How Has Generative AI Affected Security?

The rapid adoption of generative AI in recent years has led to changes in the cyber threat landscape. While generative AI has long held out the promise of more efficient, more productive workplaces, it poses several risks, including: 

• Privacy

• Regulatory compliance

• Legal obligations

• Intellectual property

• Business relationships

Additionally, cybercriminals have now learned to use generative AI to launch increasingly sophisticated attacks. Explore how generative AI affects security and how to enhance cybersecurity measures.

Understanding generative AI

Programmers utilize complex deep learning algorithms to develop chatbot-like generative AI interfaces capable of understanding and convincingly rendering human-like responses in novel ways. 

Today, generative AI is widespread, easy to use, and broadly applied to various tasks. People use generative AI to: 

• Write emails

• Write computer code

• Generate images

• Analyze data

As such, generative AI would appear to promise massive gains in efficiency and productivity. However, others use it to produce new cybersecurity threats. 

How has generative AI affected security

Generative AI has been adopted by a variety of industries and sectors, such as: 

• Health care

• Utilities

• Transportation

Workers in these and other fields collect and track highly sensitive customer data that, in the absence of a comprehensive cybersecurity framework, can be liable to theft. 

Many who have adopted generative AI believe cybersecurity and innovation are competing interests, with a delicate balance between the two. As a result, many CEOs prioritize innovation over security, despite the manifold risks generative AI presents. 

Black-hat hackers (those aiming to access or destroy private data, disrupt networks, or shut down websites for financial gain) are still figuring out how to use generative AI to leverage even more sophisticated attacks. This presents an opportunity for you to gain a competitive edge by securing your company using the very same technology. 

New security challenges and risks

At this point, generative AI is sophisticated enough to present many new security challenges and risks to organizations of all types. 

Phishing

Phishing is a way for scammers to trick people into voluntarily giving up personal information by pressuring them into believing fabricated information is legitimate. Phishing is a form of social engineering—a way of manipulating people’s behavior to obtain secure information rather than going after specific protected networks with a coordinated malware attack. Phishing can take the form of: 

• Emails

• Phone calls

• Text messages

• Websites

While phishing itself is a long-established cyberthreat, generative AI allows scammers to develop more creative, harder-to-detect phishing attacks that can evade established security measures. They can use generative AI to create phishing messages free from the spelling and grammar errors that previously alerted users to their fraudulent nature. Using generative AI to expand and automate the scamming process allows phishing to operate at scale. Cybercriminals can now do in minutes what used to take them hours. 

A new-generation threat involves using generative AI to accurately mimic the speaking voices of particular people. AI models can learn from audio inputs how to clone voices; hackers can then program them to request money or goods using those voices. With generative AI, it’s possible for you to get a phone call from a familiar voice asking for something highly unusual—something this person would never request or claim in reality. 

Deepfakes

Deepfakes are audio or visual recordings (or photographs) that appear to be real but were, in fact, manipulated by AI technology. In other words, the events shown in a video may not have occurred, but due to the sophistication of AI, they appear convincingly real. 

Generative AI can imitate specific human visual and audio features in a sophisticated way to make it look as if someone said or did something they didn’t. Hackers often use deepfakes to exploit individuals, companies, or even governments. 

Deepfakes are among the fastest-growing cybersecurity scams. While creating deepfakes with generative AI is quite simple, detecting them requires sophisticated technology beyond what most people currently have at their disposal. 

Enhancing cybersecurity measures

Despite its potential for misuse in terms of creating new cybersecurity threats, generative AI can improve cybersecurity. This is because the same large language model (LLM) training protocol, in which a programmer trains an AI model on huge data inputs, is used to develop today’s sophisticated AI-based cyber threats and counteract those threats with equal sophistication. 

While traditional cybersecurity methods, such as authentication, still have their place, generative AI-based practices show promise for enhancing cybersecurity. 

