Understanding Generative AI: Opportunities, Risks, and the Path to Responsible Use

GenAIArtificial Intelligence
Written By Andrew Alaniz

Generative AI (GenAI) has rapidly moved from experimental research to mainstream adoption. Unlike traditional AI and machine learning systems, which typically classify data, predict outcomes, or optimize processes, GenAI models create entirely new content like text, images, code, or even video. Tools like ChatGPT, Claude, and many others are now being integrated into workflows across industries from customer service to legal review, software development, and healthcare. While these capabilities represent a leap beyond conventional AI, they also introduce novel risks that organizations must understand and manage effectively. Organizations that want to leverage GenAI effectively must understand both how the technology works and what controls are needed to manage it responsibly.

What Is Generative AI?

Generative AI refers to models trained to create new content such as text, images, audio, or video by learning patterns from existing data. Unlike traditional AI systems that typically classify or predict outcomes, generative models synthesize entirely new outputs. Below are some prominent types of generative AI models:

1. Large Language Models (LLMs)

Large Language Models, such as GPT-5, are trained on vast amounts of text data to understand and generate human language. These models excel in tasks like text generation, translation, summarization, and question-answering. They operate by predicting the next word in a sequence, enabling them to produce coherent and contextually relevant text. LLMs are foundational in applications like chatbots, content creation, and code generation. Wikipedia.

2. Generative Adversarial Networks (GANs)

Generative Adversarial Networks consist of two neural networks: a generator and a discriminator. The generator creates synthetic data, while the discriminator evaluates its authenticity. Through iterative training, the generator improves its ability to produce realistic data, such as images or videos. GANs are widely used in image synthesis, style transfer, and data augmentation Wikipedia.

3. Neural Radiance Fields (NeRFs)

Neural Radiance Fields are a type of generative model designed for 3D scene representation. By encoding a scene into a neural network, NeRFs can generate novel views of complex 3D environments from a sparse set of 2D images. This capability is particularly useful in applications like virtual reality, augmented reality, and 3D modeling Wikipedia.

Risks of Generative AI Adoption

While powerful, GenAI introduces a unique set of risks for organizations:

  1. Data Security and Access

    • Sensitive data may inadvertently be shared with external providers or users.

    • Input prompts could be stored and used for retraining, but these prompts may contain sensitive information.

  2. Hallucinations and Reliability

    • Models can generate factually incorrect or misleading outputs with high confidence - put more succinctly, they can make @#$! up.

    • This creates risks in regulated sectors like finance, law, or healthcare.

  3. Reputation and Brand Risk

    • Inaccurate or offensive outputs can damage trust.

    • Overreliance without human oversight may erode credibility.

  4. Liability and Compliance

    • Questions remain around copyright, intellectual property, and regulatory exposure as well as legally binding decisions that can be made inadvertently.

    • Regulators in the EU, U.S., and beyond are actively developing frameworks for responsible AI use.

  5. Third-Party and Supply Chain Risks

Where Do Organizations Go from Here?

Recognizing these risks is the first step. The next is building a governance and control framework that balances innovation with responsibility. Fortunately, there are emerging global standards designed to help:

  • OWASP LLM Top 10 Risks - This framework is especially useful for those who are developing their own AI models.

  • RiskRubric by the Cloud Security Alliance: This is not a framework, but was just released and is an incredible resource for organizations to use to help understand high level risks of the models they use.

  • NIST AI Risk Management Framework (NIST 600-1): A U.S.-driven standard focusing on governance, trustworthiness, and operational risk management of AI.

  • ISO/IEC 42001:2023 (requires purchase): A new international standard establishing requirements for an AI management system, similar in structure to ISO 27001 for information security. ISO standards are paywalled, but come with the ability to be assessed by a certifying body. This post will only cover a high level overview of the framework.

In addition to these frameworks, organizations can:

  • Implement clear AI usage policies and intake processes.

  • Establish model risk review processes.

  • Conduct vendor risk assessments before adopting third-party tools.

  • Invest in training employees to understand both the power and the limits of GenAI.

  • Apply OWASP LLM Top 10 guidance to proactively mitigate security vulnerabilities.

What’s Next in This Series

In the next few blog posts, we’ll explore how organizations can put structure around GenAI adoption:

  1. Applying general risk management practices to AI - A foundational framework that should be in place prior to GenAI adoption or usage including a cheat sheet of actions that can be taken to build this into your organization.

  2. Applying the OWASP Top 10 for Gen AI to your Appsec Program - This will provide an overview and resources for OWASP Top 10, plus a cheat sheet for building this into your existing programs.

  3. Applying NIST 600-1 to Secure GenAI - This will provide an overview of the framework and a downloadable cheat sheet for adopting this into your existing programs.

  4. Adopting ISO 42001 for AI Governance - the road to building a certifiable AI management system.

By the end, you’ll have both the high-level understanding and the actionable steps needed to responsibly embrace GenAI in your organization.

We are part of a collaborative effort with NIST to develop overlays for GenAI, and here are some additional sources that have been shared:

Andrew Alaniz
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