Machine Learning Meets Cybersecurity
By Krystal Rennie / Adlumin, Inc.
The healthy and transformative relationship between Machine Learning (ML) and cybersecurity continues to unfold in front of our eyes. As our world becomes increasingly digital, the demand for ML and cybersecurity has peaked. We often hear the two topics discussed separately, but they are, in fact, more robust and stronger together. As a result, they have emerged from the world of technology as one of the most influential couples within the last few years.
We know that cybersecurity solutions are being implemented across multiple industries worldwide. As you continue to read, you will see how Machine Learning plays a significant role in making that happen.
Machine Learning Attracts Cybersecurity
Here’s the big question: How do these two single elements come together and make such a blazing match?
On its own, ML is often associated with artificial intelligence, but the truth is that it is a separate concept with its own science and algorithms. SAS describes Machine Learning as “a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.” ML can be actively involved in various industries, including government, retail, healthcare, oil and gas, and luckily for Adlumin customers, financial services.
The SAS article also mentioned, “banks and other businesses in the financial industry use machine learning technology for two key purposes: to identify important insights in data and prevent fraud. The insights can identify investment opportunities, or help investors know when to trade.” ML plays a critical role in keeping bad actors out of your networks, specifically when paired with cybersecurity, by pinpointing warning signs of risk and fraud behavior. Machine Learning is responsible for continually analyzing massive amounts of data in ways humans simply cannot. When it comes to security, this is critical for the productivity of IT teams.
A Healthy Relationship: Machine Learning and Cybersecurity
The chemistry between ML and cybersecurity was strong from the beginning, and their relationship continues to mature as the digital world expands and changes. Cyber Ventures predicts that by 2022, there will be 6 billion internet users, increasing to 7.5 billion internet users by 2030.
Machine Learning is and will remain an essential component of protection against hackers. According to Security Boulevard, “Machine Learning in Cybersecurity puts a defense system at an advantage over others. Helping systems adapt with intelligent updates and corrections is invaluable protection against malware and ransomware at the beginning and endpoint stages of cybersecurity.” ML helps cybersecurity solutions develop a deeper understanding of patterns, information, and errors that occur within networks by highlighting the vulnerable data at risk.
Also mentioned in the Security Boulevard article, the following are a few benefits ML offers to cybersecurity:
- Application security
- Endpoint protection
- Fast analysis of large amounts of data
- Fast and effective data monitoring
- Hands-off scanning for potential data breaches, malware, and more
- Writes SIEM rules
These are just a glimpse into the positive outcomes of this connection. ML and cybersecurity are a team; they are better together than apart. Simply put, ML needs cybersecurity to perform its tasks correctly and vice versa. As the cybersecurity industry continues to battle against hackers, machine learning remains its backbone by providing the support, tools, and algorithms necessary to ensure networks and data remain protected.
Keeping the Flame Burning
Since meeting, ML and cybersecurity have blazed a new path for industry solutions. When the right ML algorithms are paired with the proper cybersecurity tools and processes to suit your organization’s needs, it cuts down the workload for your IT team, minimizes human error, and is an excellent allocation of your security budget.
The future of the relationship between ML and cybersecurity lies within its ability to keep both individuals and companies safe. Where ML is a must-have feature for a streamlined security presence within your organization, a cybersecurity platform is the glue that will hold it all together. This relationship is 50/50; the two ignite each other to properly execute specific tasks and ensure threat detection and network protection remain top priorities. Machine Learning, when paired with your organization’s cybersecurity solution, is simply the perfect match to spark success.