With the rise of technology comes the rise of threats, of course. As new firewalls and other protections are put into place, criminals come up with new methods to take down those protections. Such is the nature of innovation. For this reason, it’s worth taking a look at how AI and automation are changing the cybersecurity landscape. With this information, you can ensure you have the best protections in place and are aware of security weaknesses.
Advanced Threat Detection
One of the most common ways automation and AI are used in cybersecurity is through advanced threat detection, or ATD. Because hackers can be relentless in their pursuit of data and secure information, any lapse in security creates a vulnerability. In short, you can no longer rely on a simple firewall, which can be broken down. Now, you need round-the-clock, automated protections supported by highly advanced AI.
Fortunately, tech companies have now made it possible to put an endpoint protection platform and an endpoint detection response platform into place. EPPs, rather than offering a single protection, are a collection of security measures that include antivirus, antispyware, personal firewalls, and data loss prevention, among others. When combined with EDRs, which are more proactive in the finding and fighting of threats, EPPs offer a strong security solution.
Real-Time Monitoring
There’s no viable way to have humans monitoring all the threats that may come in through cyberware at all times of day and night. It’s not like having a security guard walk around an office building. Large amounts of data are constantly streaming across the cloud and the internet. You’d need an unfathomable number of people to monitor that data to keep it safe. Never mind the fact that 95% of cybersecurity breaches are caused by human error.
Instead, you need a technological solution to a technological problem. That’s where AI and automation come in. Thanks to machine learning, or ML, AI can be trained to monitor massive amounts of data for security breaches and potential threats. Algorithms can continuously process data streams, recognize patterns, and identify suspicious activity. And because many of these tasks are repetitive, they can be automated.
Incident Response
One area that is relatively new to cybersecurity is AI’s ability to respond to incidents. In earlier days, AI was primarily passive in terms of cyberattacks. It could detect the attack and send out a report or alert to a human who could then respond. While this passive, monitor-only approach is helpful, it is also costly in terms of time and money. Waiting for a human response can allow for intensified attacks and data breaches.
Now, an attempted cyberattack can trigger an AI response that will recognize the attack and take action to shut it down. This response can even be an automated counterattack of sorts. AI can defend against the breach and shut down systems that could be vulnerable to intensified attempts. What’s more, some AI agents are empowered to present a counterattack and seek to disable the attacker, rendering it harmless, at least for a time.
Vulnerability Management
Finally, AI offers vulnerability management. You may have heard it said that there’s no better offense than a good defense. That’s where AI comes in particularly handy. By the time an actual cyberattack occurs, the technology that stages that attack has already monitored and tested the protections in place, seeking vulnerabilities. Once the attack occurs, the attacker is confident of securing a successful breach. While you may have strong defenses in place to stop the attack, it would be far better to prevent it from happening in the first place.
Highly trained AI and proper automation can act as a kind of simulated attacker, constantly testing your systems for vulnerabilities. Approaches like intrusion detection systems will monitor for suspicious activities. At the same time, anomaly detection draws on artificial intelligence to identify any abnormal behavior in the system that might signal a potential threat. Automated vulnerability scanning and intelligent exploitation will simulate cyberattacks in order to prevent them.
False Positives, Skill Gap, and AI-powered Attacks
Still, AI and automation are not perfect. There are bound to be problems as long as AI continues to need to learn, adapt, and adjust to errors, both technological and human. The biggest issues lie with undertrained or poorly designed AI security systems. AI agents can trigger false positives, generating alerts without need. These end up wasting time and energy as shut down systems need to be rebooted and any interruption in service must be addressed.
Furthermore, AI can have skill gaps, which are dark areas of knowledge and expertise within the technology that leaves your system vulnerable. Skill gaps can result in missed opportunities for prevention and data breaches. The solution to this, and to false positives, is to more finely tune and better train AI to live up to its potential. Then, of course, there’s the issue of AI-powered attacks. If there’s a technology to protect, there will be technology to attack.
In the end, the best the cyber tech world can do right now is continue to advance technology and train AI better. AI and automation are now crucial tools in the world of cybersecurity, so software development companies must continue to rise to the occasion. As attackers become smarter and more sophisticated, cybersecurity AI must leapfrog past them to prevent and protect in ways that can be automated. Fortunately, the cybersecurity world seems to be on top of it.
