10 Risks of Generative AI & How to Mitigate Them
With industries prioritizing generative AI for innovation and automation, its potential grows. However, risks of generative AI like accuracy and ethical concerns remain. Addressing these challenges is key to ensuring AI benefits humanity.
Explore the top 10 risks of generative AI and steps to mitigate them:
Model reliability & output integrity risks
1. Accuracy risks of generative AI
Generative AI tools like ChatGPT rely on large language models that are trained on massive datasets. To answer a question or to create a response to a certain prompt, these models interpret the prompt and induce a response based on their training data. Although their training data sets consist of billions of parameters, they are finite pools and the generative models may hallucinate responses from time to time.
There can be many potential accuracy risks caused by generative AI models:
- Generalization over specificity: Since generative models are designed to generalize across the data they’re trained on, they may not always produce accurate information for specific, nuanced, or out-of-sample queries.
- Lack of verification: Generative models can produce information that sounds plausible but is inaccurate or false. Without external verification or fact-checking, users might be misled.
- No source of truth: Generative AI doesn’t have an inherent “source of truth”. It doesn’t “know” things in the way humans do, with context, ethics, or discernment. It’s generating outputs based on patterns in data, not a foundational understanding.
How to mitigate hallucination and accuracy risks?
Mitigating the accuracy risks of generative AI requires a combination of technical and procedural strategies. Here are some ways to address those risks:
- Data quality and diversity: Ensure that the AI is trained on high-quality, diverse, and representative data. By doing this, the likelihood of the AI producing accurate results across a broad range of queries increases.
- Regular model updates: Continually update the AI model with new data to improve its accuracy and adapt to changing information landscapes.
- External verification: Always corroborate the outputs of generative AI with other trusted sources, especially for critical applications. Fact-checking and domain-specific validation are essential.
- User training: Educate users about the strengths and limitations of the AI. Users should understand when to rely on the AI’s outputs and when to seek additional verification.
Limitations
According to a recent paper, hallucinations in language models are a statistical consequence of their training and evaluation. During pretraining, models optimize cross-entropy to approximate the language distribution, which mathematically implies that they will generate some plausible but incorrect outputs.
Even with error-free training data, hallucinations arise from inherent uncertainty, limited data (e.g., rare “singleton” facts), or model limitations.1 Therefore, it is almost impossible to completely remove hallucinations; our goal should be to accurately inform the users and try to minimize them.
2. Bias risks of generative AI
Generative AI’s potential for perpetuating or even amplifying biases is another significant concern. Similar to accuracy risks, as generative models are trained on a certain dataset, the biases in this set can cause the model to also generate biased content.
Some bias risks of generative AI are:
- Representation bias: If minority groups or viewpoints are underrepresented in the training data, the model may not produce outputs that are reflective of those groups or may misrepresent them.
- Amplification of existing biases: Even if an initial bias in the training data is minor, the AI can sometimes amplify it because of the way it optimizes for patterns and popular trends.
For example, A 280 billion parameter model showed a 29% rise in toxicity compared to a 117 million parameter model from 2018. As AI systems grow, bias risks also increase. The figure below illustrates this trend, where larger models generate more toxic responses.
Figure 1: Stanford AI Index Report2
How to mitigate bias risks?
- Diverse training data can help reduce representation bias.
- Continuous monitoring and evaluation of model outputs can help identify and correct biases.
- Establishing ethical standards and supervision during AI development helps minimize bias and encourages responsible use.
3. Adversarial & manipulation risks
Adversarial inputs refer to intentionally crafted inputs designed to deceive AI models into making incorrect or harmful outputs. In the context of generative AI, such inputs can subtly manipulate the model to generate biased, false, or even offensive content, which can amplify the existing risks related to accuracy, ethics, and security. The following are examples of such threats:
- Misinformation propagation: Attackers can design prompts that exploit model weaknesses to output misleading or manipulative narratives.
- Toxic content generation: Carefully phrased queries can bypass safety mechanisms and prompt the model to produce offensive or inappropriate content.
- Model exploitation: Adversarial techniques can be used to extract sensitive training data or influence outputs in unintended ways, posing privacy and intellectual property concerns.
How to mitigate manipulation?
- Model training: Incorporate adversarial training techniques to expose models to malicious prompts and teach them to respond safely.
