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Generative AI Risks

Organisations must understand and consider all these risks in developing GenAI apps. Before moving forward, they should analyse the input from the technology partners to fill in-house knowledge and skill gaps. Our team at Apsisware is here to help you manage these risks and achieve your AI vision.

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Generative AI Projects

All these types of projects must deal with two categories of risks:

Risks associated with the "traditional" software applications lifecycle

These risks are generally well-known, varying from project scope creep to incorrect estimations and lack of commitment from the stakeholders.

The risk management process is a key component in all project management methodologies, and its principles and components are integrated in any well-driven project.

Risks that are specific to GenAI applications

introduced by the usage of the new AI technologies – LLMs, SLMs, RAG, ReACT and so on. This is the category we will focus on below.

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Types of GenAI Risks

While assembling a full, comprehensive list of GenAI applications risks is not possible, the common ones that you need to protect against are

Biassed or Toxic responses

This is an ethical challenge which is associated with inherent GenAI potential to do unintended bias and discrimination.  Machine learning algorithms, which power LLMs, are learning from vast datasets and often reflect the biases present in the input data. 

This risk manifests when GenAI provides incorrect or imaginative answers that are not grounded.

GenAI can present erroneous information or may respond to the same inputs with widely varied outputs.

This is a phenomenon where GenAI solutions provide responses that incorporate fabricated data that appears authentic.

“Bad” users can use GenAI solutions to generate fake information and fake data. Deepfake technology uses GenAI technology to manipulate audio and video content, making it appear as if individuals are saying or doing things they never did.

Copyright infringement, Privacy breaches, GDPR con-compliance, Regulatory compliance to country- and region-specific, Intellectual property.

Prompt injection, Jailbreaking, Data privacy breaches.

Consider a GenAI solution that inadvertently or accidentally allows a user to delete content in a database.

The non-deterministic nature of LLMs poses significant risks to your brand reputation when exposing users to your GenAI apps.

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Risk Management

Here is a quick guideline to help you start addressing the above mentioned common GenAI risks. Of course, there is no one-size-fits-all approach – you must customise your project risk management for the specific application.

  • Perform careful input data validation and data preprocessing.
  • Test! And next, test again! There is no replacement for good testing and effective quality assurance processes. Testing is a strategic imperative when building GenAI apps.
  • Establish an AI governance framework in your organisation. Enforce policies for how GenAI applications can be used. Create an AI governance and compliance checklist.
  • Perform Risk Management.
  • Build a diverse team. This can be very important in verifying accuracy, spotting hallucinations, and reducing bias and toxicity.
  • Permanently collect feedback from the users
  • Implement good logging. GenAI apps are notoriously hard to debug, and logging can be very helpful.
  • Monitor your solution.
  • Plan for maintenance and support. Be proactive.
  • Stay informed and adapt, as the GenAI landscape is evolving very fast. Stay updated on evolving threats and best practices. Train your team. Educate your users.
  • Ensure solutions infrastructure Scalability, Optimization, and Reliability.
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