Beyond Human Capabilities: How AI Is Shaping the Future of Cyber Threat Detection

Cyberattacks are at an all-time high, and there doesn’t seem to be any sign that they are slowing down. Most businesses' expanding attack surfaces are rooted in cloud technology and the Internet of Things, which combine remote workforces tied to shared data and resources.

It’s no surprise that security is turning to AI to power threat detection across all these disparate assets and systems. Artificial intelligence has entered the gap between data, assets, and users, providing advanced threat detection across all attack surfaces.

AI In Threat Detection

Traditional threat detection methods have historically relied on a combination of monitoring and human analysts who can check for behavioral patterns, identify suspicious activity, and field appropriate responses to incidents.

But, in a world where security professionals may only have a few seconds to prevent a threat, this approach can add a lot of overhead that could hinder a security team’s response.

AI automates this essential process. Leveraging machine learning tools, AI systems can parse through tremendous amounts of data and identify behaviors that humans won’t catch.

For example, consider traditional firewalls. These perimeter security solutions often allow or deny web traffic based on pre-written rules that have to be programmed before or during implementation. Intelligent firewalls driven by AI can adapt to different situations and create rules based on traffic behavior. This means that the firewall can be much more responsive to real-world situations, identifying threats faster and allowing an organization to stop a breach before it even happens.

Is AI Surpassing Traditional Threat Detection?

The short answer is yes.

The longer answer is that traditional methods rely on human analysts to monitor systems and make decisions about potential anomalies or suspicious activities. In a sense, this approach could only be reactive, meaning that security experts were more often than not on poor footing trying to respond to threats after they had already happened. This resulted in:

• Slower response times
• Delayed traffic analysis and threat identification
• Analyst overwhelm due to the overwhelming amounts of data

AI mitigates these issues specifically because it can sift through massive amounts of data in real-time, well beyond the capacity of a human analyst. More importantly, AI can do so without compromising effectiveness. It can:

• Use traffic behavior to identify anomalies in massive data streams and identify potential attacks while they happen.
• Rank different types of threats based on behaviors and attack surfaces, suggesting specific courses of action for different priorities.
• Launch attack mitigation measures without manual input from a human analyst to stop threats as they emerge.

Analysing Big Data and Smart Threat Detection

Modern IT systems produce and traffic in extremely large amounts of data--a reality that can thwart any security team. AI, however, is purpose-built to handle this information easily due to the fact that it is embedded within the systems it protects.

For example, an AI embedded into a network security tool directly analyses data in real-time, across thousands of devices spanning routers and modems, workstations, mobile devices, and IoT systems. This AI is able to notice even the slightest deviation in normal network behavior. So, if a local device starts rapidly communicating with several outside IP addresses, the AI can immediately notice and respond. Or, if users begin to access data outside business hours or from non-work locations, the AI can raise alarms about a potential breach or insider threats. In contrast, this work might call for hours or days of investigation and forensics from a human analyst.

More importantly, AI learns from its interactions. As it ingests data and responds to threats, it also notices trends, behaviors, and normal baseline activities to adapt to new threats. This helps address shifting tactics in areas like phishing attempts or social engineering.

Predictive Analytics: Looking to the Future

AI isn’t a new technology; businesses have been using machine learning to conduct big data analytics for the past decade. This fact means that cybersecurity benefits from the analytical capabilities of advanced AI, especially when it comes to predictive modeling.

We mentioned how AI learns. When it comes to predictive analytics, this “learning” becomes a powerful tool in frontline threat mitigation and long-term strategic planning.

• As an AI builds predictive models, it can make suggestions regarding emerging threats and the best approach to stopping them.
• Additionally, AI can deliver analytics and reports to human operators in charge of high-level strategy to help them better understand the threat landscape and how to adapt to it.

Challenges in AI Threat Detection

It’s clear that AI gives security professionals the edge they need to address evolving challenges. That doesn’t mean that it doesn’t come with its own set of challenges:

• AI can still return high rates of false positives, which leads to alert fatigue for human security teams, who start to see the AI as a chicken-little situation.
• AI must constantly be trained, even when implemented. This means having an internal or third-party team consistently working on machine learning algorithms with large training sets to keep the AI up-to-date against modern threats.
• Poor training data and false positives can skew AI responses, making them less effective. Without the right training sets or algorithms, AI may develop blind spots that cause it to miss certain types of attacks at the expense of others.

Where is AI Being Used in Threat Detection?

As AI becomes the norm in cyber threat detection, it might be better to ask, “Where isn’t AI used in threat detection?”

Some of the more prominent examples of this technology include:

• Microsoft: Thousands of businesses rely on Microsoft cloud services to run critical parts of their businesses, which means they can leverage Microsoft Copilot for Security. This tool is a must-have for Azure users, as it connects their cloud resources to AI-driven threat detection informed by internal SIEM and SOAR solutions.
• IBM: The Guardium suite of tools uses AI to address several ongoing threats, from data breaches to cryptographic attacks. This includes the Quantum-Safe tool, which uses AI to prevent attacks from the rise of quantum computing.

This is just a tiny sample of how AI is being used. Literally, hundreds of security companies are developing applications that use AI to help mitigate threats with a comprehensive combination of behavioral analysis, analytics, and intelligent, predictive mitigation measures.

What Is the Future of AI in Cybersecurity?

Security AI has seen continuous improvements over the years in areas like natural language processing and deep/reinforcement learning models. This will lead to better data analysis and more refined responses with less and less direct human intervention.

Other trends coming to the industry are a focus on integration, with AI embedded into more comprehensive security suites that include or connect to SIEM, SOAR, or XDR solutions. This provides a much more holistic view for AI to coordinate its efforts and links critical monitoring and response services to create a cohesive security environment.

Also, predictive analytics will only continue to grow, and improvements in predictions and responsive mitigation efforts will shorten response times. When attacks happen, especially in massive industries like finance or utilities, an attack can breach a system in seconds and cause billions in damages, and AI trained to act immediately can reduce performance bottlenecks associated with human response efforts.