The Role Of AI in Cybersecurity
Working in cybersecurity sometimes feels like a losing battle. Between the vast amounts of threats reported by ongoing monitoring systems and the limited hours security personnel have to respond to those threats, cybersecurity defenses are often lacking in some way. Cybercriminals are becoming so sophisticated that a growing number of cybersecurity defenses are becoming obsolete. That’s why a growing number of professionals are using AI in cybersecurity to reduce threats and protect digital assets.
Read on to learn about the role of AI-based solutions in cybersecurity and determine whether an AI solution might bolster your organization’s cybersecurity defenses.
The Increasing Importance of AI in Cybersecurity
Cybersecurity professionals are always on the hunt for technology that will make their jobs more efficient. After all, hackers are getting smarter and faster every day, and it falls on cybersecurity professionals to find unique ways to keep up. Artificial intelligence cybersecurity solutions offer one more tool in the cybersecurity toolbox, empowering professionals to assess threat levels, make informed decisions, and reduce the rate of security incidents.
AI in cybersecurity matters because it can simply do more at a given time than human cybersecurity professionals. For example, cybersecurity defenses can monitor and detect threats in real time, perform behavior analysis, and even make predictions based on unusual patterns. This level of threat hunting simply isn’t possible for existing cybersecurity teams.
Key Benefits of Incorporating AI in Cybersecurity
AI-based systems provide several key benefits when incorporated with existing cybersecurity frameworks. Human analysts can use AI in cybersecurity to improve threat detection, respond to incidences faster, reduce false positives, and enhance overall security analytics.
Improved Threat Detection
AI-powered systems are capable of monitoring for threats in real time. Human analysts can train neural networks to recognize normal patterns of system behavior and suss out potential threat actors. And because cybersecurity with AI can parse through vast amounts of data in a short span of time, AI in cybersecurity can detect threats much more quickly than even the most seasoned security analyst.
One of the distinctive features of AI in cybersecurity is anomaly detection. This means that cybersecurity AI notices when online behavior falls outside the realm of normal. By noticing anomalies early, AI in cybersecurity can reduce harmful behaviors before they have a major impact on digital assets.
Faster Incident Response
Every moment counts when it comes to responding to cybersecurity threats. The more time a system’s network is down, the more money the attack costs the business. According to a 2023 report by IBM, in the US, the average cost of a data breach is $4.5 million. More than half of small businesses are forced to shut down within six months of a security breach.
Artificial intelligence cybersecurity is capable of noticing breaches significantly faster than human analysts. Also, because AI employs continual monitoring technology, it can often pinpoint where the issue is. This allows cybersecurity professionals to respond precisely, reducing downtime and limiting the costs of cybersecurity breaches.
Reduced False Positives
False positives happen when continual monitoring systems falsely flag something as being suspicious when it’s not. And while it can be tempting to think that it’s better to have false positives than not to catch threats when they happen, this isn’t always the case. Research shows that too many false positives can lead to a phenomenon known as “alert fatigue,” and alert fatigue can desensitize cybersecurity professionals to actual threats, leading to slower response times when it actually matters.
AI and cybersecurity teams can work together to reduce these false positives and combat alert fatigue. Pattern recognition combined with contextual analysis primes artificial intelligence cybersecurity systems to be much better at recognizing legitimate threats than other forms of monitoring software. AI systems can also be paired with threshold adjustments to make them more or less sensitive to anomalies. Analysts can use this smart technology to ensure they’re alerted about the most important threats while limiting false positive alerts.
Enhanced Security Analytics
AI in cybersecurity can lead to enhanced security analytics for a number of reasons. For one, NLP (natural language processing) software is capable of parsing through large sections of unstructured data to look for trends, which it can then summarize for data analysts. This enables analysts to get a better handle on the qualitative data coming in at any given time.
AI is also capable of deep packet inspection. This means that it can analyze network traffic content for malicious activity, which traditional signature-based systems may miss.