Creating advanced security tools

LLM-powered generative AI can continuously learn more about an evolving cybersecurity landscape. By adapting to threats in real-time, generative AI-based cybersecurity frameworks can not only respond to threats as they occur but can additionally learn to predict what those threats will be like, proactively enabling security measures.

Generative AI can, for instance, detect the kind of unusual surge in security that suggests a distributed denial of service (DDoS) attack. It can then mitigate the attack more quickly than a person could. 

Advanced AI-based cybersecurity tools can enhance security measures, such as: 

• Endpoint monitoring

• Data security

• Cloud security

• Threat hunting

• Fraud detection

• Access management

• Vulnerability management

AI security tools also provide upstream benefits, such as ease of scaling, better customer experience, and automated regulatory compliance, which can improve your company’s general performance. 

Automating security tasks

Generative AI cybersecurity frameworks are capable of automating repetitive security tasks that normally take human specialists a long time to do. AI can streamline and speed threat detection, scan your system for vulnerabilities, and develop and apply appropriate security patches when necessary. 

AI can continuously monitor network and user behavior for anomalies, to which it can then respond automatically. It can also automate the phishing protection process by analyzing email content, email sender behavior, and past phishing patterns to detect and mitigate phishing-based cybersecurity risks. 

It’s worth noting that human error is still the leading cause of cyber breaches. AI helps reduce common user mistakes, such as mistyped data and bias in cyber threat detection. For example, it can identify potential threats that humans might otherwise miss. Plus, by automating threat response, AI increases efficiency. It responds to threats faster than human cyber professionals can and automates the threat elimination process. 

Generative AI in threat modeling

Generative AI models can accurately analyze information and identify patterns, rendering them capable of predicting future threats. This form of powerful predictive analysis enables AI to proactively protect your systems. 

You can additionally use AI in threat modeling. For instance, you can introduce inert, AI-generated malware into your systems and monitor its progress. This allows you to analyze its behavior, learn to defend against it, and predict future threats. You can then input the information you’ve gathered into your generative AI security system, which can then repeatedly and rapidly perform the process. 

Ethical considerations and regulations

Using generative AI can enhance cybersecurity, but it also introduces risks. Security breaches could expose sensitive data, leading to possible legal issues. This can raise potential concerns about the ethical implications of companies collecting vast amounts of customer data. 

Furthermore, in the absence of human oversight, generative AI is liable to: 

• Factual inaccuracies

• Compliance violations

• Contract breaches

• Copyright infringement

• Misleading communication with the public

This could result in potential lawsuits, increased customer churn, and reputational damage. You may need to devise new ways of auditing risk, evaluating the accuracy of financial reports, and performing other risk management protocols, even in the presence of a robust AI-based cybersecurity framework. 

Ultimately, you’ll need to develop an AI governance strategy—that is, a policy outlining how your company plans to use AI responsibly. As generative AI becomes more commonplace, so too do its associated threats. Issues surrounding data privacy, fairness, model transparency, security robustness, environmental sustainability, and accountability will eventually come up, regardless of how your company uses AI. 

Proactively embracing the opportunity to develop a comprehensive AI governance strategy could be beneficial. A variety of agencies have issued regulatory requirements regarding the responsible use of AI. Such agencies include: 

• The US Department of State

• The National Institute of Standards and Technology

• The US Department of Homeland Security

Critical infrastructure relies heavily on digital technology, and the cybersecurity threat landscape continues to present challenges. By implementing robust safeguards, essential government functions can remain resilient against potential cyber threats.

Learn more about how generative AI affects security with Coursera

AI-based cybersecurity attacks may be increasingly common, but many businesses can use this awareness to learn more about the latest generation of cyber threats. By using existing generative AI frameworks, you can discover powerful tools to proactively defend against these risks and strengthen security. 

To learn how you can utilize generative AI to help mitigate today’s cyber threat landscape, discover more with Coursera. Consider the Google Cybersecurity Professional Certificate, as well as Microsoft's Cybersecurity Analyst Professional Certificate.