The Growing Role Of Machine Learning In Cybersecurity

Machine learning is playing an increasing role in the deployment of effective cybersecurity tools. As cyberattacks continue to grow in sophistication, machine learning is going to continue to provide a responsive and powerful defense.

How Machine Learning Works in Cybersecurity

Machine learning (ML) is an example of artificial intelligence. It “learns” via algorithms that analyze data sets generated from past results. It also provides statistical analysis to help adjust processes and actions. The core of machine learning is really all about identifying patterns and deducing actions based on those patterns. This makes it a perfect fit for supporting cybersecurity tools. 

Early Detection

Machine learning quickly analyzes millions of files and data points and finds those that are potentially dangerous. Early detection tools equipped with ML allow you to identify threats sooner, so you can contain and neutralize them before significant damage is done.

Finding Vulnerabilities

Organizations need to be proactive about potential threats and machine learning helps make that possible. For example, ML can conduct penetration tests that simulate cyberattacks — identifying potential weak points in systems, firewalls, and networks. Machine learning can also run a simulated attack and use the results to make patches to applications, fix code, and recommend other solutions.

During simulations, machine learning will apply what it has learned from looking at historical data. It will then use that information to shape its approach to future scans and analyses to determine likely threats.

Reducing Costs

The cost of cybersecurity is an increasing burden on IT budgets and personnel. IT teams are responsible for keeping systems updated, educating others, and conducting tests to determine how effective cyber tools are at defending against attacks. They are expected to do so continually across monitor network perimeters, systems, and devices.

All of that work takes time and detracts from other IT work, especially in smaller organizations with fewer IT staff. Machine learning can help organizations reduce IT staffing needs and automate previously manual tasks.

Responding to Emerging Threats

Cyberattacks are constantly evolving, using new vectors and approaches to try and damage organizations. Behavioral analyses fueled by machine learning allow for faster identification of anomalies that could be the precursors of an attack.

Machine learning analyzes software behavior, making it a powerful defense against malware that often embeds itself in software applications used by your users. By detecting unusual activity patterns, machine learning algorithms can detect unusual patterns that could lead to a dedicated denial of service (DDoS) attack.

Real-Time Responses

The best way to protect data and systems is to prevent a cyberattack from inflicting damage. Machine learning makes it possible to send alerts whenever a threat is identified. In addition, they can be programmed to respond autonomously, bypassing human engagement and deploying patches immediately when an attack is identified.

Zero-day attacks can be particularly insidious, causing extensive damage before patches and other defenses can be deployed. One challenge is that most legacy malware detection systems require a negative action to be seen at least once. Threat exposure tools equipped with machine learning can identify previously unknown or unidentified threats and respond quickly.

Scalable Defense

Your data, applications, devices, and users may be spread over multiple locations. Identifying usage trends across large amounts of information or devices can be challenging. However, machine learning can do what humans cannot, providing reliable detection at scale and quickly.

Benefits of Machine Learning in Cybersecurity

Some additional benefits of using tools that leverage machine learning include:

  • Improved Overall Defense Posture. Organizations face increasing pressures to keep data and systems protected. A failure to keep data secure can have a devastating impact on the bottom line, with increased remediation costs, lost revenue, and reputational risk. There’s also the risk of failing to comply with increasing regulatory mandates to keep consumer data safe
  • Faster Response Speed. Machine learning reduces the time spent on identifying and responding to threats, with the ability to analyze massive datasets in seconds. They can also contain and neutralize threats and deploy patches far faster than humans can
  • Cost Reductions. With machine learning, your cyber defense will become more cost-effective. Machine-learning-based tools can replace legacy, outdated technologies and let you redeploy staff to more essential IT tasks

Today, the persistent threat of cyberattacks presents a formidable challenge to those who are charged with protecting technologies and data. Machine learning offers a proactive, responsive, and effective defense.

The SightGain Threat Exposure Management Platform utilizes the most advanced technologies to keep you protected. Learn more about how to operationalize threat intelligence and reduce your risk by scheduling a free demo.