With so many AI models available, ChatGPT, Gemini, Perplexity, DeepSeek, and others, choosing the right one isn’t just about features, it’s about risk. The model you select can have significant implications for data privacy, security, regulatory compliance, and intellectual property protection.
No matter what you’re using AI for, understanding these risks is essential to making an informed decision. In the below, we look at the privacy and security, intellectual property, accuracy, bias and explainability, and compliance risks of four models.
Data Privacy and Security
One of the most pressing concerns when using AI models is how they handle your data. Before entrusting an AI model with sensitive business information, it is crucial to understand where your data is stored, whether it is used for training, and if it could be shared across jurisdictions. Different AI providers have varying policies on data retention, which can have serious implications for compliance with laws such as GDPR and the UK Data Protection Act.
Model | Pros | Cons |
ChatGPT | Enterprise versions offer better security. | OpenAI stores user inputs, raising privacy concerns. |
Gemini | Strong security infrastructure. | AI is deeply integrated across Google’s ecosystem, increasing exposure risks. |
Perplexity | Research-focused, using live data. | Relies on external sources, posing confidentiality risks. |
DeepSeek | Optimised for technical use. | Jurisdictional concerns due to its origin in China. |
To mitigate risks, businesses should consider using enterprise-grade AI solutions with private deployments or API access, ensuring strict privacy controls. For highly sensitive data, self-hosted AI models may be the safest option.
Intellectual Property and Confidentiality
Many AI models retain user inputs to improve their systems, meaning confidential business information could inadvertently become part of a broader dataset. If your competitors are using the same AI provider, there is a risk that insights derived from your data could indirectly benefit them. This is particularly concerning for organisations handling proprietary research, legal documentation, or commercially sensitive strategies.
Model | Pros | Cons |
ChatGPT | Enterprise versions provide stricter privacy controls. | Free versions may train on user inputs. |
Gemini | Seamless integration across services. | AI-driven data flows may increase exposure risks. |
Perplexity | Provides citations. | Pulls from external sources, increasing IP risks. |
DeepSeek | Strong for coding and technical tasks. | Regulatory concerns due to jurisdiction. |
The safest approach is to avoid inputting proprietary data into public AI models. Businesses should prioritise private instances or enterprise-grade AI solutions that guarantee data segregation.
Accuracy, Bias and Explainability
AI models are prone to generating false or misleading information. While some models provide citations to improve transparency, others generate responses without explaining their reasoning. Understanding whether an AI model can verify its claims and mitigate bias is essential, particularly for businesses relying on AI-generated insights for decision-making.
Model | Pros | Cons |
ChatGPT | Strong language capabilities. | Prone to hallucinations (false information). |
Gemini | Optimised for factual accuracy. | Can still generate misleading content. |
Perplexity | Cites sources, reducing hallucinations. | Limited depth in proprietary insights. |
DeepSeek | Useful for technical queries. | Transparency on data sources is unclear. |
To reduce the risk of misinformation, businesses should opt for AI models that provide citation-based responses and always verify critical insights with human oversight.
Regulatory Compliance
With AI regulations evolving rapidly, businesses must ensure that the AI models they use comply with relevant laws. Key considerations include whether the model aligns with GDPR, the UK Data Protection Act, and emerging regulations such as the EU AI Act. Cross-border data transfers are another major factor, particularly for organisations handling personal or commercially sensitive data.
Model | Pros | Cons |
ChatGPT | GDPR-compliant enterprise plans. | US-based, which may create concerns for UK and EU businesses. |
Gemini | Strong compliance frameworks. | AI services span multiple jurisdictions, increasing legal complexity. |
Perplexity | Sources external information. | Potential compliance risks for regulated industries. |
DeepSeek | Useful for technical work. | Data governance concerns due to its origin. |
To minimise compliance risks, businesses should choose AI providers with clear data handling policies and enterprise compliance guarantees. For highly regulated industries, self-hosted AI solutions may be the safest option.
Matching AI to Your Use Case
Not all AI models are suited for every business function. While some excel at general business tasks, others are better for research, coding, or technical workflows. Selecting the right model based on your specific needs can enhance efficiency while reducing risks.
Use Case | Best Model | Why? |
General Business | ChatGPT (Enterprise), Gemini | Strong natural language capabilities and security. |
Research and Fact-Checking | Perplexity | Pulls live data with citations. |
Technical and Programming | DeepSeek, ChatGPT (Code Interpreter) | Optimised for coding and data analysis. |
Regulated Industries | Enterprise AI (Self-hosted) | Reduces compliance risks from cloud AI. |
Aligning AI capabilities with business needs ensures maximum efficiency while minimising risks.
Buy, Build, or Partner? The Trade-offs of AI Adoption
If your business is considering integrating AI, you’ll need to decide whether to buy an off-the-shelf model, build a custom solution, or partner with an AI provider. Each option carries distinct risks and benefits.
For companies with strict compliance or security requirements, developing a custom AI may be the best long-term investment. However, for businesses seeking rapid deployment and scalability, a hybrid or vendor-provided AI model could be more practical.
Key Takeaways
Define an AI Strategy: Choose a model based on risk, privacy, and compliance needs.
Classify Your Data: Avoid inputting sensitive or proprietary data into public AI models.
Monitor AI Regulations: Stay ahead of evolving laws such as the EU AI Act and UK regulatory updates.
Use Enterprise-Grade AI: Private AI models offer better security and compliance.
Match AI to Your Needs: Select a model that aligns with your business function and risk appetite.
Custom AI: If you have strict compliance or security requirements, developing a custom AI may be the best long-term investment
By carefully assessing privacy, intellectual property, accuracy, compliance, and business fit, it’s easy to choose the right AI model for your business.
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