AI in Cybersecurity: Pros and Cons

Threat detection and prevention is one of the top potential uses of AI in cybersecurity. AI systems can process vast amounts of network traffic and system logs quickly, spotting deviations from normal patterns and identifying potential threats. 

Through behavioral analysis, AI-based apps can detect anomalies and unauthorized access attempts before they can harm the system.

NLP can analyze text to detect phishing attempts. Machine learning algorithms used in AI-powered threat intelligence platforms analyze large volumes of data to identify emerging cyber threats and previously unseen malware variants.

A key advantage of using AI in cybersecurity is the decreased time it takes to perform threat detection. 

AI is skilled at pattern detection. As a result, when fed large quantities of data, AI models can become quite adept at analyzing signals and deciding whether a click comes from a bad bot, good bot, or a human in a matter of seconds. Some of the signals that are most commonly analyzed include mouse click behavior, screen touches, user cadence, and natural timing. 

AI is great at automating routine tasks such as log analysis, vulnerability scanning, and patch management, handling these repetitive tasks more effectively and improving overall security. 

It also could  facilitate centralized security orchestration, integrating various security tools into a single platform. This centralized approach could improve visibility, control, and coordination, leading to faster and easier threat detection and incident response.

Threats are ever-evolving and cybersecurity solutions need to evolve just as quickly. AI learns continuously from past threats and incidents, monitoring new threats, and adapting as needed. This helps you stay on top of new risks and improve the efficiency, agility, and effectiveness of your security systems.

AI isn’t always perfect. There’s a need to consider the risks of both false positives and false negatives. For instance, AI tools may sometimes flag as suspicious a new activity that looks different from what it previously analyzed. It may even block someone from accessing the network, which could impact the flow of operations.

The opposite is also true. Some attacks may be disguised so well that the AI will not spot the threat right away. After all, hackers can also use AI and their attacks are more and more similar to what would usually be deemed as normal activity. Human intervention may still be necessary to avoid blocking good traffic.

Many AI models, especially deep-learning ones, are black-box systems. This means their internal workings and algorithms are not visible to humans. Plus, most people working with these AI tools won’t have any knowledge about the internal algorithms used. 

Attackers can exploit this lack of transparency, finding even the smallest vulnerabilities and causing incorrect results. AI models also heavily rely on training data. If that data is incomplete or incorrect, the vulnerabilities to adversarial attacks increase. Data poisoning, for example, is a concept where AI training materials are intentionally fed with misleading information via bad actors.

Privacy is a major concern when using AI tools. Even a simple model like the popular generative AI app ChatGPT was subject to discussions, with Italy banning it for a few weeks in 2023 because of non-compliance with GDPR. 

Those concerns have since been addressed and ChatGPT works again in Italy, but ensuring user privacy is still a concern, especially when the AI needs as much data as possible to learn.

On top of that, AI can sometimes make discriminatory decisions, leading to it blocking users from certain groups from accessing the system.

We’re quick to believe AI will solve all our cybersecurity problems. We delegate as many tasks as possible and often hope to minimize user involvement. This overreliance comes with serious risks. AI is in no way infallible but the false sense of security leaves us vulnerable to threats.

Plus, AI strongly depends on how you train it, the datasets you use, and how you interpret results. User error is a strong factor to consider, so you’ll need to double-check information before making critical decisions.

AI has its pros and cons and using it successfully ultimately comes down to how you implement it. Take the right steps from the start, and it will be an invaluable ally. Make mistakes, and you might face false results and many vulnerabilities. Here are a few best practices to keep in mind.

AI is slowly but surely revolutionizing many tech sectors, and cybersecurity is no exception. With capabilities like automated scanning of network traffic, and analyzing user behavior and other patterns to detect anomalies and intrusions, AI can be a powerful tool in cybersecurity.

Like any technology, AI is not without challenges. Issues with privacy, the possibility of bias and discrimination against people based on their location, race, or gender, and false results can seriously impact your business.

That’s why it is essential to keep a close eye on your algorithms and the results they generate and have a team of experts who understand both AI and cybersecurity. Before adopting AI at a wider scale, ensure everyone in the organization understands its implications so that you can take advantage of AI with minimal challenges.