What are the Risks of Generative AI?

Generative AI and its potential risks and challenges
What are the Risks of Generative AI?

Artificial Intelligence entity that capable of generating content is called Generative AI. It makes use of machine learning techniques to generate content on its own, depending on data trends. Many companies are racing to employ generative AI in light of the growing AI potential with the goal to reduce the high maintenance costs associated with human labour.

Even though generative AI has come a long way, there are still issues with data privacy and confidentiality. The application of generative AI will probably still require careful evaluation of the dangers involved, such as inaccuracy, potential fraud, and loss of intellectual property. Instead of attempting to reduce expenses, several experts advise businesses to safeguard themselves against its exploitation.

According to Salesforce's recent IT Survey on generative AI, 33% of respondents believe the technology is "over-hyped," with worries about bias (73%), security vulnerabilities (79%), and other issues. After careful consideration of several research articles and personal experience here we gathered some of the common risks of Generative AI.

1. Intellectual Property- KPMG lists intellectual property issues as one of the main concerns associated with the usage of generative AI. Using artificial neural networks could be trained on considerable amount of pre-existing data, the technology generates new content which could be audio, images, videos, etc. replies mostly on the patterns it finds as the input it is given. The input data contains various users have input, which the tool retains so to continue learning and growing its knowledge. Furthermore, the information could be exploited to address a question submitted by an individual, this leads to reveal private or proprietary information to the wider public. Organizations/Individuals who use this technology more often face the possibility of having their data accessed by unauthorised parties.

2. Data Poisoning- The training set of data is exploited with misinformation upon user’s request and this is the process of data poisoning. It includes various kinds of attacks that employ data poisoning. Malware is one approach adversaries employ to infiltrate so it could spread unrealistic content. For instance, many contaminated models that were uploaded to the open-source AI platforms that were discovered by researchers lately. Each one might have given attackers the ability to infect user computers with harmful programs. Considering these models are probably going to be incorporated into other systems, this is a type of supply chain constraint. Phishing attacks can also be carried out by attackers with the help of data poisoning. In a phishing scenario, hackers may infect an AI-powered assistance desk and program it to send visitors to a phishing website under their control. Attackers may then simply steal any data they fooled the user into revealing through the Chatbots if they convinced them to enable API interfaces.

3.  Job Risks- AI power has drastically changed the world to overturn professions and abolish certain job process, which is considered as a significant technical change. These modifications have outcome in the removal jobs that are monotonous and require much human labour. Certainly, the AI has changed the approach of role and responsibilities that require for a certain job. However, as per many scholars’ massive loss of jobs probably overstated. As per IMF experts new report the conceivable AI on the global labour market. Around 60% of may have an impact on roughly on jobs due to AI in developed nations. The efficiency more than half might be increased by AI implementation. More than 50% workforce forecast in reduction in labour demand, results in lower salaries and scarcity of jobs, as artificial intelligence technology takes critical purposes achieved presently by individuals. Several of job roles might vanish, in least scenario. In contrast, it is foreseen that artificial intelligence exposure emerge good economy nations by 40% while in low-economy nations by 26%. The outcome entails poor economies and developing market experience direct disruptions lesser from artificial intelligence. Also, many nations impede the requirement of resources to take the leverage of power of artificial intelligence.

4. Bias and Ethical Issues- Training data sets of should be humanoid, consistent, and legal. The training data quality relies on the factor that could impact the output Generative AI outcome in regards to its reliability and precision. The short come of Generative artificial intelligence model is it lacks in standard value. Consequently, the information which is biased used for training make a foundation for algorithms which further develop data that that is not trustworthy. Groups utilize this to make phony images, demeaning content or fake audio that could be used to damage any individual’s identity. As training data may contain personally identifiable information (PII) it could pose ethical challenges. As it can breach individual's name, phone number, card number, username and password, etc. which is considered as personally identifiable information. The requirements of privacy should adhere to the data training models, and must ensure that no PII data is provided to generative AI.  The advantage of AI-powered outcomes would lead employees into trouble as it unintentionally disclosure the private data.

5. Generative AI Fraud- AI era has redefined the fraudlent activities with the touch of Generative AI which is popularly known as Genreative AI fraud. Individuals take advantage of fake (i.e., produced) content made by neural networks to spread rumours. Fraudsters may bypass verification processes and create fraudulent accounts by using generative AI to produce fake selfies, pictures, and audio recordings of actual individuals.


Though there are assorted challenges and opportunities for innovation and advancement in generative AI technology, individuals/organization should be aware of the security risks. It may be the outcome of human-error or unintentionally disclosing private information, challenges with data storage, lack of standard guidelines, problems with international regulations, and data leaks. Businesses ought to employ generative AI even after the numerous issues surrounding it. After careful consideration of its risks expert’s advice organizations/individuals to create a comprehensive plan of action for the business that underlines the risk management, AI system security, and thus fostering AI trust.


1.  What is Generative AI?

The power of artificial intelligence to generate new content, such as written content, images, sounds, audio, and videos is termed as Generative AI. Machine learning model employed in generative AI to identify trends/patterns and connections in a large dataset of content developed by humans. It furthers generates fresh new content by incorporating the trends/ patterns it has learned. One popular learning method is Supervised learning in generative AI models, in this the snippet is fed a set of labels along with human-generated content. Thus it procures the ability to develop content which has human-generated similarities and is considered accordingly.

2. What are the Generative AI Applications?

Utilizing generative AI, one can:

The technology improves customer interactions through real-time conversations.

The technology examines large set of data volumes of unstructured data through summaries and conversational interfaces.

The technology assists with mundane duties that include answering to requests for request, marketing materials translation into different languages, compliant to customer contracts, and more.

3. Is Generative AI a disruptive technology?

To automate mundane tasks, quicken software development, enrich problem-solving abilities, and user experiences make Generative AI as a disruptive technology. This Generative artificial intelligence technology will encourage creativity and reliability while assisting individuals of IT industry in managing the challenges and opportunities of the quickly evolving digital era. The IT industry's future is directly proportional to the transformative potential of generative AI. Generative AI alters services and products of IT organization that fits to user-focused experiences by customizing interfaces, idea generation, service/product recommendations, and interactions to specific user demands. The IT sector would jump a journey of never-before-seen outcomes as it embraces the potential of generative AI.

4. How do we make sure generative AI is used ethically?

The potential of generative AI responsibly can be harnessed by organizations/Individuals and safeguard alongside in an unexpected scenarios or consequences, and ethical AI are promoted by understanding the technology downsides, maintaining transparency, addressing reliability, educating users, periodic updates, and establishing standard guidelines

Related Stories

No stories found.
Analytics Insight