AI in Cybersecurity: Innovations, Challenges and Future Risks

Artificial intelligence (AI) is unquestionably one of the most significant current trends across all sectors. It’s estimated that big tech firms are set to invest over $300 billion into this area this year and its impact will be felt across every department in every enterprise.

Cybersecurity is no exception. But while AI-driven cyber defenses will help give companies improved protection from hackers, criminals are also taking note of these tools. So what do firms need to know if they’re to successfully incorporate AI into their security posture while also guarding against new, more advanced threats?

AI offers a range of benefits to businesses when it comes to cybersecurity. The technology helps ease some of the burdens on professionals and offers better visibility into potential threats. In addition, automated systems are able to assess risks and respond faster when issues do arise. This will be essential in preventing issues such as unauthorized data exfiltration.

However, the other side of the coin is that criminals are also increasingly using the technology in their own efforts. Criminal gangs can take advantage of AI cyberattacks in a number of ways, from AI-powered hacks to more convincing phishing attempts that can fool even the most wary employees.

One study found almost three-quarters of security leaders (74 percent) say AI-powered cyberthreats are already having a significant impact on their organizations. However, despite this, 60 percent worry they aren’t fully prepared to defend against these dangers.

As such, firms can’t simply assume their own AI and machine learning security tools will provide full protection. This technology is just the latest front in the arms race between hackers and cybersecurity pros that has been ongoing throughout the digital era. 

To ensure firms are well-protected, it’s important to understand not only what AI can offer to businesses, but how criminals are using the technology to bypass these defenses.

AI is proving highly useful in a number of ways. According to Statista, the most common use for AI in cybersecurity as of 2024 was to monitor network traffic, with  54 percent of organizations deploying it for this purpose. However, this is just the start of what AI is capable of.

Key ways in which AI and deep learning security tools are changing how enterprises enhance their defenses include:

• AI-powered threat detection: AI enables faster identification of a range of threats, including malware, phishing, ransomware and data exfiltration. As well as monitoring traffic, it can use behavioral analysis to spot unusual activity that may indicate an attack, which traditional solutions wouldn’t be able to see.
• Automated security responses: Once potential attacks have been spotted, AI can also react automatically to take whatever response measures it deems necessary, without waiting for human signoff. This may include locking accounts, shutting down data transfers, applying patches or assisting with script creation.
• AI-driven security analytics: AI is able to quickly analyze massive data sets for proactive defense. This can help predict potential attacks before they happen, spot weaknesses within a network or examine malicious code after an attack to learn what it does, how it infiltrated the network and make recommendations for future improvements.

For hackers, AI also presents a huge range of opportunities to refine their attacks and bypass defenses. Like cybersecurity pros, these bad actors are still testing the waters and finding out what the most effective attack strategies will be. However, there are a few common applications for AI when it comes to attacking businesses. Key areas that firms will need to be ready for include:

• AI-powered cyberattacks: This often involves the use of adaptive malware that can react and change its behavior in order to avoid detection. For example, it may learn what normal activity on a network looks like, then adjust its own code to mask its signature or blend in with other traffic.
• Adversarial AI: This involves tools that aim to manipulate business’ AI and machine learning models by feeding them with inaccurate or misleading information.
• Deepfakes and identity fraud: AI-driven impersonation attacks can use powerful generative AI tools to create highly realistic images, audio or video. This could be used, for example, to create fake voice clips of a senior executive at a company and give bogus instructions to employees over the phone or via video link.
• Social engineering: As well as deepfakes, AI can be used to enhance other aspects of a social engineering attack. This may be by studying a business’ communications in order to create more realistic-looking phishing emails or posing as chatbots within customer support solutions in order to convince people to hand over login details.

We’ve seen above how AI is being applied to cybersecurity defenses in order to enhance the way businesses detect and respond to threats. But what does this look like in practice? Here are some of the key applications AI is being used for today.

• Zero trust security: AI can make zero trust security policies easier to implement by assessing every access request in real time and making context-based decisions based on its risk assessment. This can also allow it to adjust privileges based on need to ensure sensitive data is always protected.
• IoT security: Internet of Things (IoT) devices are a weak point in many firms’ security. AI can study patterns in network traffic and detect anomalies that may indicate a cyberattack using these endpoints. AI for endpoint security can also be used to identify and close vulnerabilities in IoT networks.
• Cloud security: With more processing and data storage than ever taking place on the cloud, it’s important to have bespoke security solutions that are tailored for this environment. As well as providing behavioral monitoring and adaptive response, AI helps with this through tools such as smart encryption, which can allow operations to be carried out without decrypting data, or dynamically manage keys, based on usage patterns and risk assessments.

Using AI to detect anomalies within a network can greatly ease the burden on IT staff. Advanced tools can take on much of the day-to-day monitoring, threat assessments and decision-making that currently take up so much of these professionals’ time and resources.

However, AI-led cybersecurity is not without its challenges. There are a range of potential pitfalls and considerations that must be taken into account if firms are to successfully implement the technology into their defenses.

For instance, they will have to consider the potential costs involved. Getting started with AI can be an expensive business. As well as the technology itself, firms need to have access to the right datasets to train their system and have internal expertise to build tailored solutions. However, with the right third-party partners, businesses can access solutions that would otherwise be beyond their reach.

Another issue is false positives. AIs that are not yet fully trained may flag legitimate activity as potentially suspicious and step in to block it before humans have a chance to review. This can be disruptive to operations.

Finally, there are the ethical considerations and issues of bias. AI models are only as good as the data they’re trained on, so poor-quality or inaccurate data can lead to the solutions making mistakes or even opening up new security vulnerabilities.

The advent of AI will allow many businesses to move from a reactive approach to cybersecurity to a proactive one. By using predictive analytics to identify emerging threats before they become a problem, companies will be able to more easily counter attacks and shut down breaches before they have a chance to do damage.

This will be particularly important in tackling issues such as ransomware, which is among the most dangerous types of cybercrime today. Too often, the first time businesses know they have been breached is when they receive a ransom demand, which is too late to protect their most sensitive data. However, the rise of AI in data protection can enable any such attempts to be blocked before any data is exfiltrated.

It’s certain that, as AI becomes even more powerful, its security capabilities will continue to improve – both for enterprises and hackers. As such, in the coming years, we can expect to see a great deal of focus on regulating the use of AI. In particular, questions about how much private data the tools are able to access and what they can do with it need to be answered.

This means businesses will have to tread carefully in their adoption of the technology. While AI is clearly a highly powerful tool, it will still need human oversight to ensure it is operating effectively and ethically.