Generative AI: A powerful tool, with security risks

Generative AI is a powerful technology that can be used to create new content, improve customer service, automate tasks, and generate new ideas. However, generative AI also poses some security risks, such as data security, model security, bias, and fairness, explainability, monitoring and auditing, and privacy. Organizations can mitigate these risks by following best practices to ensure that generative AI is used in a safe and responsible manner.

Generative AI is a rapidly emerging technology that has the potential to revolutionize many aspects of our lives. Generative AI can create new data, such as text, images, or audio, from scratch. This is in contrast to discriminative AI, which can only identify patterns in existing data.

Generative AI is made possible by deep learning, a type of machine learning that allows computers to learn from large amounts of data. Deep learning has been used to train generative AI systems to create realistic-looking images, generate human-quality text, and even compose music.

There are many potential benefits to using generative AI.

• Create new content: Generative AI can create new content, such as articles, blog posts, or even books. This can be a valuable tool for businesses that need to produce a lot of content regularly. The technology can also support the reduction in time it takes to generate work, enabling a steady stream of fresh content for marketing purposes.

• Improve customer service: Generative AI can improve customer service by providing personalized assistance. Generative AI can create chatbots that can answer customer questions or resolve issues. These types of uses can support both an enterprise’s employees and customers.

• Automate tasks: The technology can be used to automate tasks that are currently done by humans. This can free up human workers to focus on more creative or strategic work. The technology has the potential to eliminate a lot of toil in many standard business practices, such as data entry and workflow.

• Generate new ideas: Generative AI can be used to generate new ideas for products, services, or marketing campaigns. This can help businesses stay ahead of the competition.

“Generative AI is a powerful technology that can be used for good or evil. It is important to be aware of the potential risks and to take steps to mitigate them.”

Generative AI provides a lot of potential to change the way businesses operate. Organizations are just beginning to leverage this power to improve their businesses. This is a very new area, and the market potential is just starting to reveal itself. Most of the current market is focused on startups introducing novel applications of generative AI technology.

Enterprises are thus starting to dip their toes into this space, but the growing use of generative AI also presents security risks. Some of these risks are new for AI, some risks are common to IT security. Here are some considerations for securing AI systems.

• Data security: AI systems rely on large amounts of data to learn and make decisions. The privacy and security of this data is essential. Protect against unauthorized access to the data and ensure it is not used for malicious purposes.

• Model security: AI models are vulnerable to attacks. One example is adversarial attacks. An attacker manipulates the inputs to the model to produce incorrect outputs. This can lead to incorrect decisions, which can have significant consequences. It is important to design and develop secure models that can resist this.

• Bias and fairness: If the training data in the models contains biased information, the resulting AI systems may have bias in their decision-making. This can produce discriminatory decisions, which can have serious legal and ethical implications. It is important to consider fairness to ensure that AI and ML system designs reduce bias.

• Explainability: AI systems are sometimes opaque in their decision-making processes. This makes it difficult to understand how and why decisions are being made. Lack of transparency leads to mistrust and challenges the credibility of the technology. It is important to develop explainable AI systems that provide clear and transparent explanations for their decision-making processes.

• Monitoring and auditing: Track and audit AI performance to detect and prevent malicious activities. Include logging and auditing of data inputs and outputs of the systems. Watch the behavior of the algorithms themselves.

• Privacy: Private data in model building and/or usage should be avoided as much as possible with artificial intelligence models. This avoids unintended consequences. Google’s Secure AI Framework provides a guide to securing AI for the enterprise.

Securing AI systems is critical to effective deployment in various applications. Considering these issues, organizations can develop secure and trustworthy AI and ML systems. These deliver the desired outcomes and avoid unintended consequences.

In addition to security risks, there are also ethical concerns related to the use of generative AI. For example, some people worry that generative AI could be used to create fake news or propaganda, or to generate deep fakes that could damage someone’s reputation. It is important to be aware of these ethical concerns and to take steps to mitigate them when using generative AI. Organizations will want to enact policies on acceptable use of generative AI which appropriately support their business objectives.

Overall, generative AI is a powerful technology with the potential to revolutionize many aspects of our lives. However, it is important to be aware of the security risks and ethical concerns associated with this technology and to use this technology responsibly. By taking steps to mitigate these risks, we can help to ensure that generative AI is used in a safe and responsible manner and supports your future business goals.

INNOVATION TAKEAWAYS

GENERATIVE AI IS INNOVATIVE

It is a powerful technology that can be used to create new content, improve customer service, automate tasks, and generate new ideas.

THERE ARE RISKS WITH THE USE OF GENERATIVE AI

Generative AI also poses some security risks, such as data security, model security, bias and fairness, explainability, monitoring and auditing, and privacy.

COMMON SENSE CAN HELP COMPANIES LEVERAGE GENERATIVE AI

Organizations can mitigate these risks by following best practices, such as protecting data privacy and security, developing secure models, reducing bias in decision-making, making AI systems more explainable, monitoring, and auditing AI systems, and considering privacy implications.

Interesting read?

Capgemini’s Innovation publication, Data-powered Innovation Review | Wave 6 features 19 such fascinating articles, crafted by leading experts from Capgemini, and key technology partners like Google,  Starburst,  Microsoft, Snowflake and Databricks. Learn about generative AI, collaborative data ecosystems, and an exploration of how data an AI can enable the biodiversity of urban forests. Find all previous waves here.