Artificial Intelligence for Cybersecurity

What Is AI for Cybersecurity?

Artificial Intelligence (AI) for cybersecurity is where a network security provision enlists artificial intelligence and machine learning to enhance protection.

The attack surface for cyber threats has been expanding rapidly, spurred on by the explosion in remote working and its associated increase in the use of Internet-connected services. Traditional signature-based methods of combating these threats have proven increasingly ineffective. Recognizing a threat, or waiting for notification from users, followed by developing a signature to recognize and counteract it, is too slow, leaving organizations vulnerable to attack.

This is where AI comes in. The only way to fight against the ever-changing range of emerging threats is for protection systems to proactively detect them as they appear and adapt accordingly. This system could even operate at the edge on the endpoint itself.

Cutting humans out at this level speeds up the process because AI can rapidly analyze millions of data sets to look for all manner of threats. Instead of looking for specific software signatures for known attacks, it can detect the behavior of malware, phishing, or “cryptojacking,” where infected systems are directed to mine cryptocurrency for the hacker. If any of these behaviors are found, AI can learn and improve its protection, providing much more effective cybersecurity.

Cybersecurity AI

What Kinds of AI Are Used for Cybersecurity?

Not all cybersecurity labeled as AI is created equal. Multiple technologies fall under this banner, including machine learning and neural networks. These systems look for patterns associated with malicious behavior but use different algorithmic methods. AI can be applied at the endpoint levels and track activity on known sources of threats such as hacker groups and the dark Web. It can protect from threats “live” or merely improve the speed of signature updates that are centrally distributed.

The key to all types of AI in cybersecurity is data. AI’s benefit comes from its ability to analyze vast amounts of information about behavior across multiple domains, including endpoints, networks, and shared services. Machine learning can be applied to build models of “normal” behavior and then use these to detect anomalies in real-time. However, these can employ human-designed algorithms or self-training neural networks, which build models using deep learning across layered analytical nodes.

AI can also be implemented in a variety of ways. The main algorithm could be static, with endpoints using AI locally to detect new threats. Or they could use a distributed model, where this approach is combined with the ability to upload results to a central repository and download regular updates based on combined learning. This approach evolves protection based on data from all cybersecurity system users.

Use Cases for AI in Cybersecurity

AI can be deployed in many ways to benefit organizational cybersecurity:

Intrusion detection systems can compare an AI-derived model of “normal” network behavior to spot anomalies that indicate malicious traffic caused by a breach within the perimeter.

AI can be built into endpoint security solutions at the hardware level to protect devices against emerging threats before their vectors have been encoded into security software as signatures.

Artificial intelligence for IT operations (AIOps) systems can collate massive amounts of data from multiple sources, enabling them to detect malicious actors even if their behavior in individual cases is imperceptible.

Machine learning can be harnessed to create advanced malware analysis tools that keep pace with the increasingly complex hacks and scams constantly being developed by cybercriminals.

Innovations in Cybersecurity AI

Just as cybersecurity is harnessing AI, so are cybercriminals, making it even more critical that the technology is constantly improved. The latest innovations focus on leveraging data science to continually win the arms race against cyberthreats, leveraging AI to improve their effectiveness.

Cybercriminals use AI to hide their payloads more effectively and to mimic authentic communications and traffic more closely in phishing attacks. Cybersecurity AI must be able to react to these threats to second guess the signs to continue providing protection.

Cybersecurity AI can automate risk mitigation and detection, providing a seamless healing process in the event of a threat. The area of risk prediction has plenty of room for growth, where AI takes the role of “white hat” hackers, finding potential weaknesses before the cyber criminals do.

In the future, autonomous AI will play an increasing role, decreasing the need for human supervision. Deep learning is leveraged by the most advanced cybersecurity AI solutions, employing neural nets rather than human-designed algorithms. These produce their innovation automatically.

AI-powered chatbots like ChatGPT are influencing cybersecurity. Cybersecurity professionals say generative AI tools like ChatGPT enable more realistic phishing emails and may help low-skilled threat actors develop more effective cyberattacks. However, many cybersecurity professionals also use ChatGPT to write cybersecurity policies, reports, and scripts that help protect their organizations.

The best cybersecurity systems make AI central to their offering, rather than merely adding it as an extra or plug-in. AI should be a core component rather than bolted onto an existing, traditional product.

It is also crucial that the AI cybersecurity features provide clear benefits rather than merely marketing. Ensure that the AI product provides its benefits as seamlessly as possible without interfering with employee productivity. It also needs to protect all devices in the fleet, including mobile and IoT, without causing performance and resource issues.

Because AI for cybersecurity improves as it learns, the more mature provisions will be better able to detect threats than newcomers. Cylance® AI is already on its seventh generation, giving it years of training across billions of diverse threat data sets. More recently introduced cybersecurity AI will not have had so much training, making it less effective.

If your organization has implemented Zero Trust Security, you want cybersecurity AI that will fit into this framework. For example, it needs to work alongside the heuristic of Zero Trust for access and permissions adjustments.

Connected to this, consider how the AI is maintained. Does it run locally on the endpoint or exclusively in the cloud? Does it just improve signatures, or does it work in real-time to prevent threats as they occur? Does it require employee training to deploy, or is it relatively seamless? Finally, how often is the AI itself retrained?

The true test is how a solution fits with your organization in real life. You need to find out if there are the proper levels of aggressiveness for different scenarios, which users might need to adjust, and if the cybersecurity AI is as effective in offline scenarios as online. If it is offline, can it prevent zero-day malware without needing connectivity? This is the true test for cybersecurity AI: how much does it enhance your organization’s protection?


What is AI for cybersecurity?

AI for cybersecurity harnesses proactive machine learning to enhance protection from emerging threats that traditional signature-based methods may not pick up. It can monitor activity and detect malicious software from its suspicious behavior, rather than needing a complete code profile.

How is AI used in cybersecurity?

AI is applied to data collected from device and network behavior. Live activity is compared to a model of “normal” behavior from the organization and its users. Anomalies that do not match a user can then be flagged. With both organizational and user behavior considered, AI can reduce false positives and false negatives to the minimum.

What are some examples of AI in cybersecurity?

Malware is constantly evolving. AI can detect new malware by how it acts compared to “normal” behavior without needing a precise code signature. AI can protect against new threats rather than act retrospectively.

AI is also adept at tackling threats from bots attempting account takeover or the creation of fake accounts. By analyzing large amounts of data, AI can distinguish between benevolent automated behavior and malicious activity.

Detecting threats in advance is another area where AI can benefit cybersecurity. The massive amount of data AI monitors allows it to pinpoint breach risks before they are exploited.

Most importantly, employing AI in cybersecurity can help keep the devices used by many remote workers safe. It can provide protection even if threat signature updates haven’t been performed promptly.

Will AI take over cybersecurity?

AI is unlikely to take over cybersecurity entirely, but it should be integral to every threat protection provision. Combining traditional approaches with AI delivers comprehensive security, but with AI at the core. Malicious hackers are also harnessing AI to improve their cyberattacks, which means human cybersecurity professionals will need to continually monitor threat vectors to learn about methods that go beyond the capabilities of AI on its own. While AI is not the only factor in a successful cybersecurity solution, it is now an essential core component that provides a much higher level of protection.

Cylance AI set the standard as one of the first machine learning (ML) models for cybersecurity. Now in its seventh generation, Cylance AI has trained on billions of diverse threat data sets over several years of real-world operation. Tested across an array of cybersecurity applications, its performance has proven unparalleled effectiveness for identifying and preventing malware.

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