Machine Learning and AI in Application Security
Artificial intelligence (AI) and machine learning (ML) are some of the most common and enduring buzzwords in the technology sector. Every few months or years, a new company announces that they’ve found a way to use AI and ML to provide a solution to some pressing problem.
While many of these miracle cures should be taken with a grain of salt, AI and ML-based solutions have matured significantly in the last few years. These technologies are very good at large-scale data processing and pattern recognition, meaning that, implemented properly, they can do some amazing things within a certain field.
One area where AI and ML have been applied with some success is application security. An organization’s web applications are often the first target of hackers, who take advantage of the fact that they are publicly exposed but often also have direct access to an organization’s store of sensitive customer data.
By targeting these web applications, attackers have a chance of breaking through your website and stealing an organization’s sensitive data, so these applications are often targets of a variety of cutting-edge new attacks. As a result, it’s essential to keep your website safe. Then, a lot of work has gone into training AI and ML systems to identify and block attempted exploitation of vulnerabilities in these valuable resources.
Applying ML and AI to Cybersecurity
Artificial intelligence and machine learning are new technologies that have potential in a variety of different fields. However, one field where they are receiving a lot of attention and investment is cybersecurity.
Cybersecurity is a field that is simultaneously facing a shortage of skilled practitioners and massive growth. As the number of cyberattacks grows, cybersecurity professionals are becoming increasingly overworked.
As a result, the potential for machine learning and artificial intelligence to lighten the load is a promising one. Two places that AI and ML are being applied to cybersecurity are alert triage & analysis and the detection of zero-day attacks.
Alert Triage & Analysis
Most cyber defense systems are designed to perform monitoring and generate alerts if anything suspicious and potentially threatening is detected. Once these alerts are generated, it’s the job of a cybersecurity analyst to triage them based upon their potential severity and determine whether additional analysis and investigation is necessary for the potential threat.
While this system works in theory, in practice analysts are drowning in alerts. The average enterprise has tens of thousands of alerts each and every day, and analysts are expected to look at each one and determine if it is an actual threat or a false positive.
And humans are really bad at this type of work. We get alert fatigue, meaning that we get bored and miss things that we would have caught if we were fresher. Additionally, every minute spent triaging and rejecting a false positive alert is a minute that could have been spent investigating a real threat. With the manpower shortage in cybersecurity, this means that real threats make it through an organization’s cybersecurity defenses.
This is where AI and ML have the potential to make a real difference. While currently AI and ML are in their infancy and can’t always be trusted to accurately determine whether or not an alert represents a real threat, this will change in the future. By using AI and ML as a first line of defense when dealing with alerts, organizations will be able to focus their limited manpower on those events most likely to be an actual threat to the business and in need of a rapid response.
Zero-Day Detection
A zero-day attack is one that exploits a previously unknown vulnerability. Skilled hackers commonly search through commonly used applications for exploitable vulnerabilities that can be used to slip malware onto a target computer. These vulnerabilities are often hoarded until the hacker finds a target worth the expense of “burning” a zero-day.
Zero-days are considered “burned” after use since many anti-malware systems are signature-based. This means that, once a malware variant is detected, analysts decide on a signature that uniquely defines it and send that signature to antivirus systems. The next time that malware sample tries to infect them, they can identify it using its signature and block it.
The problem with signature-based detection is that a signature can only be developed once malware exploiting a certain vulnerability is used. Artificial intelligence and machine learning can help with detection of these zero-day attacks since they can effectively detect malware using anomaly detection.
Instead of malware being detected because it matches a known signature, anomaly detection identifies it because it is something abnormal for the system. AI and ML are capable of collecting and processing massive amounts of data to extract patterns, making them ideally suited for anomaly detection-based malware identification.
Protecting Your Software
The state of artificial and intelligence is evolving rapidly. As a result, some organizations have incorporated AI-based solutions into their cyber defense and threat detection products.
This can prove to be a huge advantage for an organization due to the capability of these systems to detect threats that would otherwise be missed. Whether by freeing up skilled personnel by reducing the load of routine alert analysis or helping to identify zero-day attacks before they compromise a system, AI and ML can make a serious difference in protecting an organization’s network.
When selecting and deploying an application security solution, picking one that leverages AI and ML for attack analytics and detection can significantly improve an organization’s cybersecurity threat readiness.