How Machine Learning Is Helping Prevent Data Breaches In Web Apps

Melkon Hovhannisyan is a tech entrepreneur and the CTO and cofounder of Direlli, providing outsourcing and outstaffing services.

As web applications become more sophisticated to meet our daily needs, such as shopping and communication, they also become more vulnerable to data breaches. In 2024, web applications were the target of nearly 50% of all data breaches, according to the Verizon Data Breach Investigations Report (DBIR).

Cybercriminals see our increasing reliance on web applications as an opportunity to steal sensitive data for financial gain and other selfish motives. Web application owners must invest in and integrate advanced technologies like machine learning into their security systems.

The use of machine learning in security started gaining popularity in the 2010s, thanks to advancements in cloud computing and big data. Today, machine learning is integrated into several security tools, including popular ones like Splunk and Microsoft Sentinel. Let’s discuss how machine learning is advancing web app security.

The Role Of Machine Learning In Cybersecurity

Machine learning-capable security systems use algorithms that learn from data to detect and respond to security threats instead of relying solely on predefined rules like traditional security solutions.

Here are some of the key advantages of ML-driven security systems:

• Proactive Threat Detection: This allows security systems to identify emerging threats before they cause harm.

• Faster Response Time: ML-capable security systems automate incident detection and response, reducing reaction time and the impact of any potential damage.

• Reduced False Positives: ML-capable systems learn to differentiate between normal and suspicious activities, which reduces false positives.

• Scalability: Security systems that use machine learning can analyze vast amounts of security data in real time, making them ideal for modern web applications.

• Adaptability: ML-capable systems continuously learn and evolve to recognize new attack patterns, making it harder for attackers to trick them.

How Machine Learning Helps Prevent Data Breaches In Web Apps

Threat Detection And Anomaly Detection

Modern security systems use ML algorithms to analyze user and system behavior to detect deviations from normal patterns. Changes in the behavior of the systems or users may indicate potential security threats such as unauthorized access, data exfiltration or DDoS attacks.

Some common examples of behavior changes that these algorithms look out for include:

• Unusual login patterns, such as logging in from a new location

• Repeated incorrect password attempts

• Sudden increase in data transfers

• A user accessing sensitive files they don’t usually open

• Running unusual command-line scripts

• A sudden surge in outbound traffic

• Abnormal interactions with APIs

Automated Malware And Vulnerability Detection

Security systems that use machine learning can also identify and classify malware, including new and previously unseen versions of the malware. To detect previously unseen malware, ML models analyze system behaviors to detect unusual occurrences such as high CPU usage, unexpected network traffic, frequent crashes and more. In addition to detecting previously unseen malware, ML systems analyze malware behavior, code and execution to classify threats and suggest responses.

Phishing Detection

Phishing is typically the initial step in an attempt to breach data. Machine learning enhances the detection rates for phishing by analyzing email patterns, URLs and sender behavior to identify phishing attempts. ML-powered tools like Microsoft Defender for Office 365 are used to analyze email patterns, attachments and URLs to prevent phishing attacks.

Automated Response And Incident Management

One of the ways web app admins can minimize the damage of a data breach in case it happens is by responding as quickly as possible. Security orchestration, automation and response (SOAR) platforms use machine learning for faster and more efficient threat mitigation. Modern SOAR platforms like Splunk Phantom use machine learning to:

• Isolate infected devices or block malicious IP addresses.

• Reduce response times by prioritizing critical threats.

• Lower false positives.

Limitations Of ML In Preventing Data Breaches

Data Quality And Model Accuracy

The effectiveness of ML models largely depends on the size and quality of the data set used to train them. Poor-quality or biased data can lead to inaccurate threat detection, making security systems unreliable. Popular security platform vendors such as Microsoft and Splunk generally have an advantage in this area because their tools have access to more data.

Balancing Automation With Human Oversight

While ML automates many security processes, human oversight is still necessary. Over-reliance on automation can lead to overlooked security risks or incorrect responses to some threats that might go unseen or be misinterpreted by ML systems that are always learning. Machine learning-powered security systems should be used as a tool, not as a replacement for security teams for web apps.

Adversarial Attacks And Evasion Techniques

Cybersecurity is usually a game of who is ahead (between attackers and the security teams). Attackers will always try and look for security loopholes in any system, including those that use machine learning. Today, attackers can manipulate some machine learning models by feeding them misleading data to evade detection.

False Positives And False Negatives

It is common for ML models to generate false positives or false negatives. Too many false positives can overwhelm security teams, while false negatives can lead to undetected breaches. To minimize false positives and negatives:

• High-quality and regularly updated data should be used to train models.

• Optimize models with fine-tuning and ensemble methods.

• Implement adaptive learning with feedback loops.

• Balance detection sensitivity with accuracy.

High Computational And Implementation Costs

Training and deploying machine learning-based security solutions requires significant computing power and expertise. Security platform vendors will always pass these costs on to the end users. This makes modern security tools that utilize the latest and most powerful models a huge expense that small and medium-sized businesses may struggle to afford.

Final Thoughts

AI and machine learning have gradually become a core part of several security tools over the last 15 years, with many platform vendors integrating these capabilities into their solutions. As web applications become more sophisticated and handle more sensitive user data, there has never been a better time for them to utilize these modern security tools. Despite the limitations discussed in this article, ML-powered security tools are still a much better option than traditional security solutions that rely on pre-configured rules.

Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?