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Top Security Challenges Of Generative AI And How To Overcome Them

Generative AI is transforming how you live and work. This emerging technology is evolving fast and h...
Anurag Dutt
Published: 11 May 2023
Category: Artificial Intelligence (AI),  Generative AI
Home Blog Top Security Challenges Of Generative AI And How To Overcome Them

Generative AI is transforming how you live and work. This emerging technology is evolving fast and has the potential to transform business landscapes. Unfortunately, generative AI exists with its host of security challenges. That’s why we’re dedicating this explainer to help you understand four pressing security challenges that come with generative AI and how to bypass them.

Challenge 1: Data Poisoning

Data poisoning is a major security challenge for businesses that work with generative AI. Data poisoning happens whenever threat actors intentionally introduce inaccurate or corrupt data into an enterprise’s AI generative model. This way, the model may generate misleading and faulty output. Overcoming the challenge posed by data poisoning means a business will have to cautiously curate the datasets on which its generative AI model trains itself.  Additionally, it is integral to use different techniques, including anomaly detection and data validation, to discover inaccurate and corrupted datasets and remove them.

Challenge 2: Model Theft

Model theft is the second security challenge generative AI faces. This theft occurs when bad actors steal training data or source code of a generative AI model. This way, they can create copies of a model, which they can use to generate highly malicious output. Businesses must keep the training data and source code of a generative AI model secure if they want to overcome this challenge. Businesses can do this by using access control, different security measures, and encryption.

Challenge 3: Bias

Generative AI models are biased, which means they may start generating discriminatory or unfair output. This anomaly happens whenever the AI model is getting trained on biased datasets. The only way to overcome this challenge is to adopt practices, such as fairness testing and data debiasing. That’s an ideal way to ensure a generative AI model isn’t getting trained on biased datasets.

Challenge 4: Safety

Malicious actors use Generative AI models to generate a stream of harmful content in the form of fake news and deepfakes. This genre of harmful content can be used to create a negative impact on society or an individual. The right way to overcome the challenge of safety is by using generative AI ethically. That means a business must be aware of the risks associated with generative AI models and the key steps to mitigating them.

A Roundup Of The Ways To Overcome Key Security Challenges Generative AI Faces

  • Safely and responsibly curating the datasets used for training generative AI models
  • Safeguarding the training data and source code of generative AI models
  • Harnessing key techniques to prevent model theft and data poisoning
  • Ensuring generative AI models are unbiased
  • Leveraging generative AI models in an ethical way

Taking these concrete steps can help businesses ensure that generative AI models are used responsibly and securely.

Final Words
Generative AI is an incredible advancement in the field of AI, which has the potential to improve the way you live and work. Nonetheless, it’s crucial to be aware of generative AI’s security challenges and how your business can overcome them. By taking the steps we mentioned in this blog, your business can be on the way to improving outcomes from generative AI without compromising on its security. We are a trusted generative AI services company with expertise in working with and securing generative AI models. We can also integrate and customize generative AI APIs to help maximize the productivity of your business at speed. Talk with our specialists and take the first step toward embedding the power of generative AI into your business processes.


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