As businesses are forced to navigate the ever-evolving digital age, their networks become more complex and widespread. Gone are the days of single locations and single devices, now we need access to multiple systems from multiple devices & multiple users. Sadly, most companies neglect cyber security and this amplifies the risks of them exposing their data and their client's data to hackers.
Data is now worth money so something as simple as a spreadsheet with internal documentation or a database with confidential client information is exactly the treasure trove hackers wants to gain access to.
The complexity of the scattered networks, on-prem, cloud, user profiles, and a large amount of devices makes it difficult to detect unknown attack patterns and identify malicious behavior. With the rise in cybersecurity threats, organizations struggle to secure their networks and monitor them effectively. Smaller companies often lack the resources to dedicate professional security experts to monitor their systems but even the biggest of companies can also struggle to get the right staff given the talent shortage in the field of cybersecurity.
To address these challenges, companies are going to have to adopt predictive analytics and Machine Learning (ML) as essential tools for securing their networks against cyber threats. It’s not enough to have a conventional firewall these days because social engineering can bypass all the hard work.
ML-driven network security solutions refer to the use of self-learning algorithms and other predictive technologies to automate various aspects of threat detection. The use of ML algorithms is becoming increasingly popular for scalable technologies as they can identify patterns, anomalies, and other subtle indicators of malicious activity, including new and evolving threats that may not have known bad indicators or existing signatures.
The drawbacks of rule-based security solutions highlight the significance of taking a more holistic approach to network security, which should nowadays include ML-powered Network Detection and Response (NDR) solutions to complement traditional detection capabilities and preventive security measures.
ML-powered security solutions are bringing about a significant transformation in network security by providing security teams with numerous benefits and enhancing the overall threat detection capabilities of organizations. With the ability to process vast amounts of data in real-time, including network traffic logs and endpoints, ML algorithms can identify patterns and anomalies that may otherwise go unnoticed.
ML-driven solutions learn from past events in order to continuously improve their threat detection capabilities, threat scoring, clustering, and network visualizations. They can also enhance incident response by automating certain aspects of the process, minimizing the time and resources required to address a security breach.
The future of network security is going to be more monitoring of traffic. It’s going to be a mix of things like deep packet inspection along with other SD_WAN features but it needs to be simple. In order to see widespread adoption of this tech we are going to have to see an extremely simple implementation of this extremely advanced technology.
Yes, the cyber security experts would like to see all the data but for the 99.9% of the population they are just going to want the very basic info like “Your phone has a suspicious app running and we think it is blank app”.
Maybe a router that has this built-in and can push notifications to your phone when it detects anomalies ;)