- Prompt filtering and sanitization: Implement pre-processing layers to detect and block harmful input patterns.
- Continuous evaluation: Regularly test models with known adversarial inputs to assess their resilience and improve defenses.
Data protection & security risks
4. Data privacy & security risks of generative AI
Generative AI technology, especially models trained on vast amounts of data, poses distinct risks concerning the privacy of sensitive data. Here are some of the primary concerns:
- Data leakage: Even if an AI is designed to generate new content, there’s a possibility that it could inadvertently reproduce snippets of training data. If the training data contained sensitive information, there’s a risk of it being exposed.
- Personal data misuse: If generative AI is trained on personal customer data without proper anonymization or without obtaining the necessary permissions, it can violate data privacy regulations and ethical standards.
5. Data provenance issues
Given that generative models can produce vast amounts of content, it might be challenging to trace the origin of any specific piece of data. This can lead to difficulties in ascertaining data rights and provenance.
How to mitigate data-related risks?
Nonetheless, using generative models to create synthetic data is a good way of protecting sensitive data. Some steps to mitigate data security threats can be:
- Differential privacy: Techniques like differential privacy can be employed during the training process to ensure that outputs of the model aren’t closely tied to any single input. This helps in protecting individual data points in the training dataset.
- Synthetic training datasets: To mitigate the data security risks, generative models can be trained on synthetic data that are previously generated by AI models.
- Data masking: Before training AI models, datasets can be processed to remove or alter personally identifiable information.
- Regular audits and scrutiny: Regularly auditing AI outputs for potential data leakages or violations can help in early detection and rectification.
Intellectual property risks
Generative AI poses various challenges to traditional intellectual property (IP) norms and regulations. Also, there are concerns around the eligibility of the AI generated content for copyright protection and infringement. Learn what are two key concerns associated with the intellectual property (ip) rights of content in the context of generative ai?
These IP concerns are hard to address given the complex nature of AI generated content. For example, look at the Next Rembrandt painting in the figure below. It is hard to differentiate from an original Rembrandt painting.
Figure 2: New Rembrandt3
Some of the primary risks and concerns of generative AI around intellectual property are:
6. Originality and ownership
If a generative AI creates a piece of music, art, or writing, who owns the copyright? Is it the developer of the AI, the user who operated it, or can it be said that no human directly created it and thus it’s not eligible for copyright? These are problematic concepts when talking about AI generation.
7. Licensing and usage rights
Similarly, how should content generated by AI be licensed? If an AI creates content based on training data that was licensed under certain terms (like Creative Commons), what rights apply to the new content?
Generative models could unintentionally produce outputs that resemble copyrighted works. Since they’re trained on vast amounts of data, they might inadvertently recreate sequences or patterns that are proprietary.
Plagiarism detection: The proliferation of AI-generated content can make it more challenging to detect plagiarism. If two AI models trained on similar datasets produce similar outputs, distinguishing between original content and plagiarized material becomes complex.
For example, a coalition of major music publishers, led by Universal Music Group and Concord Music Group, has filed a new copyright lawsuit against AI company Anthropic, seeking more than $3 billion in damages.
The complaint alleges that Anthropic illegally downloaded and used over 20,000 copyrighted musical works, including song lyrics, sheet music, and compositions, to train its AI models, such as Claude.
The suit also names Anthropic’s CEO, Dario Amodei, and co-founder Benjamin Mann as defendants, and follows earlier litigation in which evidence from another copyright case (Bartz v. Anthropic) revealed extensive unauthorized downloads of copyrighted material.4
How to mitigate intellectual property risks?
- Clear guidelines and policies: Establishing clear guidelines on the use of AI for content creation and IP-related matters can help navigate this complex landscape.
- Collaborative efforts: Industry bodies, legal experts, and technologists should collaborate to redefine IP norms in the context of AI.
- Technological solutions: Blockchain and other technologies can be employed to track and verify the provenance and authenticity of AI-generated content.
Societal & ethical risks
Over the years, there has been a significant discourse on AI ethics. However, ethical concerns of generative AI is comparatively recent. This conversation has gained momentum with the introduction of various generative models, notably ChatGPT and DALL-E from OpenAI.
8. Deepfakes
One of the biggest ethical concerns around generative AI is deepfakes. Generative models can now generate photorealistic images, videos and even sounds of persons. Such AI generated content can be difficult or impossible to distinguish from real media, posing serious ethical implications. These generations may spread misinformation, manipulate public opinion, or even harass or defame individuals.
