Artificial intelligence and machine learning have the power to change our technological capabilities significantly, and, in many ways, they already have. Think of retail brands utilizing chatbots to facilitate customer service or farmers using sensors and ML-based software to monitor their soil and maximize their harvest.
The strength of ML- and AI-based applications is that they allow individuals and organizations to put large volumes of data to good use – identify patterns, uncover insights, and make recommendations.
Every industry – retail, agriculture, banking, etc. – is increasingly paying attention to the increasingly accessible capabilities of AI- and ML-enabled technologies. Cybersecurity is no exception. If anything, it’s at the forefront of integrating cutting-edge technologies. The global AI in the cybersecurity market was valued at $12 billion in 2020. It is expected to reach $30.5 billion by 2025.
These technologies have become an essential part of risk monitoring solutions due to their ability to identify risks proactively. And, when it comes to endpoint security, the role of ML and AI will continue to grow exponentially.
It is no longer enough to install anti-virus software on all desktop computers in your office. With the rise of BYOD and millions of people switching to working from home due to the coronavirus pandemic, organizations need to pay close attention to how endpoints interact with a network. Endpoint security widens the physical perimeters of cybersecurity and provides more comprehensive protection from malicious actors.
Endpoint security is a cybersecurity framework that focuses on securing end-user devices: mobile phones, desktop and laptop computers, IoT devices, etc. These devices are the endpoints of a network in that they provide access to the network. As a result, they present a lucrative target for malicious actors. Endpoint security secures these points of entry and protects them from cyberattacks.
Endpoint security solutions monitor and assess every file that enters a network. They secure devices that access a network through encryption and application control. By encrypting data on endpoint devices, endpoint security software protects these devices from data breaches. Meanwhile, application control monitors users’ interaction with applications and blocks unauthorized access or suspicious applications from being deployed onto a device.
System administrators have an overview of the performance of endpoint devices through a centralized console. Endpoint security solutions allow them to control each device remotely, including installing software patches and updates.
The short answer is a whole lot!
Artificial intelligence and machine learning exponentially boost the capabilities of endpoint security solutions.
- AI- and ML-powered endpoint security software can take advantage of the mountains of data already available on different types of malware, attack strategies, and behavioral patterns to maximize the precision with which incoming threats are predicted, detected, and eliminated.
- ML-enabled solutions can collect and analyze historical data – geolocation, login time, application usage, etc. – and spot suspicious behavior, as well as derive risk scores.
- Feeding an ML algorithm data on what constitutes good vs. corrupt data allows ML-enabled solutions to spot file-based malware and neutralize it before it manages to breach a network.
- Machine learning can take much of the routine incident analysis off the IT security team’s hands, allowing organizations to lower operational costs.
- AI- and ML-enabled technologies can discover and classify sensitive data and make sure that their usage and management complies with relevant regulations, be it HIPAA, GDPR, or others.
- Finally, AI and ML can help optimize IT asset management by automating many (if not most) of the manual activities and identifying opportunities for improved efficiency.
Soon, AI- and ML-powered cybersecurity is not going to be optional for organizations. Malicious actors – be it a nation-state, hacker group, or individual – increasingly utilize AI and ML capabilities to unleash sophisticated attacks on organizations. For example, ML is used to evade filters, generate highly personalized phishing attacks and brute-force passwords.
The only way to minimize the risk of cyber attackers successfully breaching your network is to stay one step ahead of them – matching their capabilities is the bare minimum.