Advanced Techniques in Automated Threat Detection

In the fast-paced and constantly evolving digital landscape of today, bad actors are always looking for newer and better methods to launch their attacks. As cybercriminal tactics evolve, they develop more sophisticated malware, more convincing scams, and attacks that are designed specifically to evade known security measures.

With this in mind, it is vital for organizations to invest in more advanced automated tools and solutions to go “from threat identification to eradication and remediation with as few humans in the loop as possible.” Taking advantage of emerging technologies and sophisticated measures can aid organizations in automating these processes to an extent and saving time, labor, and other resources that can run thin when relying solely on humans to handle threats.

How Traditional Threat Detection Methods Fall Short

There are a number of reasons that traditional techniques for threat detection fail to fully account for modern threats.

• The growth in popularity of cloud solutions for storage and other processes makes it harder to detect threats, as the “castle and moat” style of threat protection is no longer effective when the perimeter is more nebulously defined.
• An increase in the last few years of employees in remote or hybrid working environments means that the attack surface is much larger and more spread out, hindering the potential for threat detection.
• Many traditional security tools produce a large number of alerts, making it nearly impossible for human security or IT teams to sift through the noise to locate legitimate threats.
• Even disregarding the volume of false positives, there are simply too many security events for human teams alone to investigate and respond to every alert.
• These solutions often fail to fully address evolving and emerging threats, which can make use of advanced tactics to circumvent security tools and prevent detection.

The convalescence of all of these factors has made it increasingly clear in recent years that modern threats require modern security tools.

Evolving Tactics and Technologies for Automated Threat Detection

The primary areas that security teams wish to automate include the generation of detection content and tuning of existing detections. Generating detection content consists of building rules for what constitutes a threat and triggers an alert, while tuning refers to the adjustments to rules and alerts to fit the needs and abilities of your organization.

While many tools these days come with detection rules out of the box, the tuning process can be complex and touchy, carrying the risk of mistakenly disabling crucial security rules. Both of these processes can be automated with the use of clever code and even LLMs.

The Role of AI and ML

According to a CNN report, 61% of large firms in the United States plan to use AI to automate tasks traditionally done by humans in the next year. Artificial intelligence and machine learning should not be the sole foundation of any security strategy, but they can have their place in automating certain processes. As AI has gained traction for personal and business purposes, cybercriminals are taking advantage of it for nefarious purposes, and organizations can wield the same technology to defend against their attacks.

Because AI and ML are complex and imperfect tools, it is important for organizations to understand the need for reliable solutions that do not rely entirely on AI. Some of the current and future use cases for AI in threat detection and response include:

• Using LLMs to generate rule content with guided prompts to add to out-of-the-box rule configurations
• Building investigations and putting together dynamic plans for incident investigation
• Reducing the workload for human security teams by analyzing large volumes of data to identify potentially risky anomalies
• Behavioral analysis based on a detailed understanding of the baseline network and application traffic in order to detect anomalous actions that may indicate a threat
• Dynamically adjusting and adapting security measures to meet new and evolving threats

While some of these technologies are not yet refined enough to be fully effective, these are all factors to keep in mind when looking to the future.

Conclusion

Organizations looking to implement advanced techniques for automating threat detection should keep in mind a few important things. Automated threat detection can only work if it is based on secure and reliable technology and deployed and managed responsibly. Reducing the workload for your human teams is great for handling an overwhelming amount of security tasks, but organizations are ultimately accountable for their security processes, so doing your research and investing in quality solutions from trustworthy vendors is crucial.

Automation of certain onerous processes can be a boon for organizations, especially ones that may lack the internal staff or resources to manage cybersecurity effectively. With the right tools and practices, organizations can benefit from the use of automation through techniques including coding and making use of AI and ML.