For example, a study by UNICEF, INTERPOL, and the ECPAT global network shows that at least 1.2 million children across 11 countries reported having their images manipulated into explicit AI-generated deepfakes in the past year, with rates in some countries equivalent to about one in 25 children.
UNICEF highlighted concerns about the impact on children and called for expanded legal definitions and stronger safeguards from governments, AI developers, and digital platforms to prevent and mitigate such misuse of AI technology.5
Erosion of human creativity
Over-reliance on AI for creative tasks could potentially diminish the value of human creativity and originality. If AI-generated content becomes the norm, it could lead to homogenization of cultural and creative works.
9. Unemployment impact
Industries built around routine, structured tasks, such as clerical work, legal services, finance, and data processing, face the highest exposure to AI-driven automation.
Entry-level roles, particularly for young workers, are especially vulnerable because predictable, rules-based tasks are easier to automate. In contrast, sectors such as healthcare and education remain less exposed due to the complexity of human interaction involved. Read AI job loss to learn which industries are most at risk and predictions from AI experts.
For example, experts and analysts from World Economic Forum stated that artificial intelligence is affecting the labor market “like a tsunami”, noting that many countries and companies are unprepared for the pace of change.
Although AI could contribute to up to 0.8 % economic growth, it was cited as a factor in about 55,000 layoffs in the U.S. in 2025, with firms such as Amazon and Salesforce attributing workforce reductions in part to automation.6
10. Environmental concerns
Training large generative models requires significant computational resources, which can have a substantial carbon footprint. This raises ethical questions about the environmental impact of developing and using such models.
How to mitigate societal risks?
- Stakeholder engagement: Engage with diverse stakeholders, including ethicists, community representatives, and users, to understand potential ethical pitfalls and seek solutions.
- Transparency initiatives: Efforts should be made to make AI processes and intentions transparent to users and stakeholders. This includes watermarking or labeling AI-generated content.
- Ethical guidelines: Organizations can develop and adhere to ethical guidelines that specifically address the challenges posed by generative AI.
Tools to overcome generative AI risks
To reduce risks in generative AI initiatives, organizations can adopt measures like AI governance frameworks, LLM security, and agentic AI security tools:
AI governance tools enforce standards, monitor outputs, and provide frameworks for audits and user training. They can track and verify AI-generated content, ensuring AI compliance with licensing and copyright laws.
For example, Airia has introduced an AI Governance capability that complements its existing AI security and agent orchestration tools, providing end-to-end oversight, control, and compliance for organizations’ AI systems.
This launch responds to growing enterprise challenges around accountability and regulatory requirements (e.g., the EU AI Act, NIST, and ISO standards) by helping ensure that AI behaves responsibly, transparently, and in line with policies throughout its lifecycle.
The governance suite includes a governance dashboard, centralized agent and model registry, model repository with versioning and audit trails, compliance automation, and risk assessment tools. Supported by the company’s governance, risk, and compliance (GRC) expertise, the solution enables enterprises to manage risk and demonstrate compliance while accelerating AI adoption.7
LLM security tools serve as another way to monitor and correct biases in real time, ensuring compliance with ethical guidelines and maintaining fair content. They implement differential privacy techniques, monitor for data leakage, and secure data processing. These tools also provide frameworks for regular audits to detect and rectify security issues promptly.
Agentic AI security focuses on mitigating the risks introduced by autonomous AI agents that can plan, make decisions, and take actions.
As these tools can execute multi-step tasks and operate with limited human oversight, the risks of unauthorized actions, privilege escalation, data leakage, and system misuse increase.
To address these threats, organizations implement controls like strict identity and access management, human-in-the-loop approvals, continuous monitoring and auditing, threat modeling, and fail-safe mechanisms to ensure agents act within defined boundaries.
For example, Singapore’s Infocomm Media Development Authority (IMDA) announced a world-first Model AI Governance Framework for Agentic AI at the World Economic Forum, aimed at guiding organisations on the responsible deployment of autonomous AI agents that can plan, reason, and act on behalf of users.
The framework outlines technical and non-technical measures to assess and bound risks, ensure meaningful human accountability, implement controls throughout the agent lifecycle, and enhance end-user responsibility and transparency.8
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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