Generative Artificial Intelligence (Generative AI) is expected to usher in a new wave of interactive, multimodal experiences which can transform the way people interact with information, brands, among others. Examples of such experiences include heavy investments from Google, Microsoft, and IBM on AI including generative and explainable. Another example is Amazon Web Services (AWS) simplifying the creation and scaling of generative AI, built for customers by using enterprise-grade security and privacy, access to industry-leading foundation models, and generative AI-powered applications.
The market size in the generative AI market is projected to reach $44.89 billion in 2023 and is expected to show an annual growth rate (CAGR 2023-2030) of 24.40%, resulting in a market volume of $207.00 billion by 2030. Moreover, the United States will have the largest share at $16.14 billion in 2023 as per insights from Statista. In a conversation with FE-TransformX, Vikram Venkateswaran, Partner, Deloitte India, talks about on the pros and cons of generative AI. (Edited Excerpts)
How can Generative AI contribute to reshaping industries and personal interactions?
Generative AI is expected to play a significant role in shaping industries. It has the ability to allow immediate access and insights into all the tribal knowledge that exists in the organisations. Today, large enterprises such as Google, Amazon, Deloitte and IBM, among others, have adopted generative AI on a large scale. Generative AI also has the potential to break through issues such as fraud, data leakage, and identifying targeted audiences, among others and provide the organisation with insights at the right levels to make optimum decisions.
How has the evolution of generative AI raised security concerns?
Generative AI has the potential for positive use as well as being used by threat actors for cyber-attacks. The most common impact could be the creation of social engineering content including deepfakes which poses a very different challenge as opposed to those faced traditionally by enterprises. Other areas of concern include data positioning, prompt injections, and overall supply chain vulnerabilities. Cyber security attacks such as social engineering can be the next important cyber threat that is expected to impact us. The ability to get an organisation to disclose sensitive information voluntarily using phishing content generated by AI is a clear and present danger to organisation security and needs to be handled by a combination of people awareness, process, and policies as well as technology such as generative AI which can bolster cyber defence.
Can you provide a real-world application of generative AI that demonstrates its potential but also highlights cybersecurity concerns?
Recently an organisation leveraged generative AI to conduct a long-distance virtual board meeting. They were able to reduce the need to travel by providing ready information and insights to all board members by using generative AI. However, during the meeting, a deep fake was used by a threat actor to send the wrong information to the participants. While the whole incident was finally exposed as a prank, this clearly demonstrates the challenges with generative AI. The current situation also gives rise to other risks including identity theft, disinformation, and privacy violation.
Why is it important to keep track of generative AI and its cybersecurity measures?
We have seen that for programs such as generative AI to succeed, there has been a need to scale otherwise, the benefits of these programs are not felt. Scaling requires a few things, insights on market demands, adoption both within and outside the organisation and a good assessment of the risks faced in all three. We have observed that cyber risks are a key factor in limiting the growth of emerging technologies hence there is a need to address it.
How can organisations ensure they’re always a step ahead in risk management given the rapid evolution of generative AI technologies?
While organisations will have to invest in their own AI programs to understand how they can leverage AI for good, they also need to identify and mitigate the risks and prepare their cyber defences. The Gen AI paradigm requires an ecosystem play, where various partners must come together. To avoid risks related to generative AI, businesses shouldn’t develop generative AI without a clear business purpose, identify and build the process within the organisation on how these programs will be developed, identify the key risks and ways to mitigate them, develop a governance program and lastly monitor the program constantly.
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