Did you know that a couple of years ago, data surpassed oil to become the most precious commodity on the planet? The news and the underlying shift behind this “change” had implications for nearly every industry and economy, including cybersecurity. The importance of data (and access to it) meant cyberattacks could become significantly more sophisticated and diverse.
Once login credentials and financial details (like credit card numbers and PIN codes) were the things hackers were most interested in. Now, buyers in the black market pay significantly more for the medical records of people. Cyber attackers are even experimenting with unique attack vectors like corrupting data that is used to train a system (like an antimony laundering algorithm), so they can exploit the loopholes later. It’s called data poisoning.
The upshot is that cyber threats have become significantly more complex and sophisticated over the years, and cybersecurity needs to keep pace.
And that requires leveraging the power of AI.
How Artificial Intelligence Is Revolutionizing Cybersecurity
There are several different ways artificial intelligence is used to enhance/augment conventional cybersecurity. AI is no longer a buzzword in the cybersecurity sphere, and many companies (including IBM) are investing a significant sum in strengthening their cybersecurity with artificial intelligence.
A few ways AI already has or will be revolutionizing cybersecurity are:
1. Easing The Burden On Cybersecurity Teams/Professionals
The number of cybersecurity professionals is not growing proportional to cyber threats and the industry’s needs. This results in cybersecurity teams being overwhelmed by complaints and problems they need to investigate. One way they get around this problem is by automation, but rule-based automation has its limits, which are being tested in the current cybersecurity environment.
That’s where Artificial Intelligence becomes indispensable. By developing the right machine learning algorithms and training AI systems, cybersecurity experts can get through a lot more of their workload quickly and efficiently than they traditionally could by relying solely on rule-based automation. AI is also more efficient in consuming data, detecting patterns and anomalies, and can sift through, categorize, and shortlist cybersecurity threats that require human intervention.
This will ensure that the time and resources of your human cybersecurity experts are directed towards the most important tasks, and high-priority threats don’t get buried under menial tasks.
2. AI-Powered Identification, Behavioral Analytics, and Biometric
Humans tend to be the weakest link in the cybersecurity chain. They set weak passwords, use public WiFi without encrypting their data, and carelessly click on suspicious links. Cybersecurity has to step up to keep us, i.e., human users, protected from our own mistakes, and AI has been an important part of this job for a relatively long time.
Identification methods like facial recognition (all three flavors, i.e., image, video, and 3D) are usually powered by AI. It’s also used for strengthening the thumb scanner, an identification avenue where ironically, AI created a major problem (synthetic fingerprints). But the most significant impact of AI can probably be seen in behavioral analytics and behavioral biometrics.
Behavioral analytics and biometrics identify users using identifiers we don’t even consciously notice, like keystroke patterns, common typing mistakes, device usage patterns and timings, location data coupled with device activity, etc. The devices that leverage AI for behavioral analytics don’t just offer more security, but they also offer ease of usage. If your device is reasonably sure that you are using the device, it might accept the password even if you misplace a character (causing less friction in the interaction). On the other hand, if the device determines unusual activity or unknown behavior patterns, it might trigger 2FA to erect another security barrier.
3. Vulnerability And Intrusion Detection
AI-based vulnerability detection, investigation, and remediation tools, even though in their nascent phase, offer very promising results. And the main reason for this is AI’s flexibility and learning capabilities. No matter how intelligent and creative a human hacker/cybersecurity expert is, they are bound by the knowledge they possess and their skill set. AI-based vulnerability detection can (ideally) simulate attacks that might be inconceivable for humans.
AI can be used to augment already existing vulnerability detection tools and improve existing processes (by reducing the number of false positives, etc.). AI-based vulnerability detection and penetration testing tools can also cover more attack vectors and check how existing cybersecurity stands up against simultaneous attacks.
In the future, AIs might become evolved enough to detect subtle or even dormant threats. Many malware, especially the ones that can slip by your cybersecurity walls, don’t become active right away. Current cybersecurity tools are useless against dormant threats, but an AI-based intrusion detection tool might be able to identify patterns and vulnerabilities that conventional systems can’t and identify and eliminate dormant threats as well.
4. Police More Of The Attack Surface
The attack surface has increased exponentially, especially since corporate networks have to expand to accommodate remote connections, and it’s only going to stretch further with the advent of IoT. Most cybersecurity tools are not built for such large attack surfaces and might not be able to fully cover an expanded attack surface. AI-based tools, however, can police more of the attack surface.
Cloud is an area of cybersecurity vulnerability. According to a survey, 49% of cybersecurity experts believe that the number of cyberattacks via cloud services has increased, while 42% reported an increase because of IoT devices. A traditional tool might not have the flexibility and evolutionary “pace” to deal with these new and larger attack surfaces, but an AI-based tool might.
5. AI Can Learn In Real-Time
Most cybersecurity software is updated after developers learn about new threats and how they work, but they are useless against an ingenious attack as it’s happening. The AI can gap this vulnerability, thanks to its ability to learn and respond in real-time. An AI-powered cybersecurity system might not be able to understand the nature of the attack, but if it can identify and classify the intent of the attack, it might be able to stop the attack or prevent it from causing more damage.
But this will require a well-trained AI that has already learned from the data of several different attacks so that it can identify patterns and thwart an attack as soon as it’s identified.
6. AI For Safe Application Development And Open Source Code Screening
Vulnerabilities in software can come from a variety of different places. From human errors to open-source elements used in/connected with the source code, there can be several different exploitable vulnerabilities in a system. Some might be present since the inception of the code; others might develop over time (usually through updates).
That’s a very common developmental oversight, and even if the core software has airtight security, an update that’s linked to an un-vetted open-source code might be a hacker’s way in. The SolarWinds attack is a major example of how updates can be used to worm their way in.
This is something AI can help with. By using AI-based cybersecurity tools at the developmental stage or by running every update, every patch through them can ensure that the software or application you are developing is up to the mark.
Conclusion
Cyber attackers have been using AI and ML for a very long time to launch more sophisticated and insidious attacks. Reactive code that changes based on the response of the cybersecurity tools or a virus that specializes in slipping through vulnerabilities of a security system can bring a company’s IT infrastructure crashing down. The best way to defend against the sword of AI is to have the AI shield.