Understand the top 4 use cases for AI in cybersecurity
AI applications in security offers organizations four unique benefits. Learn how machine learning advances can change industry approaches to threat detection and prevention.
Cybersecurity is perhaps the single greatest threat to any organization today. While hardly a challenge, the proliferation of systems, data, cloud technologies, apps, devices and distributed endpoints has only exacerbated cybersecurity threats. Organizations must work harder than ever to safeguard their assets and customers. This goes beyond automating reactive measures. It now requires infosec professionals to work toward proactive detection to preemptively avoid or thwart threats.
Companies have enlisted the aid of AI for security enhancement and protection of their business assets. Specifically, security software today uses machine learning, deep learning, machine reasoning and a host of related techniques to review massive amounts of data. The intent is to accelerate understanding of normal versus anomaly to detect malicious behavior and entities.
With global information security expenses expected to reach $170 billion by 2022, eyes are on the cybersecurity industry to innovate more effective, resilient mechanisms and tools. Thanks to advances in technology and techniques, there are four main use cases of AI and machine learning in infosec you can expect to see soon in an enterprise near you.
1. Network threat analysis
Companies today digitize more and more of their operations. They update old and develop internal -- often hybrid -- networks. These vast network topologies are not only complicated; they also require extensive network security resources to manage all communications, transactions, connections, applications and policies.
At enterprise scale, this amounts to enormous investments -- not to mention risks of error. AI in cybersecurity supports this grizzly challenge in a few ways. Significantly, AI in cybersecurity monitors all incoming and outgoing network traffic to mine for suspicious activities and classify threat types.
2. Malware detection
Malware is an umbrella term for an ever-evolving category of code or software that is intentionally designed to harm. While malware detection has been around for years -- often matching suspect code with signature-based systems -- machine learning is now shifting toward inference techniques.
In its analysis of massive amounts of data, event types, sources and outcomes, AI in cybersecurity detects the presence of malware before malicious files are opened. It also identifies types of malware. This is critical because malware continues to evolve alongside other advancements, from bots and botnets to malvertising, ransomware and beyond.
To date, the availability of tens of millions of labeled samples from both malware and benign applications have rendered this one of the most successful applications of deep learning and AI in cybersecurity. Well-trained algorithms rely on big, accurately labeled sets of data.
3. Security analyst augmentation
The enterprise hacker cat-and-mouse game represents an important and dangerous dynamic in cybersecurity innovation.
AI automates repetitive tasks. For example, it triages low-risk alerts or tedious data enrichment tasks in order to free up analysts for higher-value or strategic decision-making.
Machine learning raises the baseline of threat intelligence. As a result, human analysts start with higher-order threats, surfaced using machine learning to more rapidly analyze, curate, visualize and suggest potential actions.
Tests show that the ideal cybersecurity performance or accuracy is often a combination of human and AI -- not either alone. Augmented security tools will likely be essential for security teams in the years to come. In fact, some technology on the market already supports UI tools to enable cyberexperts to incorporate threat types to retrain machine learning models and configure specific fixes based on the problem.
4. AI-based threat mitigation
Cybersecurity technology and risks evolve in lockstep with AI. Today, companies must train machine learning algorithms to recognize attacks perpetrated by other machine learning algorithms. For example, hackers were discovered to have used machine learning to identify weak points in enterprise networks. They used this information to target points of entry via phishing, spyware or distributed denial-of-service attacks.
Other threat actors have developed smart malware -- or even artificial hackers -- to personalize attacks tailored to victims' specific contexts. AI-based attacks demonstrate AI's common value propositions: rapid scalability, behavioral analytics and personalization. These capabilities can be used nefariously in breaches, outbreaks or other security incidents.
The enterprise hacker cat-and-mouse game represents an important and dangerous dynamic in cybersecurity innovation. It remains critical that organizations wield investment to protect, especially as legacy systems cannot be easily updated or replaced.
The above use cases are but a few of the numerous applications for AI in cybersecurity. For all the potential, machine learning is not a silver bullet; it is a just a tool. And remember: Avoid silver bullet thinking, but consider the silver lining. Despite vendors' lofty marketing, the reality is that enterprise security landscapes are vast, dynamic networks. They must be constantly monitored, audited and updated based on ongoing unpredictable internal and external threat vectors. To define what is anomalous requires defining what is normal. This is extremely difficult, as computing and economic environments transform so rapidly.
While traditional signature-based methods of threat detection -- not to mention humans -- have blind spots, so too do machine learning techniques. Clear intention for application is paramount for any tool, and the output is only as good as the data input. Finally, as with any action-reaction, there is cause for optimism: Ever more sophisticated threats are sparking a renaissance of ever more sophisticated mitigation tools.
