics if the training data reflects historical biases. Such biases can result in reputational damage and potential legal repercussions for organizations (“The flip side of Generative AI”, n.d.). - Generation of Misinformation
Generative AI has the capability to produce content that appears credible but is factually incorrect. This "hallucination" can lead to the dissemination of misinformation, which can mislead stakeholders and damage public trust (Lawton, 2024). For example, Air Canada’s chatbot promised a customer a discounted fare he did not qualify for. Air Canada said the chatbot is responsible for its own actions, but ultimately, the company had to pay the passenger $812.02 (Yagoda, 2024). - Regulatory and Compliance Challenges
The evolving regulatory landscape for AI poses compliance challenges for enterprises. The EU AI Act’s unacceptable risk deadline started February 2nd, 2025, and organizations must navigate regulations concerning data usage, privacy, and AI ethics. Non-compliance can result in legal penalties and hinder AI initiatives. For instance, adhering to data protection regulations requires diligent oversight of AI data handling practices (Joyce et al., n.d.). - Security Vulnerabilities
Generative AI systems can be susceptible to adversarial attacks, where malicious actors manipulate inputs to deceive the model into producing harmful outputs. For example, an attacker could input crafted data to cause an AI system to generate malicious code or misinformation (Lawton, 2024). Such vulnerabilities can compromise system integrity and security as seen when someone tricked a ChatGPT powered chatbot to sell a 2024 Chevy Tahoe for $1. - Operational Dependence and Reliability Issues
Over-reliance on generative AI can lead to operational challenges, especially if the AI system fails or produces unexpected outputs (Joyce et al., n.d.). Recently, Virgin Money's AI-powered chatbot mistakenly flagged the word "virgin" as inappropriate when a customer inquired about merging their Individual Savings Accounts (ISAs), leading to a frustrating interaction. This incident highlights the risks of AI misinterpretation in customer service, potentially harming user experience and company reputation (Quinio, 2025). - Inadequate AI Governance and Unclear Accountability at Companies
AI governance involves ensuring that the technology is used responsibly and transparently. Inadequate governance frameworks can lead to issues such as the misuse of AI for unethical purposes, lack of accountability in decision-making, or insufficient oversight. This could expose companies to legal challenges or undermine public confidence in AI technology (Committee of 200, 2025). A notable example highlighting the challenges of AI governance in the private sector involves OpenAI's internal management of its generative AI technologies. In November 2023, OpenAI's board of directors dismissed CEO Sam Altman, citing concerns over his lack of transparency and potential conflicts of interest related to AI safety processes. This incident underscores the critical need for robust governance frameworks to ensure responsible and transparent AI development, as inadequate oversight can lead to internal conflicts and undermine public trust in AI technologies (Perrigo, 2024). - Ethical and Societal Implications
The deployment of generative AI raises ethical questions, including concerns about job displacement due to automation, the potential misuse of AI-generated content and denial of insurance claims (Lawton, 2024). Giant insurance companies like UnitedHealthcare, Humana, and Cigna are facing class-action lawsuits over their use of AI algorithms, which have led to a rising number of claim denials for life-saving care. As a result, enterprises must carefully navigate these ethical challenges to uphold public trust and fulfill their social responsibility (Schreiber, 2025).
How to Balance GenAI Innovation and Risk
Balancing the innovation potential of GenAI with its inherent risks requires a structured approach that prioritizes governance, compliance, and responsible deployment. Organizations must implement robust AI governance frameworks that ensure transparency, accountability, and security while enabling innovation at scale.
- Establish Visibility and Inventory of AI Systems
To mitigate risk, enterprises need a single source of truth for all AI initiatives. A comprehensive inventory of AI use cases and models enables organizations to track performance, identify risks, and enforce compliance in real time. - Automate Risk Controls and Compliance
Organizations should enforce standardized governance policies across AI projects. Automated workflows ensure regulatory adherence, ethical AI development, and consistent oversight, reducing legal exposure and operational risks. - Monitor and Mitigate Bias, Misinformation, and Security Threats
Continuous AI monitoring allows organizations to detect and address bias, misinformation, and adversarial threats. Implementing automated testing and validation frameworks ensures AI-driven decisions remain fair, accurate, and aligned with ethical standards. - Implement Responsible AI Safeguards Without Stifling Innovation
The key to balancing risk and innovation is integrating AI governance seamlessly into AI development workflows. Scalable governance models enable teams to experiment and deploy AI rapidly while ensuring compliance with evolving industry regulations. - Ensure Enterprise-Wide AI Accountability and Transparency
A clear AI governance structure fosters accountability across teams, ensuring AI aligns with business objectives and regulatory expectations. Real-time reporting and risk assessments provide executives with the insights needed to make informed decisions about AI investments.
By embedding governance into every stage of the AI lifecycle, organizations can confidently scale Generative AI while mitigating financial, legal, and reputational risks.
References
- KPMG. (n.d.). The flip side of generative AI. https://kpmg.com/us/en/articles/2023/generative-artificial-intelligence-challenges.html
- Committee of 200. (2025, February 4). AI governance: The CEO’s ethical imperative in 2025. Forbes. https://www.forbes.com/sites/committeeof200/2025/02/04/ai-governance-the-ceos-ethical-imperative-in-2025/
- Gartner. (n.d.). Gartner predicts 40% of AI data breaches will arise from cross-border GenAI misuse by 2027. https://www.gartner.com/en/newsroom/press-releases/2025-02-17-gartner-predicts-forty-percent-of-ai-data-breaches-will-arise-from-cross-border-genai-misuse-by-2027
- Joyce, S., Kashifuddin, M., Kosar, J., Persons, T., Agarwal, V., & Greenstein, B. (n.d.). Managing the risks of generative AI. PwC. https://www.pwc.com/us/en/tech-effect/ai-analytics/managing-generative-ai-risks.html
- Lawton, G. (2024, July 23). Generative AI ethics: 8 biggest concerns and risks. TechTarget. https://www.techtarget.com/searchenterpriseai/tip/Generative-AI-ethics-8-biggest-concerns
- Lopez, J. (n.d.). GM dealer chatbot agrees to sell 2024 Chevy Tahoe for $1. GM Authority. https://gmauthority.com/blog/2023/12/gm-dealer-chat-bot-agrees-to-sell-2024-chevy-tahoe-for-1/
- McCarty Carino, M. (2024, April 8). Pre-employment tests are getting the AI treatment. Marketplace. https://www.marketplace.org/2024/04/08/tech-is-supercharging-pre-employment-personality-tests/
- Perrigo, B. (2024, September 5). Helen Toner: The 100 most influential people in AI 2024. Time. https://time.com/7012863/helen-toner/?
- Quinio, A. (2025, January 29). Virgin Money chatbot scolds customer who typed “virgin.” Financial Times. https://www.ft.com/content/670f5896-1fe5-4a31-b41f-ad4f5b91202f
- Ray, S. (2024, February 20). Samsung bans ChatGPT among employees after sensitive code leak. Forbes. https://www.forbes.com/sites/siladityaray/2023/05/02/samsung-bans-chatgpt-and-other-chatbots-for-employees-after-sensitive-code-leak/
- Schreiber, M. (2025, January 25). New AI tool counters health insurance denials decided by automated algorithms. The Guardian. https://www.theguardian.com/us-news/2025/jan/25/health-insurers-ai
- Yagoda, M. (2024, February 23). Airline held liable for its chatbot giving passenger bad advice: What this means for travelers. BBC News. https://www.bbc.com/travel/article/20240222-air-canada-chatbot-misinformation-what-travellers-should-know
