The rise of generative AI like ChatGPT and Midjourney has been one of this decade’s most revolutionary technological advancements. This technology has allowed businesses to scale their operations more efficiently and enabled entirely new functionalities within existing tools.

While generative AI has had a largely positive impact on the world, its negative aspects cannot be ignored. From text to code and images, the quality of the content being produced by these apps is quite high and can be very convincing if used in phishing attacks.

The fact that this type of AI is generally wrapped in a web app can also be an issue, considering 72% of vulnerabilities were due to flaws in web application coding.

According to Security Magazine, 75% of security professionals witnessed an increase in attacks over the past 12 months, with 85% attributing this rise to bad actors using generative AI.

The companies behind these AI models have put in place measures to prevent them from creating malicious code, but they can still pose serious threats if used irresponsibly.

This article will go over the main uses of generative AI in business, explain the related cyber security risks of each, and offer best practices around their use.

5 Use Cases of Generative AI in Business

The main benefit of AI is the versatility it provides. If used properly, this technology can positively impact every department of your organization. Here are the main tasks being delegated to AI currently and the cyber security risks attached to each of them:

Content generation

This task has been executed by AI for a long time, but the results have only become impressive enough to be used in business in recent years.

While platforms like ChatGPT don’t produce content good enough to be used as is, they can provide a helpful starting point and allow your content team to scale outputs faster.

Believable content has long been an issue for hackers attempting phishing attacks. With proper training, users can identify spelling and other grammatical errors to recognize an attack before falling victim to it.

However, generative AI can produce shockingly “human” written content and can even be trained to sound like a specific person, making it harder for users to detect phishing attempts.

Writing code

One of the most significant recent developments in AI technology has been its ability to write coherent, usable code. Early on, bad actors have used this capability to write malicious code like trojans and keyloggers, but the companies behind AI models have quickly patched out this possibility.

Another consequence of the code-writing capabilities of generative AI has been to use it to cut costs around software developers. This practice must be heavily monitored since it can create software breaches and cyber security issues if left without a proper QA process.

Data analysis

Generative AI is increasingly integrated into software to help process large data sets to identify trends and produce recommendations. Some businesses generate tremendous amounts of data but lack the data analysis skills to convert the data into usable insights.

While AI can solve this issue, giving an AI model access to company data can be a significant vulnerability. AI providers are still, in most cases, early startups that often lack a robust cyber security structure, which could put your data at risk.

Optimize business processes

In large industrial or manufacturing settings, data and work instructions often flow between various departments and must be harmonized to be usable.

Given that its output will only be as good as the data input, generative AI can collate instructions from various sources and convert them into detailed plans that any employee can use.

Again, while this process can revolutionize the operations of some companies, operational details can be critical information that must be secured properly for this kind of software to be truly valuable.

Create images or alter photos

Not only can AI create entirely new images, it can alter existing pictures to increase the field of vision or remove certain items. While humans can do this task at a steady pace, generative AI can execute it at a rate that far outpaces human capacity.

The most common use case for the creative editing capability of AI is for e-commerce brands with hundreds of photos per collection. However, this aspect of AI can also produce fake images that can be used to fool victims in social engineering attacks and more.

AI Cyber Security Best Practices

Organizations already have almost no choice but to include AI in their workflow to keep up with the competition. This reality presents a tricky challenge for IT departments since they must now learn about new technology, integrate it within their cyber security plan, and teach users about its pitfalls.

Bringing AI into your organization must be done with extreme care and include a detailed research phase. Here are some of the most important AI cyber security best practices:

Third-party risk assessment

Before allowing an AI tool to be used within your organization, it’s crucial to thoroughly learn about the provider and the underlying AI model being used. This type of software usually has many moving parts and can easily create cyber security risks.

Additionally, AI can sneak its way into a lot of different software tools that might not directly advertise it. For example, some ERP systems utilize AI to connect the different parts overseen. Make sure to ask any new vendors if they have similar practices.

Cyber security awareness training

One of AI’s biggest cyber security impacts is how it improved many popular hacking techniques. Phishing, already the most common cyber threat, can be drastically scaled to create more precise and believable messages.

A new wave of phishing underlined the importance of cyber security awareness programs. AI-assisted phishing bypasses usual detection methods like spelling mistakes and odd grammar. Users must stay vigilant to recognize AI’s speech patterns and other detection methods.

Using Generative AI Responsibly

Generative AI brings an entirely new dimension to many business-critical tasks. It is bound to create an essential shift in how companies operate in the coming years as the adoption of this technology grows.

The proliferation of this technology will also pose serious cyber security threats, both through its usage and the ways it enables hackers to elevate their attacks to new heights. Phishing, in particular, will be affected by generative AI, making it easier to pump out and much more believable.

With a new, scalable version of phishing, it will be more critical than ever to keep your employees informed and prepared against these cyber attacks. While training can help, nothing compares to phishing simulations to truly teach your staff the subtleties of this threat.

 


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