Generative AI: Driving Enterprise Value with Cybersecurity at the Forefront (Nasdaq)

2023 will be known as the year generative AI took the world by storm. While other forms of AI have been used by enterprises for years, generative pre-trained (GPT) models like ChatGPT are taking center stage today.

The implications for the global economy are significant: McKinsey research estimates generative AI could contribute $2.6 to $4.4 trillion to the global economy annually and the AI market is projected to grow to $407 billion by 2027. Market leaders are adopting new technologies to stay ahead of the curve, with hefty investments from the largest global technology players, including Amazon (AMZN), Microsoft (MSFT), Alphabet (GOOG), and Meta (META).

Companies of all sizes have an opportunity to incorporate generative AI and improve business efficiency, enhance existing products and services, and create better customer experiences. This is especially true in knowledge-based industries like technology, banking, pharmaceuticals, retail, and education.

However, any company integrating generative AI must do so in a secure manner to create lasting value. The cybersecurity industry has critical insights on how generative AI can help protect companies from cyber threats and how AI needs to be secured.

Productivity

Generative AI has the potential to help companies and employees dramatically increase productivity. Tasks like reading through long email chains, taking and organizing notes during meetings, performing research, and synthesizing information can be automated or streamlined. One clear example of this is Microsoft (MSFT) Copilot, a personalized AI assistant that integrates with Microsoft applications (like Outlook, Word, and Teams). Copilot is designed to help users answer questions, summarize content, customize settings, and act as an assistant to improve efficiency. Other co-pilots and AI assistants (like Zoom’s (ZM) AI companion) are emerging as well. Companies that invest in these generative AI-powered assistants will have access to a significant productivity booster for their employees.

Sales and Marketing

Generative AI can also help companies automate or enhance sales processes. Every company’s sales pipeline is fed by vast amounts of unstructured data including text from prospect emails, audio from sales calls, and video meetings. Generative AI can analyze interactions with prospects and customers, guide negotiations, and predict trends based on historical data. One recent example of this is Salesforce’s (CRM) Einstein GPT, a customer relationship management-focused AI that generates personalized content throughout the sales pipeline.

Generative AI is expanding possibilities for marketing as well. There’s already precedent for AI-powered advertising- Google (GOOG) Ads, for example, has used AI to help businesses optimize advertisements for years. Now, with generative AI, marketers will be able to enhance content personalization, production, and search engine optimization to create and edit emails, blogs, website copy, social media posts, newsletters, and other content. Some of the current market-leading AI in this area include Jasper.ai, ChatGPT, and Bard.

Data Analytics

Data is the lifeblood of modern business. The challenge companies face is how to organize, analyze, and derive insights from an ever-expanding amount of data. There’s a history of companies leveraging other forms of AI for this purpose- for example, Amazon (AMZN) Web Services’ machine learning AI helps companies analyze and leverage their data across numerous business functions. Generative AI will help automate and scale data analytics by generating synthetic data to enhance AI model training, producing actionable insights from data, crafting narratives around data, and creating business reports and presentations. One current example is pharmaceutical giant Merck (MRK), which uses generative AI to create synthetic image data to develop life-saving drugs.

Product Development and Customer Support

Companies will also find opportunities to improve their products, streamline the software development process, and enhance customer support with generative AI. These business areas have seen significant integrations with other types of AI for years. For example, Tesla (TSLA) leverages a neural network with deep learning in its cars; numerous chatbots rely on natural language processing; Intel (INTC) partners with TruEra* to optimize AI model quality in the enterprise; Apple’s (AAPL) uses AI algorithms in its autocorrect and Siri features; and Netflix’s (NFLX) recommendation feature is AI-powered.

Generative AI can also be used to better understand consumer needs during product development, automate coding and debugging in the software development process, draft technical documentation, and efficiently handle customer questions. Two current examples include GitHub’s Copilot, which acts as a coding assistant to augment software development, and JetBlue Airlines’ (JBLU) chatbot, which leverages generative AI to automate and streamline its customer service chat channel.

Cybersecurity

Generative AI will additionally transform cybersecurity in companies. Enterprises have already integrated various types of AI in their products to enhance cybersecurity for their customers. A few well-known examples include Alphabet’s (GOOG) Gmail spam filtering, Palo Alto Networks’ (PANW) Cortex XSOAR which automates incident response, and Fortinet’s (FTNT) network detection and response capabilities. With generative AI, companies will be able to further automate operations around cybersecurity including threat analysis, incident response, and reporting. Cybersecurity co-pilots like CrowdStrike’s (CRWD) recently launched Charlotte AI will automate repetitive tasks, helping to close the talent gap in cybersecurity and enhance cyber defenses to safeguard enterprise data, prevent breaches, and protect consumer privacy.

Securing Generative AI and Defending Against AI-Powered Threats

The cybersecurity industry provides another critical perspective on generative AI- the importance of securing AI models. Generative AI comes with cybersecurity risks that every company must consider. These AI models can be susceptible to data poisoning (manipulating data AI relies upon), prompt injection (using malicious inputs to manipulate AI into changing its behavior), and data leakage (exposing sensitive or proprietary data fed into AI). Companies- both those that develop generative AI models and those that integrate them- must understand and mitigate these risks.

Cyber criminals who target companies for financial gain are also using generative AI to launch more effective attacks and evade detection. This could lead to a growing number of sophisticated phishing, business email compromise, and ransomware attacks, leading to business disruption for companies. Cybersecurity teams must stay up to date with the latest AI-powered threats and use generative AI to their advantage. Companies without in-house cybersecurity expertise should develop strong policies and strategies to manage risk and invest in relevant cybersecurity platforms, tools, and capabilities.

Companies Must Prioritize Cybersecurity to Get the Most out of Generative AI

Generative AI integrations across sales, marketing, product development, customer support, and cybersecurity will spark innovation. Companies will gain capabilities to increase automation, personalize customer experiences, enhance their products, and improve employee productivity. These efficiencies and productivity gains will help alleviate talent shortages, add new value to products and services, and create growth opportunities. However, using generative AI safely requires a cybersecurity-first approach. Companies must consider cybersecurity risks and take steps to both secure their AI integrations and defend against new AI-powered risks and threats.

***This blog was originally posted on Nasdaq.com. You can read it here.***