While the latest iterations of AI-backed solutions hold massive promise, artificial intelligence does also play a part in improving cybersecurity efficacy already. Here’s how:

Even data breaches can be trendy. Exactly what information and systems hackers target — and how — is always changing. AI-based cybersecurity systems address this by accumulating new data through deep learning on both global patterns and industry-specific concerns. This data feeds automatic prioritization during crisis response.

AI systems conduct reviews of internal infrastructure boosted by threat pattern predictions to project risk levels for each segment of your infrastructure. This forewarning empowers organizations to plan accordingly and — critically — to allocate AI resources such as automated actions for immediate incident response.

When an attack is detected, three factors become vitally important: time to respond, prioritized responses, and execution. AI systems enhance all three of these by immediately initiating incident response, contextualizing response priority, and accelerating response beyond human levels to automatically execute optimized security protocols.

AI’s ability to explain conclusions, recommendations, and analysis makes it capable of improving both systems and people: the system AI is integrated into becomes more efficient and the security personnel who work with the system become more effective. A critical end result? Better communication of vulnerabilities to other departments, the impact, or ‘why’, of cybersec ops, as well as streamlining relevant info from stakeholders filing incident reports.

AI and ML are closely related, often overlapping, but machine learning is a distinct sub-field with its own discrete functions. The function of AI is, fundamentally, to accomplish tasks normally performed by humans, but with the speed and power of a computer. Machine learning, functionally, is the process of gaining insights by processing data that a human analyst can’t.

AI and machine learning software constantly monitors the behavior of a network. By doing so, it discovers what qualifies as standard vs. what is anomalous behavior. ML engines sift through massive volumes of data, discovering incidents — or the indicators of an impending incident — no human engineer could.

Machine learning can analyze web activity and predict “areas” of the internet that are high risk, such as sites that are more likely to be malicious. For example, with browser isolation technology, organizations can limit risk exposure by limiting users to viewing content but not sharing or downloading content on a site with high-risk qualities.

Algorithms are now able to form predictive models from massive amounts of malware data to detect entirely new threats. The benefits include identifying cutting-edge methods used to create hostile data files and projecting possible new methods based on the behavior and traits of known malware.

With more points of connection in a cloud system, enhanced monitoring is vital. Machine learning systems are able to analyze login activity and flag any logins or attempts that read as suspicious. This includes different elements of a login such as a geographic anomaly, or an atypical IP that can provide early warning before there’s an issue.

By sifting through encrypted traffic as it passes through a network, machine learning systems are able to spot malware moving through that traffic from prior deep learning processes. Another distinctly not-possible-for-humans ability, ML finds patterns in the traffic that indicate a threat rather than decrypting the data itself to search for possible threats.

While AI brings a mountain of benefits to cybersecurity operations, like any tool, its use is determined by the person wielding it. Cybercriminals also use AI-powered tools to launch attacks on IT systems. By utilizing some of the same enhanced output and data analysis that makes it a powerful staple of threat intelligence, bad actors can and do use AI to create methods designed to evade detection by more traditional security solutions.

Whenever a powerful defense is developed, work begins on an offense that beats it, and AI is no different. In this new era of AI-powered cybersecurity, AI systems use data to create countermeasures that more effectively neutralize cyberattacks — including AI-powered cyberattacks. Rather than confront these systems head-on, some attackers will attempt to corrupt the data that AI is trained with, leading to faulty parameters for the pattern recognition and threat assessment deep learning that an AI uses to conduct cybersecurity operations. This is known as data poisoning — and yes, it’s every bit as nasty as the name implies.

The final risk is a human one. Namely: underestimating the value of the human element. As we see with methods like data poisoning, our AI solutions need to be defended by cybersecurity professionals as much as the solutions protect us. While artificial intelligence and machine learning offer incredible boosts to security frameworks, they’re an enhancement, not a replacement.

To be absolutely, bet-your-bottom-dollar clear: AI is a permanent fixture in the cybersecurity landscape. From here on out, it’s only going to start taking up more real estate in that landscape. As businesses increasingly adopt cloud-based services and incorporate IoT devices, automating threat detection and prevention will become a do-or-die prospect.

Adopting AI technology into your cybersecurity infrastructure now will help your business stay ahead of the curve — ahead of when, in the not-so-distant future, it becomes a bare minimum requirement rather than a forward-thinking, proactive measure. With early adoption, you can dramatically improve your security posture and also get a headstart on familiarity with a soon-to-be mandatory element of any cybersecurity system.

But the juiciest benefit of early adoption is one that every security expert wants right now and every other stakeholder wishes they had after the fact: the chance to prepare for future threats that have not yet emerged.