Finally, continual monitoring can lead to continual reporting and analytics. This means that analysts can access information from all times of day and all days in a subset of time. Cybersecurity professionals can make more informed decisions based on more data points than if all data-gathering was happening manually.
Real-World Applications of AI in Cybersecurity
Cybersecurity with AI isn’t an abstract concept. It has numerous real-world applications that many major brands are using regularly. This includes AI-powered endpoint security, behavioral analytics, threat intelligence, and automated security patching.
AI-Powered Endpoint Security
Currently, the majority of cyberattacks are driven by human errors. Endpoint security limits these human errors by protecting points of entry — the places where cyber attackers can enter the network. This includes personal devices like laptops, tablets, and cell phones.
AI-powered endpoint security uses machine learning to protect these endpoint devices more effectively than human cybersecurity professionals can do on their own. It does this through a combination of behavior analysis and predictive analysis to prevent attacks, as well as remediation technology to quarantine compromised endpoint devices.
Some AI devices also have UEBA (User and Entity Behavior Analytics). This unique technology analyzes the behavior of endpoint devices in conjunction with endpoint users, which can help detect insider threats and add an extra layer of security to your system.
AI systems collect data from a variety of sources, including endpoint devices, network traffic, and system logs. Systems can then combine this information with collected data regarding human behavior, including user activities, system activities, and application interactions.
By comparing these swaths of data, AI systems can create models of normal behavior for a given organization. Over time, machine learning causes these models to become increasingly accurate.
These models then enable AI cybersecurity systems to notice patterns, single out anomalies, and make predictions about potential threats based on these anomalies. This level of behavioral analytics improves threat detection and reduces both false positives and false negatives.
Threat intelligence is all about predicting the behavior of cyber attackers by understanding the tactics, techniques, and procedures that threat actors use. Threat intelligence also includes information about known vulnerabilities and their associated patches. The more information cybersecurity professionals have about potential threats, the better they can defend their organizations against these threats.
AI improves threat intelligence by monitoring incoming information about vulnerabilities and threats. Because AI systems can comb through large chunks of data at a time, they can stay on top of threat research more efficiently than individual cybersecurity professionals can. This gives cybersecurity professionals more information about potential threats in a more timely fashion, providing plenty of time for them to design a security bulwark to keep major threats out.
Automated Security Patching
When major systems experience vulnerabilities, professionals design security patches to prevent these vulnerabilities from impacting too many companies. Unfortunately, all too often, cybersecurity professionals don’t know about new security patches or don’t have time to install those patches.
AI improves security by installing security patches automatically after they are released. Because AI operates behind the scenes, it can even do this during the hours the business isn’t otherwise operating. This means less downtime during business hours and improved security protocols year-round.
How To Implement AI in Cybersecurity
Implementing AI in cybersecurity is an ongoing process. It starts with choosing the right AI cybersecurity provider to work with. Once you’ve decided on the AI technology you want to work with, you must decide how to integrate that with your existing cybersecurity protocols.
This takes foreplanning by your cybersecurity team. Team leaders will need to evaluate their pain points and determine how they want to use AI systems to address those pain points.
Next, they’ll have to take some time to teach their AI system about their organization. This involves providing data so the AI’s machine learning technology can get a baseline for your organization’s normal activity levels. Once it has this baseline, AI technology can begin scouting for anomalies in real time.
Finally, team leaders need to train their teams to work with AI technology. This includes explaining how to adjust the AI’s parameters to improve threat assessments. Remember that adjusting AI to your business’s needs can take some time. Over time, your AI system will become a trusted, automated tool in your team’s cybersecurity arsenal.
Strengthen Your Processes With NLP and AI
With cybersecurity threats on the rise, there’s no better time than now to improve your cybersecurity systems and processes. Artificial intelligence and natural language processing can help you stave off major threats.
Ready to take the leap? Request a demo to get started today.