To face modern threats, using AI for cybersecurity a necessity
As cyberattacks grow in complexity, using AI for cybersecurity is required to stay ahead of threats. Here's how to integrate AI into security processes and avoid potential risk.
AI is becoming an absolute necessity for cybersecurity. Every day, organizations are inundated by threats, exploits and vulnerabilities. Security teams have only a finite number of hours to respond and can only do so much. As a result, AI is being tapped to handle the many repetitive tasks that formerly required manual intervention.
Among the benefits of using AI for cybersecurity, two stand out. The first is AI's ability to rank threats from lowest to highest in real time. It does this by sifting through vast amounts of collected security data. This means security administrators no longer have to manually review dashboards for each security tool and then determine on their own what threats should be addressed first.
Instead, admins can focus on the more complex tasks of fixing problems, rather than being forced to first identify the problem before starting to resolve it. While this function of AI has been integrated within a variety of security tools for years, newer security AI platforms can churn through data generated by multiple security and network devices and then aggregate the information into a centralized platform. Once centralized, advanced AI can get a complete end-to-end view regarding the overall security health of the network. These platforms are known as network detection and response (NDR) and are quickly becoming the AI-based tools that all cybersecurity administrators must be familiar with.
The second benefit is the opportunity to teach the algorithm to automate risk remediation on the administrator's behalf. This is a fairly new concept and requires security administrators to understand how to train AI to properly respond based on factors such as a threat's risk level or to have the AI choose the remediation option that's least impactful to the business. Thus, it's critical that security administrators have the proper level of AI training required to successfully automate cybersecurity remediation tasks.
It's almost certain that your next security upgrade will include systems with features tailored to using AI in cybersecurity.
What part of the security infrastructure should deploy AI first?
With AI now being baked into an increasing number of cybersecurity tools, it's almost certain that your next security upgrade will include systems with features tailored to using AI in cybersecurity. Figuring out which part of the infrastructure to introduce AI can be a challenge. Fortunately, the roadmap for integrating AI-based security is like deploying any other security tool: You want to start with a solid foundation within the network.
For decades, the network has been a key strategic deployment location for security tools, such as firewalls, intrusion prevention systems, secure web gateways and secure remote access. When replacing these tools with modern versions that include AI, they should remain housed in the network. Although much of the data mining and analysis may happen in a private data center or public cloud, the amount of relevant security information that can be pulled directly out of network routers, switches and network-deployed security tools is unmatched.
Also, remember that, for AI to be productive, it needs a constant stream of security intelligence to analyze. One of the best sources for security-centric data is the network itself. This includes flow data, deep packet inspection and other streaming network telemetry information that can be extracted, pooled and analyzed by a security-centric AI. That's another reason NDR systems are growing in popularity.
Finally, a network-centric deployment will give you the most bang for your investment buck. A network-based AI platform can quickly identify threats and mitigate risk for the corporate LAN, WAN and remote users and can even be deployed to monitor and secure public and private cloud resources. The network may not be the cheapest or easiest part of an enterprise infrastructure to begin using AI for cybersecurity tools, but it is the one area that clearly offers the most potential.
What are the risks of using AI in cybersecurity tools?
While AI's benefits largely outweigh its drawbacks in cybersecurity, risks do exist. The first involves properly maintaining AI so it aligns with emerging threats or changes to the physical or logical infrastructure architecture. For AI to work most effectively, security admins must shape and tune the underlying technology so that it understands what data is most important, what is considered legitimate versus questionable and which threats should be deemed most harmful to the organization. Without this type of AI care and feeding, an AI-centric security platform cannot meet its full potential.
Vulnerability is another consideration. Bad actors spend plenty of time trying to hack the same AI-based tools you've got on your infrastructure. While it's becoming more intuitive, cybersecurity AI is far more predictable than human intelligence. Hackers understand that, and it is one reason why they work so diligently to figure out ways to successfully bypass the AI you rely on. This makes layered security best practices, backed by both AI and human intelligence, so important to cybersecurity.
In many deployment cases, cybersecurity AI is also severely limited in terms of overall IT infrastructure visibility. Thus, decisions the AI platform makes based on this limited view may not be the best decisions from an overall business perspective. That means that, no matter how intelligent and automated your network infrastructure becomes, it's still best to have human security admins make the big decisions regarding security threat remediation strategies.
Finally, understand that, in using AI for cybersecurity, AI is only as intelligent as the data and threat information it receives. AI-backed cybersecurity tools must tap into the best local and global threat intelligence sources possible. That said, also understand that more data does not equate to better intelligence. Often, duplicate data can overwhelm and slow down AI. Instead, work to source only the most useful data from a wide variety of sources.
